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Investment Thesis

▪ Autonomous trucking is set to disrupt the $5.5 trillion global freight trucking market

as it can solve some of the major challenges faced by the industry, especially fleet

operators. The global and U.S. trucking industry continues to be plagued by critical

issues including driver shortage, driver retention, parking, compliance, safety, and

accountability (CSA), and rising insurance costs. Autonomous trucks can solve these

problems to a large extent by reducing the number of drivers required to run fleet

operations, improving the retention rate of qualified and safe drivers, and solving the

parking and CSA problem, while driving a ~20% drop in operating costs and up to

12.5% reduction in insurance premium, thus incentivizing fleet operators to switch to

these vehicles. Reduced GHG emissions are an added sustainability benefit.

▪ Autonomous vehicles (AVs) will drive the migration of the automotive ecosystem

from the pyramid structure to a hub and spoke model and will lead the shift in value

from hardware to software, as the brain becomes more important than the body. As

AVs redefine the ecosystem, hardware will cede significant value to software, with the

hardware to software mix changing from the current 90:10 to 40:40 in an autonomous

vehicle, and apps (20%) emerging as new centers of value. Technology companies

working on a sense-think-act model and developing the brain behind the machine will

emerge as the dominant players in this redefined ecosystem. The list of such

companies is led by players like Plus and Embark, that have proven autonomous

trucking technology, best-in-class industry partners, and a clear commercialization

roadmap that puts them in a strong position to emerge as future trucking leaders.

▪ Sensing technology, led by LiDAR, is the other disruptive force shaping the industry.

Sensing technologies provide key inputs to the machine to make driving decisions and

play a crucial role in the development of AVs. LiDAR (Light Detection and Ranging) is

the most disruptive of these technologies, thanks to its ability to provide accurate

inputs even in challenging external conditions such as bad weather. Velodyne LiDAR,

Ibeo Automotive Systems, and Quanergy Systems are some of the key players in this

segment that is attracting investor attention. HD mapping, connectivity, stack

development, computing hardware, and deep learning are other parts of the

redefined ecosystem that will experience rapid growth in coming years.

▪ Trucks lead cars on the AV commercialization timescale, with full automation

expected to be a reality by 2027. Trucks are more conducive to automation than cars,

thanks to their high freight capacity and simpler operating conditions (running on

fenced highways). As a result, Class 8 trucks, which mostly operate on highways, is the

key target segment for developers of Level 4 autonomous trucks. Commercialization

will start with platooning in the initial phases and move to fully autonomous trucks by

2027; at a geographic level, deployment will start in Southern U.S. and gradually

expand to the North.

▪ Investor interest rising as commercialization appears in sight. Autonomous vehicle

startups raised a record $8.4 billion in venture capital last year, up 33% y/y. We believe

that the industry is now set to enter its next phase of funding and will start receiving

capital from public equity investors – as demonstrated by the recent IPO and

announced SPAC deals – which will result in value discovery and multiple expansion.

▪ Investors looking to benefit from the Autonomous Trucking opportunity should

evaluate technology leaders like Plus (SPAC Deal – NASDAQ: HCIC), Embark Trucks

(SPAC Deal – NYSE: NGAB), TuSimple (NASDAQ: TSP), Aurora (SPAC Deal – NASDAQ:

RTPY), Locomation, and Waymo (NASDAQ: GOOGL).

Autonomous Trucking

Redefining Freight Transportation

Tuesday, September 07, 2021

Autonomous Trucking – The

Future of Freight

Transportation

Market Size and Growth

Key Growth Drivers – Solving Major

Problems Within the Trucking Industry

Redefining the Automotive

Ecosystem

Dominance of Software in the Hub

and Spoke Model

Sensing Technology – Key to Success

OEMs – Ceding Value to Technology

Plays

Hardware Suppliers, Connectivity

Providers, Stack Developers,

Computing Hardware, and HD Maps

Commercialization Roadmap

Current State of U.S. Freight Market

Level 4 Automation – Targeting the

Class 8 Truck Segment

Phased Deployment – South to North.

Full Automation by 2027

Growing Investor Interest

Venture Capital Led Funding So Far

SPACs Leading the Increasing Allocation

of Public Funds Toward Autonomous

Trucking

Top Picks

Plus (SPAC Deal – NASDAQ: HCIC)

Embark (SPAC Deal – NYSE: NGAB)

TuSimple (NASDAQ: TSP)

Aurora (SPAC Deal – NASDAQ: RTPY)

Locomation (Private)

Waymo (NASDAQ: GOOGL)

CONTACT

Bill Jenks

Intro-act, Incorporated

617-244-4960

bill@intro-act.com

Please refer to disclaimers at the end of

this report for more information about

Intro-act, and this report.

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Autonomous Trucking – The Future of Freight Transportation

Providing A Safe, Efficient, and Sustainable Freight Transportation Solution

▪ Autonomous trucking is set to disrupt the $5.5 trillion global freight trucking market. The global freight trucking market is

expected to grow at a CAGR of ~4% from $4.2 trillion in 2020 to $5.5 trillion in 2027, per ResearchAndMarkets. The market

opportunity is given that every product or raw material which is bought and sold would have likely spent some time on a

truck. We believe that demand for trucking services will continue to rise due to a combination of factors including 1)

increasing manufacturing activity, 2) increasing consumer and retail spending, 3) growing international trade volume

between individual countries and trade blocks, 4) improving road infrastructure, 5) trucks being an efficient and convenient

mode of transportation (vs. railroad freight), 6) growing urbanization, and 6) technological breakthrough in the trucking

industry. The strong growth potential will be boosted by ever rising customer expectations, with a strong focus on 1) on-

time delivery, 2) cost efficiency and 3) increased safety. Given this context, we believe that autonomous trucks – which are

capable of meeting the increasingly high service standards demanded by customers while simultaneously being able to

meet demand growth – are ready to disrupt the global freight trucking market.

Chart 1: Global Freight Trucking Market Will Reach $5.5 Trillion by 2027

Source: Intro-act, ResearchAndMarkets

▪ U.S. the world’s largest freight trucking market, accounting for >25% of the global market, and given the regulatory

tailwinds and focus on innovation, it is likely to maintain its leadership position in the foreseeable future. U.S. – the

largest economy in the world – is also the largest market for freight trucking, which was estimated to be worth $1.1 trillion

in 2020, accounting for 26.2% of the global market, per ResearchAndMarkets. China, the world's second-largest economy

and the second-largest freight trucking market is, on the other hand, forecasted to reach $1.2 trillion by the year 2027,

growing at a 7.1% CAGR between 2020 and 2027. Other key markets are Japan and Canada, forecasted to grow at 1.2% and

3%, respectively, through 2027. Within Europe, Germany is forecast to grow at a ~2.1% CAGR.

▪ With 38 Federal departments, independent agencies, commission, and the Executive Office of the President

working together to ensure American leadership in autonomous vehicle technologies, the U.S. is likely to be

the first to commercially deploy autonomous vehicles (including trucks) on roads. Ensuring American Leadership

in Automated Vehicle Technologies: Automated Vehicles 4.0 (AV 4.0) – the U.S. government’s guiding principle

for accelerating the development of autonomous vehicles – focuses on 10 U.S. government principles consisting

4.2 4.4 4.5 4.7 4.9 5.1 5.3 5.5

2020 2021 2022 2023 2024 2025 2026 2027

Global Freight Trucking Market ($ trillion)

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of three core interests and simultaneously promotes collaborative efforts in the development of autonomous

vehicles.

▪ The U.S. government’s AV 4.0, non-binding guidelines, are aimed at providing basic ground rules to be followed

while developing autonomous vehicles. By doing so, the government ensures that there is a proper mechanism

for co-existence of the autonomous vehicles and conventional vehicles in the overall transportation system for a

considerable period. Additionally, to ensure that both the AVs and conventional vehicles can seamlessly and safely

use the national transportation infrastructure, the U.S. government continues to make a significant investment in

the development of complementary technologies.

▪ The AV 4.0 outlines the U.S. government’s enterprise-wide involvement in AV policy and development with the

objective of signaling that it wants to be on the cutting-edge of AV research, development, and manufacturing.

The guidelines emphasize the U.S. government’s commitment to fostering American leadership in AV

development and integration while ensuring safety and privacy.

Chart 2: Ensuring American Leadership in Automated Vehicle Technologies: Automated Vehicles 4.0 – 10 Principles

Protect Users and Communities

Promote Efficient Markets

Facilitate Coordinated Efforts

▪ Prioritize Safety

▪ Remain Technology Neutral

▪ Promote Consistent Standards and Policies

▪ Emphasize Security and Cybersecurity

▪ Protect American Innovation and Creativity

▪ Ensure a Consistent Federal Approach

▪ Ensure Privacy and Data Security

▪ Modernize Regulations

▪ Improve Transportation System-Level

Effects

▪ Enhance Mobility and Accessibility

Source: Intro-act, USDOT

▪ Regulations are leading the technological development and will boost the countrywide deployment of autonomous

vehicles in the U.S. While the trucking industry is well regulated within the U.S., laws for autonomous trucks are absent at

the federal level whereas various states have enacted their own legislation in the absence of a comprehensive federal policy.

We, however, believe that the regulatory framework enacted by the states is supportive and promotes the testing and

deployment of autonomous trucks (limited to some states). We also believe that the absence of federal-level regulation

with respect to the deployment of autonomous vehicles will be soon addressed and Level 4 automation targeted at highway

routes, which at times requires the vehicle to cross state borders, will get a significant boost. We discuss below some of the

government initiatives aimed at promoting the development of autonomous vehicles in the U.S.

▪ The absence of federal regulation for autonomous vehicles reflects the current state of consensus technical

standards and test procedures which are under development and the lack of published autonomous driving

standard (ADS) specific voluntary consensus standards and standardized test procedures. In January this year, the

U.S. Department of Transportation has amended the Federal Motor Vehicle Safety Standards (FMVSS) to clarify

that AVs not designed to carry humans are exempt from the crashworthiness standards that conventional vehicles

need run on the roads. This has come as a significant relief for developers of autonomous vehicles.

▪ Within the U.S., there are 43 states with regulations for testing of Level 4 autonomous trucks and 24 states out

of these have regulations for commercial operations of Level 4 autonomous trucks. Each state has different

permit requirements for licensing autonomous vehicles. To note some of the remarkable variances – Tennessee

does not require any operator in the vehicle during the pilot, whereas an operator is mandatory in Michigan, and

California has created a graduated licensing system to allow AV developers to move forwards with the driverless

operation.

▪ Texas is a major on-road testing location for several AV companies; in addition, Daimler has started tests of

autonomous trucking in Virginia.

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Chart 3: Regulatory Landscape of U.S. States for Autonomous Vehicles

Source: Intro-act, Embark

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Autonomous Trucks’ Adoption Will Be Driven by Their Ability to Address Key Challenges

▪ Autonomous trucks provide a solution to multiple issues faced by the trucking industry, thereby incentivizing developers

of autonomous trucking technology to expedite their development efforts. The trucking industry in the U.S. faces multiple

challenges, despite the critical role played by trucks in moving the majority of the goods. This hampers the ability of fleet

operators as well as owner-operators to operate profitably. Driver shortage, driver retention, and hours-of-service limit the

ability of fleet operators to meet customer demand, while other issues such as rising fuel cost, congestion, and driver

satisfaction (through imposed penalty) negatively impact their profit margins. Autonomous trucks are capable of addressing

most of these challenges faced by the trucking industry and are thus set to disrupt the $5.5 trillion global trucking market

in the coming years. Below, we discuss the top issues faced by the trucking industry and how autonomous trucks will help

tackle them.

Chart 4: Top Industry Issues Addressed by Autonomous Trucks

Source: Intro-act, Roland Berger, ATRI

▪ Autonomous trucks provide either a complete solution or partial relief from the top five issues plaguing the trucking

industry in the U.S. According to a survey published by the American Transportation Research Institute (ATRI) the top five

issues prevailing in the trucking industry include 1) driver shortage, 2) driver compensation, 3) truck parking, 4) compliance,

safety, accountability (CSA), and 5) insurance availability/cost. We discuss below how autonomous vehicles will help tackle

these industry concerns.

▪ Autonomous trucks promise to solve the biggest concern of the trucking industry – driver shortage – as Level 4

automation will eliminate the need for a driver on the highway, thus solving to a large extent the driver shortage problem

faced by the industry. Driver shortage has remained the biggest challenge facing the trucking industry for a long period and

this issue has been further aggravated as several drivers leave the industry due to FMCSA Drug and Alcohol Clearinghouse

and older drivers continue to retire. In addition to retirement of existing drivers, other factors resulting in driver shortages

include demographics, lack of female participation, challenging working conditions, low wages, and strict regulations.

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▪ An ATRI survey placed driver shortage at the top of trucking industry’s top concern, for the fourth consecutive year, in

2020. While 26.4% respondents considered this as one of the top three concerns, at least 13.9% freight stakeholders said

that this was the topmost concern for them. The trucking industry has been struggling with driver shortage issue since 2015;

however, this issue has been plaguing the industry much before that and was first documented by ATRI in 2005, and is likely

to further get aggravated through 2026.

Chart 5: Driver Shortage Has Remained One of The Top Industry Concerns for a Decade

Year

Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

2020

Driver Shortage

Driver Compensation

Truck Parking

CSA

Insurance

Cost/Availability

2019

Driver Shortage

Hours-of-Service

Driver Compensation

Detention (delay) at

Customer Facilities

Truck Parking

2018

Driver Shortage

Hours-of-Service

Driver Retention

ELD Mandate

Truck Parking

2017

Driver Shortage

ELD Mandate

Hours-of-Service

Truck Parking

Driver Retention

2016

ELD Mandate

Hours-Of-Service

Cumulative Impacts of

Regulation

Truck Parking

Economy

2015

Hours-of-Service

CSA

Driver Shortage

Driver Retention

Truck Parking

2014

Hours-of-Service

Driver Shortage

CSA

Driver Retention

ELD Mandate

2013

Hours-of-Service

CSA

Driver Shortage

Economy

ELD mandate

2012

CSA

Hours-of-Service

Economy

Driver Shortage

Fuel Prices

2011

Economy

Hours-of-Service

Driver Shortage

CSA

Fuel Issues

Source: Intro-act, ATRI Note: CSA – Compliance, Safety, Accountability; ELD – Electronic Logging Device

▪ Truck driver shortage is likely to swell to 160,000+ by 2028, per ATRI. The truck driver shortage has increased

from ~20,000 in 2005 to 60,800 drivers in 2018 and will continue to remain a major challenge for truck operators,

with retirement being the major factor. Over the next decade, replacing retiring truck drivers will account for

~54% of new driver hires.

▪ The Class 8 truck segment will be the worst hit by driver shortage in the U.S. Class 8 tractor-trailer is the segment

most severely impacted by this trend, and within this category, most of the shortage is within the over-the-road,

or non-local, for-hire truckload sector.

The American Transportation Research Institute (ATRI) predicts that the trucking industry will be required to hire ~1.1

million new million drivers over the next 10 years (an annual run rate of ~110,000 per year) to meet the truck driver

demand. We believe autonomous trucks or driverless trucks provide a long-term solution to the driver shortage problem

faced by the trucking industry, even though commercialization of full-autonomous trucks is still some years away.

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Chart 6: Truck Driver Shortage in the U.S. (2011-28)

Source: Intro-act, American Trucking Associations (ATA)

▪ High turnover/attrition rate (92%) add to the woes of fleet operators who are already suffering from driver shortage.

Truck driver turnover rates in the U.S. remain high with an annualized turnover rate for 2020 at 92% for truckload fleets

with more than $30 million in annual revenue. The churn for smaller truckload carriers stood at 72% in 2019 and decreased

to 69% in 2020. The economic recovery, in the post-pandemic period, and a robust freight market are likely to drive the

turnover rate higher as the demand for long-haul drivers increases further.

▪ High turnover rate reflects the challenging working conditions for drivers, who are willing to switch for a small

wage hike. A 92% turnover rate suggests that of every 1,000 drivers employed 920 drivers left their fleet for one

or more reasons during the year. Some of the key reasons for the high turnover rates are 1) disillusioned new

drivers, 2) low wages, and 3) deleterious health effects associated with long-haul driving. Most drivers who quit

one truckload job for another are either in need of more money, more miles, or more home time switching

operators with an expectation that their problem will likely get solved with the next fleet operator. However, as

time passes and a significant of these hoppers realize that their condition has remained unchanged, they start

looking for another alternative and, in the process, end up sending the turnover rates even higher.

▪ Autonomous trucks will help fleet operators retain qualified and safe drivers. While the deployment of autonomous trucks

will lead to reduced demand for drivers, they will still be required to take over the control of the vehicle in specific

conditions. As such, fleet operators will continue to need qualified and skilled drivers with a good safety track record. The

deployment of autonomous trucks with the latest technology designed to increase safety and reduce the fatigue and stress

associated with driving tasks will help fleet operators retain talented drivers with an impeccable safety record. Autonomous

trucks will not only reduce the monotony associated with long hours of driving but will also enable drivers to get back home

sooner. The opportunity to operate autonomous trucks will also be appealing to tech-savvy youngsters and provide them

with a better-than-before career path in driving, increasing their longevity with their existing employer. As a result, we

believe that autonomous trucks will help fleet operators in retaining talent to a large extent.

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▪ Autonomous trucks will also emerge as the solution to the $200 billion+ parking problem. Parking has always remained a

nuisance for truck drivers and per the 2020 ATRI survey it is the third-most critical challenge faced by the trucking industry.

This has remained one of the top 10 most critical issues within the industry since 2012. Truck parking becomes an even

more severe issue when on highways, since lack of information can result in overcrowded parking areas as drivers need to

strictly adhere to the strict running hours regulation and halt when they have to. An INRIX report aimed at studying parking

issues and driver behavior found that the combined cost (including the cost associated with wasted time, fuel, and carbon

emissions) across 30 cities in just three countries (the U.S., U.K., and Germany) exceeded $200 billion per year.

▪ Parking shortage forces truck drivers to resort to unsafe and unauthorized parking. With more than 11 truck

drivers for every parking space in the U.S., it is no surprise that ~98% of truck drivers report problems finding safe

parking and spend an average of 56 minutes of available drive time every day looking for parking. This lost time is

often unpaid as fleet operators pay drivers for the miles covered and thus the time wasted in searching for parking

costs a driver ~$5,500 per year – a meaningful sum that is equivalent to ~12% pay cut for the truck driver,

according to ATA. This forces truck drivers to resort to parking in unauthorized areas with ~58% of drivers

admitting to parking in unauthorized or undesignated spots at least three times per week to meet their parking

needs.

▪ Within the U.S., truck parking spaces have grown ~10% between 2014-19, however, they are still not sufficient

to solve the parking issues faced by truck drivers. According to Federal Highway Administration, there are

~313,000 truck parking spaces (40,000 public rest areas and 273,000 private truck stops) nationally within the U.S.

Whereas the public rest areas grew 6% between 2014-19, the private truck stops grew ~11% during the same

time frame. The slow-paced growth of truck parking facilities results from existing challenges in planning, funding,

and accommodating truck parking. As a result, at least 98% of truck drivers faced parking issues in 2019 with the

problem being most severe along key freight corridors and metropolitan areas.

Chart 7: 98% U.S. Truck Drivers Face Problems Finding Safe Parking Place

Source: Intro-act, BTS

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Chart 8: U.S. Truck Parking Facilities (2019)

Source: Intro-act, BTS

▪ The Truck Parking Safety Improvement Act, if enacted, will help solve the truck parking issues faced by highway

drivers to an extent. The Truck Parking Safety Improvement bill designates $755 million from the Highway Trust

Fund for states to finance projects aimed at increasing the number of parking spaces for commercial truck drivers.

This funding could be used for construction of new truck parking facilities, expansion of truck parking at existing

rest areas, conversion of space at existing weigh stations, or any other innovative solution that increases capacity.

If enacted, the act promises to solve the critical parking issue faced by truck drivers to a certain extent.

▪ At the same time, there are various players such as Cleverciti Systems GmbH and Urbiotica who are harnessing

technology to develop smart parking solutions. Smart parking solutions allow a driver to locate vacant parking

slots, meeting their requirements, at service stations along the highways (and also within city limits) through a

mobile app or an in-built feature and can help drivers better plan their stoppages and remain compliant to hours-

of-service regulation as well as take a break and relax in a peaceful environment. The futuristic parking system

developed on the foundation of a well-connected network of connected (autonomous) vehicles, parking lots and

a central server will also enable the drivers to book a parking slot well in advance, helping save time that would

have otherwise been spent in searching for vacant parking slots. Smart parking solutions use image processing

technology and sensors to identify vacant and occupied parking slots. Cleverciti Systems GmbH and Urbiotica are

some of the leading players developing smart parking solutions for trucks and other vehicles.

We believe that smart parking solutions will be an integrated feature of autonomous trucks, helping them save valuable

time and money which otherwise would have been spent searching for vacant parking slots.

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Chart 9: Illustrative Smart Parking Solutions for Trucks at Highway Service Stations

Source: Intro-act, Urbiotica

▪ Autonomous trucks will also help improve the Compliance, Safety, and Accountability (CSA) track record of drivers. The

Compliance, Safety and Accountability (CSA) program from the Federal Motor Carrier Safety Administration (FMCSA) is

designed to hold drivers and transport companies accountable for their part in promoting safety on the highways. Speeding,

failure to obey traffic signals, and failing to use a seatbelt are some of the most common violations for which drivers are

booked. These violations dent profit margins and also expose others on the road to significant accident risks. Since CSA

affects a driver’s safety record, it remains one of the most prominent challenges being faced by the industry. We believe

that introduction of autonomous trucks will reduce the Safety Measure System (SMS) score; however, the scoring

methodology will have to be modified so as to reflect the driverless mode of operation in autonomous trucks.

Chart 10: Top Five Violations Committed by Truck Drivers

Source: Intro-act, FleetOwner

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▪ Autonomous trucks will lead to 20%+ reduction in the total cost involved in operating a truck – we believe this margin

expansion will be a key incentive for fleet operators to switch to these vehicles. According to ATRI, the average marginal

cost of operation per mile for a traditional truck was $1.65 in 2019 whereas the average marginal cost of operation per mile

for an autonomous truck, in the long run, is estimated to be $1.26 in the transfer hub model, according to Medium. In the

transfer hub model, autonomous trucks without a driver carry the cargo from the transfer hubs to distribution centers. This

provides an opportunity for fleet operators and truck owners to add >20% to their operating margins. The cost savings

through the use of autonomous trucks will come in the form of lower driver cost, improved fuel efficiency, improved

utilization, and lower insurance premium, among others.

Chart 11: Average Marginal Costs per Mile (2019) of Operating a Manual Truck

Source: Intro-act, ATRI

▪ Autonomous trucks will help reduce driver costs by up to 77%. Trucking automation will deliver the largest cost

savings in the form of reduced driver cost which has grown exponentially over a decade and now accounts for

~42% of average marginal cost compared to 39% in 2010. It is estimated that higher driver costs are responsible

for a 15%+ increase in the average per mile cost of operation. According to industry experts, the driver cost per

mile for a 200 miles cumulative distance, in a transfer hub model, assuming that remote assistance professionals

(teleoperators) will be managing 30 trucks at a time, is likely to be $0.16. This offers a ~77% cost savings in drivers

costs compared to $0.69 driver cost per mile in the current set-up. This translates into an annual cost saving of

~$88,000+ in driver cost alone (assuming 166,375 miles travelled by an autonomous truck in a year and a $0.53

saving per mile in driver cost).

▪ Fuel savings to the tune of ~10%. Fuel savings will be realized by truck operators due to optimal driving speeds

being maintained by autonomous trucks, in addition to smoother acceleration and deceleration, and a longer

planning horizon. It is expected that autonomous driving will help reduce fuel cost by ~10% when the machine is

in control of driving. Assuming that truck runs on automatic mode for ~82% of the distance, cost savings over a

200-mile distance will be ~0.04 per mile. Autonomous trucks, with an average speed of 62 miles per hour and a

machine having a better understanding of the grades of the road for acceleration, will help realize fuel efficiency

and will also help minimize carbon footprint.

$0.40 , 24%

$0.26 , 16%

$0.14 , 9%

$0.07 ,

$0.02 , 1%

$0.04 , 2%

$0.03 , 2%

$0.53 , 32%

$0.16 , 10%

Fuel Costs

Lease or Purchase Payments Cost

Repair and Maintenance

Insurance Premiums

Permits and Licenses

Tires

Tolls

Driver Wages

Driver Benefits

Average Marginal Costs per Mile - $1.652

12 | P a g e

▪ Platooning – a key application of Level 4 automation – has the potential to deliver fuel savings of between $0.02

to $0.042 per mile, according to Peloton. When autonomous trucks travel in a platoon they travel within a close

distance and the frequency of stopping and slowing down decreases significantly. As a result, trucks in the platoon

can undertake their journey with lower peak speeds, which helps improve fuel economy.

▪ Another driver will be the fact that autonomous vehicles can lead to a ~12.5% drop in insurance premiums through 2035,

according to Accenture estimates. Rising insurance costs continue to remain one of the biggest challenges within the

trucking industry. According to an ATRI (2019) report on the analysis of operational cost of trucking, the insurance cost per

mile had increased 18.3% over a five-year period through 2019. These rising insurance costs have been cited amongst one

of the key reasons for the bankruptcy of multiple fleet owners. However, insurance premiums for vehicles are likely to

decline with the rising penetration of autonomous vehicles which are considered safer than those driven by humans, led by

a drop expected in the number of road accidents leading to falling claims. Also, the autonomous vehicles are likely to be

owned by OEMs, OTT players, and fleet operators who will be bulk buyers of insurance policies and will be in a better

bargaining situation, forcing insurance companies to reduce their premiums.

▪ According to Accenture, auto insurance premiums will start dropping as early as 2026 due to the launch of autonomous

vehicles and by 2035 the reduction could be as high as $25 billion or 12.5% of the total market. While insurance companies

are expected to bring new products to market to meet the needs of changing operating conditions which will add to their

revenue, Accenture estimates that by 2033 lost premium revenues will begin to outweigh the gains from new insurance

product lines. However, insurance companies will be able to set off some of their losses through the introduction of new

products centered around autonomous vehicles. The new products will provide insurance cover for 1) cyber security risks,

2) product liability insurance for sensors or algorithms, and 3) insurance against infrastructure problems. Of the three, cyber

security and product liability are likely to fetch premiums of $12 billion and $2.5 billion respectively, by 2025.

Chart 12: Impact of Autonomous Vehicles on Insurance Premium (Annual Gains vs. Losses)

Source: Intro-act, Accenture

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▪ Other advantages offered by autonomous vehicles that will make them attractive to fleet operators include:

▪ Reduced repairs and maintenance. Automation will reduce accidents and unplanned maintenance in the long

term; however, increased part utilization and added sensor parts requiring new forms of planned maintenance

will most likely offset any improvements gained from reductions in unplanned maintenance activities.

▪ Improved utilization. Fleet operators typically use a truck for a period of five years before transferring the

ownership to smaller and individual operators who then operate it for another five to six years. Over these five

years, fleet operators are able to run the trucks for ~91,506 hours, based upon an 11 hour per day (hours-of-

service rule allows truck drivers to work up to 14 hours a day, during which time they can drive up to 11 hours,

followed by at least 10 hours of off duty before coming on duty again) driving schedule. However, an autonomous

truck can run for ~20 hours per day leading to an 82% increase in utilization.

▪ Falling payback period will make owning an autonomous truck an attractive proposition from an ROI

perspective. As technological advances lead towards full automation, the cost of trucks is also likely to increase.

According to a Roland Berger report, the incremental cost of an autonomous truck with Level 4 automation is

likely to be ~$5,900 higher than a truck with Level 3 automation. It also estimates that the total incremental cost

of an autonomous truck is ~$23,400 more than a truck with no automation (Level 0) and the incremental cost is

likely to be driven by software cost which will account for the bulk of the incremental cost. However, with cost

savings coming through multiple channels on the deployment of an autonomous truck, the average payback

period will continue to fall at higher levels of automation. The Roland Berger report estimates that the payback

period of an autonomous truck with level 4 automation is expected to be 28 months whereas that for an

autonomous truck with Level 5 automation is likely to be just 4 months. This creates a significant value proposition

for fleet operators and other owner-operators to switch to autonomous trucks as they are rolled out for

commercial deployment.

Chart 13: Incremental Technology and Vehicle Cost from Level 1 to Level 5 Automation

Source: Intro-act, Roland Berger

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Chart 14: Payback Calculation for Long-Haul Autonomous Trucks

Source: Intro-act, Roland Berger. DATP = Driver-Assisted Truck Platoon.

▪ Autonomous trucks promise to enhance on-road safety and can potentially save ~5,000 lives per year. In addition to cost

savings and improved efficiency, autonomous trucks also offer an attractive proposition in terms of improved on-road

safety. Autonomous trucks minimize the role of a driver thereby drastically reducing the possibility of driver error and have

the potential of saving the trucking industry billions of dollars. According to National Highway Traffic Safety Administration

(NHTSA), ~150,000 people were injured in crashes involving large trucks in 2018 which claimed 4,951 lives. While the

number of people killed in accidents involving large trucks has been increasing since 2009, it is largely driven by the

increasing vehicle miles travelled (VMT) despite significant improvements in safety technologies.

▪ Driver errors are the leading cause of accidents involving large trucks. According to NHTSA, ~94% of serious

crashes are due to dangerous choices or errors people make behind the wheel reflecting the role of driver errors

in claiming lives on roads. When the accidents involve a Class 8 truck, the situations become even worse since the

sheer size and weight of these vehicles means that the consequences of accidents are particularly severe, with

high fatality rates. Unsafe driving actions including speeding, reckless driving, improper lane change, and

inattention will get eliminated as long as the machine is in control. Autonomous trucks, which are expected to be

safer, will reduce the number of accidents that result due to human error.

▪ Fleet operators also incur significant financial losses due to accidents involving their fleet vehicles. Fleet

operators suffer a significant loss when their trucks get involved in an accident as they have to spend for vehicle

repairs apart from incurring losses due to loss of consignment, loss of driving hours, and depreciation in vehicles

value. The associated costs with accidents have the capacity to erode operators profit margins. However,

autonomous trucks with enhanced safety features will reduce these losses for fleet operators.

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Chart 15: Fatalities in Crashes Involving Large Trucks in the U.S.

Source: Intro-act, NHTSA

▪ In addition to their economic and safety benefits, autonomous trucks are also ESG friendly as they provide an

environment-friendly solution while meeting the rising demand for freight transportation. Trucks are responsible for

~25% of the U.S. transportation greenhouse gas (GHG) emissions and autonomous trucks will lead to reduced fuel

consumption, running at optimal speed (due to cruise control features), and reduce road congestion due to better vehicle-

to-vehicle communication. All these factors will collectively contribute toward reduced GHG emission. Platooning and less

time spent on searching for parking will also lead to environmental benefits.

▪ Truck platooning is known to help reduce fuel consumption and Teleroute estimates that the lead truck in a

platoon is capable of saving up to 4.5% of fuel costs, a number which can increase to up to 10% for each following

truck in the platoon. This will have a significant impact on reducing the carbon footprint of the platoon of

autonomous trucks.

▪ As discussed earlier, parking is one of the major concerns for the trucking industry with drivers spending an

average of 56 minutes of available drive time every day looking for parking. During this process, a lot of fuel is

burnt. Autonomous trucks, while solving the parking problem, will also lead to fuel savings, which in turn will lead

to lower GHG emissions.

▪ It is also expected that as safety levels of autonomous trucks improve over a period of time, they will likely shed

some of their bulky weight, which at present is a part of the standard fixture to provide a margin of safety. Lighter

vehicles will also help reduce fuel consumption, ultimately leading to lower GHG emissions.

▪ As autonomous trucks transition to completely electric models, the consumption of fossil fuel will be reduced to

zero and this will be a gamechanger for the trucking industry, which will lead to a sharp drop in GHG emissions

from the autonomous trucks.

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Chart 16: Multiple Factors Will Contribute to Reducing GHG Emission with Deployment of Autonomous Trucks

Source: Intro-act, MDPI, International Journal of Environmental Research and Public Health

35%

19%

35%

2% Reduction of GHG Emission

Platooning

Less Hunting for Parking

Eco-Driving

Eco-Traffic Signal

Collision Avoidance

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Autonomous Vehicles – Redefining the Automotive Ecosystem

▪ Autonomous vehicles will bring a radical change in the value chain of the automotive industry and will drive the migration

from the pyramid structure to a hub and spoke model. Under the traditional automotive ecosystem – best described as

the pyramid structure – OEMs sit at the top and below it are several tiers of suppliers ranging from raw materials and

component suppliers at the base (third tier) up to systems suppliers and fully integrated production partners. The OEMs –

by virtue of controlling the customer relationship – are at the apex. However, with the rising adoption of autonomous

vehicles, this value chain structure will undergo a radical shift and is more likely to resemble a hub and spoke structure.

While the finished product will remain at the center it will be surrounded by indispensable and in some cases interconnected

parts of a wheel which will include tiered suppliers, the OEMs, IT suppliers, online players, telecom companies, and device

manufacturers. The success of the hub and spoke ecosystem will depend upon the harmonious functioning of each of the

spoke. We believe that under the hub and spoke model, technology developers such as Embark and Plus will emerge as

the centerpiece of the ecosystem, given their importance and head start in innovation and development of autonomous

trucking technology. We discuss the various parts of the hub and spoke ecosystem in detail below.

Chart 17: Autonomous Vehicles Will Disrupt the Automotive Industry Value Chain

Source: Intro-act, AT Kearney

Technology/Software Developers Are Emerging as the Dominant Part of the Value Chain

▪ Hardware and software players will play a complementary role in the development, testing, and deployment of

autonomous vehicles. It is widely believed that the autonomous vehicle is a software story; however, hardware also has an

important role to play in the development of an autonomous vehicle. Software is an important aspect of the autonomous

vehicle given that it is a first-of-its-kind initiative, whereas hardware development is critical given its fixed nature and that

fact that it is unlikely to be changed during the product’s lifespan. The hardware in an autonomous vehicle plays a critical

role in environment sensing, vehicle-to-vehicle (V2V) communication, and executing actions such as braking, acceleration,

etc. On the other hand, software is used to analyze the inputs received from environment sensors and plan the next course

of action and then send the commands to various accentuating devices.

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Chart 18: Autonomous Vehicle System = Hardware + Software

Source: Intro-act, MDPI

▪ Software developers are responsible for developing the technology – the brain behind the machine – which will help in

running an autonomous truck. The software for autonomous vehicles is unchartered territory and demands significant

research and development (R&D) and industry participants are responding accordingly by allocating greater resources to

software development. Increased resource allocation is being done across the complete spectrum of vehicles including the

development of sophisticated telematics to complex collision avoidance systems to the navigation system. In software

development, technology-focused companies are taking a lead.

▪ Waymo, Aurora, Ike, Embark, Kodiak, Plus, TuSimple and Peloton are some of the most prominent names

focusing on developing the technology (software) for autonomous trucking. The technology developed by these

players allows the truck to understand the environment to make driving decisions. These companies have been

able to attract significant talent and capital for developing the best technology to bring autonomous trucks on the

road.

▪ Hardware development is also keeping pace with software development as traditional automotive players are taking a

lead in development of hardware. In an autonomous vehicle, hardware covers critical aspects such as the ability of the

vehicle to handle heavier loads over longer periods, the ability to withstand the temperature variance, road debris and high

levels of moisture faced by vehicles in daily service. However, intense development and testing have led hardware

companies to bring next generation parts to the market which will be able to support the complex and demanding needs of

the software being developed to run autonomous vehicles.

▪ Hardware developers are also focusing on cutting the weight of the vehicles by introducing the use of thin-walled

aluminum tubing and honeycomb recycled carbon-composite panels – which allows the weight of frames to be

reduced up to 50% and improves the fuel efficiency of vehicles. However, hardware, unlike software, does not

enjoy the luxury of frequent updates and are likely to last the lifetime of a vehicle. This makes it necessary to get

the hardware piece of the puzzle right the very first time.

▪ Investment opportunities in the autonomous vehicle space are currently focused on the autonomous trucking technology

developers who are focusing on Level 4 automation. Autonomous vehicle technology providers are striving to bring full-

scale Level 4 autonomy to commercial trucking. In Level 4 automation, autonomous trucks take full control of the vehicle

including safety-critical functions – subject to meeting certain specified conditions and drivers stepping in in exceptional

cases such as adverse weather conditions. Technology developers are envisioning a model where self-driving trucks focus

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on long, lonely highway miles while leaving the first-mile pickup and last-mile delivery to the human driver. The autonomous

trucking technology provides a superior ability to sense, predict, and react to real-world driving situations better on

highways when compared to city driving.

▪ Level 4 automation is highly dependent upon the suite of sensors including LiDAR (light detection and ranging),

radar, cameras, ultrasonic and infrared sensors. In a Level 4 autonomous truck, the camera, LiDAR, and other

sensing components of autonomous trucks collect street view mapping data, landscape images, and other LiDAR

information to further strengthen the architecture by using the raw data collected for machine learning.

▪ The other key elements of Level 4 automation include prediction model and planning capabilities. Trucks, by

virtue of their length and weight, need longer stopping distance and duration; therefore, autonomous trucks

require a longer planning horizon and better predictive capabilities. Further, the accuracy of perception and

motion planning continue to be augmented through machine learning.

▪ Some of the key players developing software for autonomous trucks focused on Level 4 automation include

Plus (NASDAQ: HCIC), Embark Trucks (NYSE: NGAB), TuSimple (NASDAQ: TSP), Aurora (NASDAQ: RTPY),

Locomation, Waymo (NASDAQ: GOOGL), Ike Robotics, Inceptio Technology, Einride.

▪ See below for the competitive landscape of Autonomous Vehicle Technology Developers. We also discuss the

key names for investors to focus on at the end of the report.

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Chart 19: Competitive Landscape – Autonomous Vehicle Technology Developers

Embark

Plus

Locomation

TuSimple

Aurora

Waymo

Year Founded

2016

2015

2017

2009

Ownership Status

SPAC Deal With

Northern Genesis

Acquisition Corp. II

(NYSE: NGAB)

SPAC Deal With

Hennessy Capital

Investment Corp. V

(NASDAQ: HCIC)

Private

Public

SPAC Deal With

Reinvent Technology

Partners Y

(NASDAQ: RTPY)

Subsidiary of Alphabet

(GOOGL)

Valuation

$5.16 billion

$3.3 billion

$10 billion*

$11 billion

$30 billion+

Founders

– Alex Rodrigues

– David Liu

– Hao Zheng

– Shawn Kerrigan

– Çetin Meriçli

– Mo Chen

– Xiaodi Hou

– Chris Urmson

– Sterling Anderson

– Drew Bagnell

– Dmitri Dolgov

Service Offering

– Embark Driver

– Autonomous vehicle

(AV) software-as-a-

service

– PlusDrive-as-a-

Service

– PlusDrive-enabled

Trucks

– PlusDrive Systems

– Autonomous Relay

Network

– Autonomous Freight

Network (AFN)

– Aurora Driver

– Waymo Driver

– Waymo One

– Waymo Via

Target Geographies

– U.S.

– Europe

– China

– U.S.

– Europe

– China

– U.S.

– Canada

– Europe

– Asia

– U.S.

– Europe

– China

Commercialization/

Product Roadmap

– 2024: Phase 1 roll

out in sunbelt states

– 2026: Rollout for the

remainder of the

lower 48

– 2021: PlusDrive (SL4)

– Highway Only

– 2022-23: PlusDrive

Navigate (SL4) –

Ramp-to-Ramp

Highway

– 2024: PlusDrive Full

Autonomy (L4)

– 2022: Commercialize

Autonomous Relay

Convoys (ARC)

System

– 2023: Launch Drone

Follower System

– 2024+: Launch hub-

to-hub system and

then dock-to-dock

system

– 2024: Commercial

production with

Navistar

– 2025: Commercial

production with

Traton

– 2022-23: Validate

Aurora Driver

(Trucks)

– 2023-24: Launch

Aurora Driver

(Trucks)

– 2024+: Expand

Aurora Driver

(Trucks)

–

Capital Raised

(Pre-SPAC Deal)

$117 million

$520 million

$63 million

~$2 billion

$320 million

$5 billion+

Select Investors

(Pre-SPAC Deal)

– CPP Investments

– Knight-Swift

Transportation

– Mubadala Capital

– Sequoia Capital

– Tiger Global

– Sequoia Capital

– Hedosophia

– GSR Ventures

– SAIC

– Guotai Junan

International

– CITICPE

– ScaleX Ventures

– Nvidia

– UPS

– The Traton Group

– VectoIQ LLC

– Sequoia Capital

– Baillie Gifford

– Amazon

– Alphabet

– Andreessen

Horowitz

– AutoNation

– Canada Pension Plan

Investment Board

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– FountainVest

Partners

– ClearVue Partners

OEM Partners

– OEM Agnostic

– FAW

– IVECO

– Navistar

– PACCAR

– Volvo

– Toyota

– Fiat Chrysler

Automobiles

– Stellantis

– Jaguar Land Rover

– Renault Nissan

– Mitsubishi

– Volvo

– Daimler

Non-OEM Partners

– Werner Enterprises

– Mesilla Valley

Transportation

– Bison Transport

– Anheuser-Busch

InBev

– HP Inc.

– Cummins

– Amazon

– SF Express

– Zhihong Logistics

– BOSCH

– Wilson Logistics

– UPS

– McLane

– U.S. Xpress

– Werner

– Schneider

– Union Pacific

Canadian National

– Uber

– Lyft

Source: Intro-act, Company Reports. *TuSimple’s Valuation is based on Market Cap as of 8/26/21.

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Led by LiDAR, Sensing Technology Is The Other Disruptive Force Shaping the Industry

▪ The technological evolution of autonomous vehicles is based on the characteristics and functional requirements of driving

and is classified into four key parts 1) navigation system, 2) path planning, 3) environment perception, and 4) vehicle

control. This is also referred to as the sense-plan-act design.

▪ Navigation system and path planning: Autonomous vehicles are equipped with on board navigation systems to

locate their position and perform the path planning to the destination automatically and intelligently. In an

autonomous vehicle’s navigation system, geographic information system (GIS) and global positioning systems

(GPS) are equipped to receive the location information such as longitude and latitude from the satellite. They are

then used with the road information generated by the location system and digital map database for intelligent

path planning.

▪ Environment perception: As an autonomous vehicle charts its course, it needs to have an accurate understanding

of its environment to be able to take control decisions and uses a combination of laser navigation, visual

navigation, and radar navigation to get an accurate perception of its environment. Multiple sensors including laser

sensors and radar sensors are deployed in an autonomous vehicle to perceive the environment and we believe

that development in sensors and processing solutions holds the key to disruption in autonomous trucking and

that cameras, laser sensors (LiDAR) and RADAR hold the key to the success of the autonomous vehicle.

▪ Laser sensor (LiDAR): The laser sensor is the main sensor in environment perception and works on the

principle of reflection. It is used to determine the physical characteristics of an obstacle including its

location, shape and state. LiDAR (Light Detection and Ranging) is used by a large number of autonomous

vehicles to navigate environments in real-time. Velodyne LiDAR, Inc. (NASDAQ: VLDR), Ibeo Automotive

Systems GmbH, and Quanergy Systems, Inc. are some of the key players in this segment.

▪ RADAR: RADAR systems are used for distance detection and use the return time of a millimeter wave

transmitted by a RADAR sensor to calculate the distance of an object or obstacle. Bosch, Continental AG

(XETRA: CON), Hella (XETRA: HLE), DENSO (TYO: 6902), ZF (erstwhile TRW), Delphi Technologies,

Autoliv (NYSE: ALV), and Valeo (Euronext Paris: FR) are some of the leading global suppliers of

millimeter-wave radar.

▪ Vehicle control: The control system of an autonomous vehicle is a core component that is tasked with controlling

the speed and direction of a vehicle based upon the inputs received from the vehicles navigation system, path

planning, and environmental perception. The control platform includes an electronic control unit (ECU) and a

communication bus. The ECU mainly implements the control algorithm, whereas the communication bus realizes

the communication function between the ECU and mechanical parts.

▪ The development of autonomous vehicles is dependent on sensors which are critical for perception and localization of

the vehicles for path planning and decision making. Sensors are devices that measure or detect the property of the

environment or changes to the property and are critical for simplifying the perception of a self-driving vehicle. They are

used to imitate the human ability to perceive and formulate a reliable picture of the environment – the underlying concept

or backbone of autonomous vehicle technology. Hence, sensing capabilities of an autonomous vehicle is among the most

essential elements in the overall autonomous driving system. Some of the most common sensors used in autonomous

vehicles include a camera, LiDAR (Light Detection and Ranging), RADAR, Inertial Measurement Unit (IMU), Global Navigation

Satellite System (GNSS), and SONAR. The use of sensors is also the key differentiator amongst the various autonomous

vehicles. We discuss below the three external sensors (cameras, LiDAR, and RADAR) which are most widely used in an

autonomous vehicle, including autonomous trucks.

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Chart 20: Cameras, LiDARs, and RADAR are Three Primary Sensors Used for Environment Perception in AVs

Source: Intro-act, MDPI

Chart 21: Location of Key Sensors – Cameras, RADAR, and LiDAR – on an Autonomous Truck

Source: Intro-act, ATRI

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Chart 22: Comparison of Cameras, LiDARs, and RADAR vs. Human Eye

Source: Intro-act, MDPI

▪ Cameras. Cameras are one of the most commonly used external sensors in developing an autonomous vehicle due to their

inexpensive nature and their ability to detect both moving and stationary obstacles within their field of view. They are best

suited to identify road signs, traffic lights, road lane markings and barriers, specifically in daylight. Despite a wide range

and low cost, cameras are of limited use in low lighting and bad weather conditions with their usability dropping significantly

under heavy fog, rain, and snow. Cameras used in an autonomous vehicle are classified into mono- and stereo-cameras,

with a stereo camera, also known as bipolar camera, containing two image sensors that are widely used in autonomous

vehicles. Some of the most prominent players manufacturing stereo camera are Roboception, Carnegie Robotics,

Ensenso, Framos, Nerian, Intel RealSense NASDAQ: INTC), and Teledyne FLIR (NYSE: TDY).

Chart 23: Competitive Landscape of Stereo Cameras Used in Autonomous Vehicle

Source: Intro-act, MDPI

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▪ Technological advancements in complementary metal oxide semiconductor (CMOS) chips – widely used in

cameras – will further improve the utility of cameras and enable them to capture images in low-lighting and

bad-weather conditions. Charge-coupled device (CCD) and CMOS and are the two image sensors used in cameras.

While the CCD image sensors are technologically superior to CMOS due to their high light sensitivity enabling the

camera to capture a better high-quality image even in low-light conditions, CMOS chips are more widely used in

autonomous vehicles due to their low cost, low power consumption, and faster data readout. Addressing this

major drawback of CMOS, leading CMOS manufacturers, which include Sony (TYO: 6758), Samsung (KRX: 005930),

ON Semiconductor (NASDAQ: ON), and OmniVision (NASDAQ: OTVI) are working toward developing a CMOS

image sensor that will help capture high-resolution images when compared to CCD image sensors. We believe

technological advancement in CMOS chips will boost the development of autonomous vehicles, given the critical

role played by cameras in environmental perception.

▪ Radio Detection and Ranging (RADAR) – another key sensor used in autonomous vehicles – uses the Doppler property of

electromagnetic (EM) waves to determine the relative speed and position of detected obstacles. It offers a significant

advantage over traditional cameras as it is impervious to adverse weather conditions and is independent of environmental

illumination. As such, RADARs are commonly deployed in autonomous vehicles to provide a reliable and precise perception

of obstacles and additional information such as speed of the detected moving obstacles. RADAR sensors are commonly

integrated invisibly in several locations including roof near the top of the windshield, behind the vehicle bumpers, or brand

emblems. However, in a truck, both long- and short-range RADARs are typically installed on the front bumper area. While

the long-range RADARs are used to focus further down the road (up to 800+ feet), short RADARs are for shorter range and

wider field. However, one of the major limitations of a RADAR is its inability to distinguish between a static and stationary

object, making it unsuitable for object recognition applications. As such, use of RADAR has to be complemented by a

combination of cameras or LiDARs. Aptiv Delphi (NYSE: APTV), Continental AG (XETRA: CON), and SmartMicro are some

of the most notable developers and manufacturers of RADAR sensors.

Chart 24: Comparison of Three RADAR Systems Used in Autonomous Vehicles

Source: Intro-act, MDPI

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▪ Light Detection and Ranging (LiDAR) is a remote sensing technology that operates on the principle of emitting pulses of

infrared beams or laser light which reflect off target objects. It is one of the other critical external sensors used in an

autonomous vehicle. LiDAR is like a RADAR but uses lasers instead of radio waves and the laser reflections are detected by

the instrument while the time interval between emission and receiving of the light pulse enables the estimation of distance.

LiDAR was first developed in the 1960s and has been widely used in the mapping of aeronautical and aerospace terrain. The

rapid development of this technology has led to its high demand in the autonomous vehicle space. Its measurement range,

accuracy, robustness to surrounding changes, and high scanning speed (or refresh rate) make it well suited for use in

autonomous vehicles for environment perception. While LiDARs perform better than cameras in adverse weather

conditions, they are also sensitive to fog and precipitation which degrade the performance of the sensor by ~25%.

▪ A >98% fall in LiDAR prices is helping boost the development of autonomous trucks. LiDARs – one of the most

critical components of an autonomous vehicle – have seen a significant drop in their cost, thanks to technological

development. According to Financial Times, in 2014, LiDARs cost as much as $75,000 per piece but now LiDAR

focused companies such as Velodyne (NASDAQ: VLDR), Aeva (NYSE: AEVA), and Luminar Technologies (NASDAQ:

LAZR) are partnering with carmakers to build units at scale for less than $1,000 each. Further, according to Alix

Partners, the cost of a stack consisting of cameras, LiDAR, and RADAR could be as low as $7,000, by 2025. This will

go a long way in bringing autonomous trucks on road at a reasonable price.

▪ Solid-state LiDARs are becoming increasingly attractive for AV developers. LiDARs are classified into mechanical

LiDAR and solid-state LiDAR (SSLs) with mechanical LiDARs being most widely used at present. Since mechanical

LiDARs have rotary lenses, they are exposed to the risk of mechanical failure. SSLs, on the other hand, use a

multiplicity of micro-structured waveguides to direct the laser beams to perceive the surroundings and eliminate

the use of rotary lenses, thereby avoiding the possibility of a mechanical failure. Apart from lowering the risk of

mechanical failure, SSLs are also more robust, reliable, and cheaper compared to mechanical LiDARs, making them

a preferred choice for developers of autonomous vehicles.

Chart 25: Some of the Key Mechanical and Solid State LiDARs Being Tested and Deployed in Autonomous Vehicles

Source: Intro-act, MDPI

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▪ Enhanced safety features in an automated driving system (ADS) are essentially an outcome of the fusion of different

sensors, processing systems, and actuators. Driving involves assessing the external environment, processing the

information, and then triggering the actuating system (such as steering, brakes, signaling, etc.) by a human driver.

Autonomous driving technology is based on the same premise and concepts with the only difference being that the sensing,

processing, deciding, and acting is carried out by automated systems which are capable of processing multiple data points

with relatively lower latency.

▪ ADS involves the use of multiple sensing devices to develop a sufficiently clear picture of the environment and

processing the information to take safe driving decisions. The use of multiple sensing devices such as Radar and

LiDAR along with cameras is very well-suited for performing driving tasks in terms of reaction times, consistency,

and multichannel information processing. It is, however, worth noting that certain common traffic scenarios still

confound automated driving system capabilities, but with machine learning we expect these hurdles to be

overcome in near future.

Chart 26: Assessment of Sensor Performance Across Driving Tasks

Source: Intro-act, OECD

▪ Images and data captured by the external image sensors (including cameras, RADARs, and LiDARs) are processed using

complex algorithms to enable the machine to make the driving decision – thanks to the deep learning techniques

employed in autonomous vehicles. Self-driving vehicles are a classic use of deep learning – a branch of machine learning

comprising of techniques such as neural networks, hierarchical probabilistic methods, supervised and unsupervised learning

models, and deep reinforcement learning (DRL) which automatically extracts features and patterns from raw data and

makes predictions or takes actions based on some reward functions.

▪ Deep learning is contributing significantly toward advances and development in object detection, a subdomain

of computer vision. Autonomous vehicles use deep learning for path planning and obstacle avoidance, in addition

to trying to process camera-based information to solve complex computer vision problems. The key deep learning

architectures for object detection and computer vision in autonomous vehicles are 1) convolutional neural

networks (CNN), 2) recurrent neural networks, 3) deep belief networks, and 4) stacked autoencoders. They have

been applied extensively for object detection and scene perception in self-driving vehicles using the images

captured using cameras, RADAR, and LiDAR. The object detection using deep learning is mainly concerned with

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key tasks including 1) self-localization, 2) understanding driving environment and reconstructing the environment,

3) pixel labeling, differentiating between individual objects, 3D detection and tracking, 4) predict trajectories

where autonomous vehicles have never been before and generalize unseen driving conditions, and 5) semantic

scene understanding, and understanding high-level semantics and scene understanding in traffic patterns.

Chart 27: Deep Learning Architectures for Scene Perception and Object Detection in Autonomous Vehicles

Source: Intro-act, Elsevier

▪ Embark (NYSE: NGAB), one of the leading autonomous trucking players, uses deep learning for object detection. Embark

(NYSE: NGAB), a pureplay autonomous trucking company, is building self-driving technology to make highways safer and

the transport of goods more efficient. Embark (NYSE: NGAB) combines deep learning with sensor fusion and surround

vision using the NVIDIA DRIVE platform to enable the functioning of automated and self-driving vehicles. Its deep learning

team focuses on developing systems that sense and perceive the world around its self-driving truck.

Chart 28: Embark’s Self-Driving Software – High Level Architecture Relies on Deep Learning

Source: Intro-act, Embark

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OEMs Will Cede Value to Technology Players and Rely on Partnerships to Drive Growth

▪ OEMs will play a key role in the development of autonomous trucks though they are likely to cede value to their

counterparts who are developing AT technology. OEMs worldwide will have a critical role to play in boosting the adoption

of autonomous trucks. While incumbent OEMs have been successful in automating the trucks through Level 1, Level 2, and

Level 3 automation levels, including development and deployment of advanced driver-assistance systems (ADAS), they are

likely to focus on systems and integrative functions in the Level 4 and Level 5 autonomous truck ecosystem. Also, OEMs will

be exposed to significant challenges under the new market structure as they will face stiff competition from other players

in the ecosystem, most of them being multi-billion-dollar companies with strong research and development teams.

▪ Incumbent OEMs are likely to maintain their dominance in the hardware segment given their process expertise

and manufacturing set-up. OEMs will continue to be responsible for providing the hardware required for

autonomous trucks even as other leading technology-focused firms and pure plays aim to develop the technology.

These technology firms and pure-play companies are unlikely to enter the hardware space, which is capital

intensive and is expected to lose value share in an autonomous vehicle.

▪ The hub and spoke ecosystem of the industry will mean that the traditionally dominant truck OEMs will lose

value to other players. Under the traditional industry set-up, the value for an average automobile is 90%

hardware and 10% software; however, in autonomous vehicles, the share of hardware is likely to shrink to 40%

with software (40%) and apps (20%) contributing much more to the value of an autonomous vehicle. Moreover,

OEMs are expected to lose a critical advantage – bargaining power over their suppliers – which they presently

enjoy. With each technology supplier now holding the key to successful development of autonomous vehicles,

OEMs will not have the same leverage with the suppliers. As a result, OEMs might turn to manufacturing white

labelled trucks and components (unbranded products that are branded and sold by other players). Google

(NASDAQ: GOOGL) and Uber (NYSE: UBER) may be typical software-centric companies developing their own

software and technology, and thereafter partnering with OEMs to develop and manufacture the necessary

product.

▪ OEMs are, however, likely to find a significant business opportunity in the form of commercializing the data

collected by their vehicles. With their autonomous trucks on road, OEMs will be able to track the vehicle on a

real-time basis, thereby collecting significant data which can be further used to draw insights and provide inputs

for future developments. However, to leverage this opportunity, OEMS will have to make the necessary

investments in beefing up their data security to ensure that data collected by them remains secure and is not

misused. OEMs will also continue to play a crucial role in providing financing facilities to fleet operators and

individual customers looking to purchase autonomous trucks.

▪ Overall, we believe that OEMs are likely to focus on their core competency of manufacturing vehicles rather

than developing technology. As a result, we expect OEMs to direct their R&D efforts toward increasing process

efficiency and enter into strategic alliances with players from other parts of the ecosystem to get the first-

mover advantage. Some of the most prominent OEMs contributing toward making autonomous trucks a reality

include Navistar, Tesla (NASDAQ: TSLA), Ford (NYSE: F), Volvo (STO: VOLV-B), Paccar (NASDAQ: PCAR), Daimler

(XETRA: DAI), IVECO, and MAN SE (XETRA: MAN).

▪ OEMs are entering into strategic partnerships with various stakeholders including technology companies and pure plays

focused on the development of autonomous trucks. In the hub and spoke architecture, the key to OEMs survival lies in

forging the right partnership within the ecosystem. While large OEMs will likely use a combination of partnerships and

inorganic moves to build their capability in this space, smaller OEMs are likely to rely majorly on partnerships to stay relevant

in the autonomous trucking market.

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Chart 29: Autonomous Trucking Partnerships and Initiatives of Leading Truck OEMs

Navistar

China FAW Group

Corporation (FAW)

Volvo

Daimler

PACCAR

Year Founded

1986

1953

1927

1998

1905

Revenue (FY20)

$7.5 billion

$107 billion

$40 billion

€154.3 billion

$18.7 billion

Key Strategic

Partnerships /

Initiatives

– Navistar and TuSimple

have partnered to bring

autonomous trucks to

market

– FAW is collaborating

with Plus to develop

autonomous trucks for

China

– FAW and Baidu are

jointly developing

Hongqi E Cocept,

China’s first Level-4

autonomous passenger

vehicle

– Partnered with NVIDIA

to develop the decision-

making system of

autonomous

commercial vehicles and

machines

– Partnered with Aurora

to deploy autonomous

trucks in North America

– Strategic partnership

with Waymo to develop

autonomous trucks

– Chosen AWS as its cloud

partner to develop

autonomous trucks

– Mercedes-Benz and

NVIDIA have partnered

to develop in-vehicle

computing system and

AI computing

infrastructure

– PACCAR and Aurora have

formed strategic

partnership to develop

autonomous trucks and

commercialize autonomous

Peterbilt and Kenworth

trucks

– Teamed up with Nvidia to

develop self-driving truck

technology

Capital Investment /

Capital Raise

– Navistar has invested in

TuSimple

– FAW announced a

strategic investment in

Pony.ai, an autonomous

technology company, in

2020

– Volvo invested in Palo

Alto-based Apex.AI, a

startup working on

developing a robotic

operating system

qualified for use in

production automobiles

– Announced investment

of $573 million to

develop autonomous

trucks in 2019

– Acquires minority stake

in Luminar – LiDAR

startup

– Owns Torc Robotics, a

pioneer in autonomous

driving solutions

– Invested in Aurora

Key Milestone /

Achievement

– TuSimple and Navistar

have received 6,775

reservations for fully

autonomous

international LT series

trucks

– Penske, Schneider, and

U.S. Xpress first few

customers

– Launched a robotaxi

service with Baidu in

2019 rolled out for

commercial operation in

2021

– FAW to start mass

production of J7L3, its

autonomous truck, in

2022

– Volvo and FedEx began

testing connected truck

platooning technology

in 2021

– Has begun driverless

truck testing on public

roads in Virginia and the

Southwest

– Displayed innovative

autonomous and electric

trucks at CES 2020

Source: Intro-act, Company Reports

31 | P a g e

Role of Hardware Suppliers Will Evolve and Require Integration Across the Value Chain

▪ Hardware suppliers will see a tectonic shift in their business model with the development of autonomous trucks – albeit

for their own good. With developers of autonomous vehicle technology at the heart of the autonomous vehicle ecosystem

and the role of OEMs being curtailed to being hardware partners, the role of hardware suppliers is also up for

transformation. In the new market architecture, hardware suppliers will have a choice to move up the ladder and assume

the role played by existing OEMs or they could shift their focus towards being a software supplier and components

supporting the software. Irrespective of the path chosen to survive and retain their relevance in the ecosystem, they will be

required to move out of their comfort zone and chose a business model which is fundamentally different from what they

are doing today. However, depending upon the alternative chosen by these hardware suppliers, specifically the tier-1

suppliers, they will have to alter their existing business model and make adjustments according to their new focus area.

Even though hardware suppliers will have significant growth opportunities, they will also face increasing competition with

the possibility of OEMs moving downstream to become component suppliers.

▪ Continental AG (XETRA: CON), Velodyne Lidar (NASDAQ: VLDR), Wabco, BOSCH, ZF, Bendix and Valeo (Euronext

Paris: FR) are some of the dominant companies that are looking to own a pie of the global autonomous trucking

market. (See competitive landscape and key initiatives of leading suppliers on next page).

▪ Automotive suppliers have a critical role to play in the future of autonomous vehicles. As the industry moves toward

complete automation, it will pass through a phase where assistive technologies will play a greater role. Auto suppliers are

presently focused on mastering these technologies as they progress to build capabilities for building autonomous vehicles.

As such, component suppliers will be focused primarily on advanced driver assistance systems (ADAS) and autonomous

driving (AD) technologies. The AD system integrates the camera and radar data into the ADAS system with inputs from

additional sensors and sources – for example, light radar (LiDAR), high-definition maps (HD Maps) and crowdsourced sparse

data maps.

▪ Top tier suppliers will have to provide automakers with cost-effective ADAS solutions that integrate with

automaker’s larger systems. They will also be required to invest in pilot programs and technologies focused on

autonomous technologies.

▪ Bain Research estimates the market size for assistive and autonomous technologies which includes software,

hardware, and services sold by suppliers to automakers is expected to reach ~$22-26 billion by 2026.

▪ Component suppliers looking to establish their leadership in ADAS and AD are likely to face a significant challenge

from technology companies. Apple (NASDAQ: AAPL), Google (NASDAQ: GOOGL), Tesla (NASDAQ: TSLA), Uber

(NYSE: UBER), Intel (NASDAQ: INTC), and Nvidia (NASDAQ: NVDA) are some of the key technology players capable

of disrupting the market.

Overall, we believe that component suppliers will have to develop key capabilities and competencies in software

engineering, specifically data fusion and machine learning. One of the key challenges these component suppliers are likely

to face will be around attracting talent on their route to technological excellence.

32 | P a g e

Chart 30: Leading Auto Component/Hardware Suppliers and Their Autonomous Vehicle Initiatives

Continental AG

Velodyne LiDAR

Bosch

Valeo

Year Founded

1871

1983

1886

1915

1923

Revenue (FY20)

€37.7 billion

$95.4 million

€71.5 billion

€32.6 billion

€16.4 billion

Key Strategic

Partnerships /

Initiatives

– Partnered with NVIDIA to

develop develop self-driving

solutions based on the NVIDIA

DRIVE platform

– Jointly developing high-

performance long-range LiDAR

with AEye

– Partnered with Amazon to

accelerate communication

from and into car

– Partnered with Horizon

Robotics to build intelligent

driving joint venture

– Partnered with Idriverplus, to

help Idriverplus, for mass

production of commercial

autonomous vehicles

– Joined NVIDIA’s Metropolis

program for Intelligent

Infrastructure Solutions

– Partnered with Beijing Trunk

Technology Co., Ltd.

(Trunk.Tech)to develop LiDAR

enabled products

– Collaborated with Ford Otosan

on product development and

testing of autonomous heavy

commercial trucks

– Partnered with NVIDIA to

develop new AI self-driving car

computer

– Partnered with Daimler to

develop software and

algorithms for an autonomous

driving system

– Partnered with Global

Foundries to develop RADAR

chips for self-driving vehicles

– Partnered with Volkswagen to

harness real-time information

for high resolution maps

– Partnered with Faurecia to

develop interior and safety

technologies for autonomous

driving

– Partnered with NVIDIA and

HELLA to deliver artificial

intelligence (AI) technology

with the New Car Assessment

Program (NCAP) safety

certification for the mass

deployment of self-driving

vehicles

– Partnered with Mobileye to

develop a new autonomous

vehicle safety standard.

– Collaborated with Navya in the

field of autonomous shuttles.

Valeo provides Navya the

sensors and associated

algorithms that enable the

vehicle to closely perceive its

surroundings.

– Joined Baidu’s autonomous

driving partner ecosystem,

Apollo.

– Collaborated with BMW to

develop autonomous parking

system

M&A (AV

Focussed)

– In Aug 21, acquired stake in

Kopernikus, a developer of

automated parking system

– In Feb 21, acquired minority

stake in German-US AI chip

start-up Recogni

– In Oct 20, acquired minority

stake in AEye, developer of

LiDAR

– In 2018, acquired 5% stake in

HD mapping company HERE

Technologies

– In Jul 19, acquired Mapper.ai

for ADAS Launch

– Invested in Trunk, a Chinese

full-stack supplier for truck

manufacturers through Robert

Bosch Venture Capital (RBVC),

the venture capital arm of

Bosch

– Acquired WABCO and mobility

provider 2getthere

– Acquired peiker, Spheros,

gestigon, FTE Automotive and

Ichikoh between 2015 and

2017 which helped it

strengthen its offering for

autonomous vehicles

Capital

Investment /

Capital Raise

– Planning to increase

investment into developing

autonomous mobility systems

from €200 million to €250

million in 2021

– In 2020, raised $150 million in

a reverse merger deal with

GRAF, a special purpose

acquisition company

– Invested $1.1 billion in setting

up self-driving and smart chip

plant which is now operational

– In 2018, committed to invest

$14 billion in electric and

autonomous vehicle

technology over the next five

years

– In 2019, announced its plan to

invest €44 million in

autonomous vehicle R&D

Key Milestone /

Achievement

– Already built a demo vehicle,

CUbE (Continental Urban

mobility Experience), to enable

driverless mobility, especially

in cities.

– Received orders for high-

performance computers for

vehicle cockpit, data

– Launched Velodyne Lidar’s

Intelligent Infrastructure

Solution (IIS).

– In Dec 20, signed a multi-year

sales agreement for Alpha

Prime™ sensors with Motional,

a global driverless technology

leader.

– Deployed self-developed

campus shuttle at Bosch

campus

– ZF subsidiary, 2getthere,

provides the autonomous

shuttle buses used in RABus

project

– Developed Valeo Drive4U

autonomous car, a prototype

vehicle.

33 | P a g e

management and vehicle

connectivity, driving safety and

dynamics, or automated

driving worth ~€5 billion

– To create a separate business

unit for autonomous mobility

in 2022

– In Dec 20, announced a sales

agreement with May Mobility,

a pioneer in AV technology.

– In Oct 20, announced a three-

year sales agreement with

Baidu

Products /

Solutions (AV

Focussed)

– Cameras, LiDAR, RADAR, AD

High-Performance Computer,

Safety and Motion HPC,

Ultrasonic Parking Sensor

– Puck, Ultra Puck, Alpha Prime

– Simultaneous localization and

mapping (SLAM)

– Bosch HMI (human-machine

interface)

– Automated valet parking

– Automated driving

– Robotics

– Advanced driver assistance

system (ADAS)

– Cameras

– Intelligent driver assists

– Autonomous emergency

braking system

– Vehicle motion control

technologies

– Autonomous Driving Open

Platform Technology (ADOPT)

– LiDAR

– Optical sensor cleaning system

– Cameras

– IMU sensors

– Ultrasonic sensors

Source: Intro-act, Company Reports

34 | P a g e

Connected Nature of Autonomous Trucks Will Create Opportunities for Cybersecurity Plays

▪ Cybersecurity poses one of the biggest threats to the adoption of autonomous trucks as and when they hit the roads at

a commercial scale. As autonomous vehicles look to disrupt the automotive industry there is going to be a complete

migration of critical risks associated with these connected and autonomous vehicles (CAV) – from the risk of physical

violation to cybersecurity attacks. Given this context, it is not surprising that cybersecurity is a nightmare for developers of

autonomous vehicles with each CAV processing ~4,000 GB of data every day, per Intel. This is likely to continue to increase

as the complexity of the CAVs increases and technology advances.

▪ According to a BAE report, today’s modern car has ~150 million lines of code which is likely to increase to 600 million by

2025, thereby significantly increasing the vast attack surface. To put it in perspective, Microsoft Vista with ~40 million lines

of codes had 905 known vulnerabilities listed in the National Vulnerable Database (NVD). The 150 million lines of codes in

modern day’s CAV make it more complex than a Boeing (6.5 million), Facebook (62 million) and even a Large Hadron Collider

(50 million). Cybersecurity attacks expose an autonomous vehicle to multiple risks including 1) transferring remote access

of a vehicle to a hacker, 2) disabling of the vehicle, 3) remotely unlocking and stealing a vehicle, 4) creating a safety condition,

5) tracking or monitoring the vehicle, and 6) using the vehicle as a weapon. It is worth noting that autonomous trucks are

the most susceptible vehicle segment for cyber-attacks and can cause more harm because they carry cargo that can cause

serious harm. They are also susceptible to GPS spoofing attacks which can spoof the GPS satellite and send new navigation

signals.

Chart 31: Each Autonomous Vehicle Will Generate Truckloads of Data Every Day

Source: Intro-act, Intel

Chart 32: Connected and Autonomous Vehicles are Susceptible to Cyber Attacks

Source: Intro-act, Elsevier

35 | P a g e

▪ GuardKnox, Upstream, Karamba Security, and Argus Cyber Security are some of the key plays working toward creating a

robust cybersecurity framework that will drive confidence among CAV users. Despite the fact that a robust cybersecurity

setup holds the key to the success of mass-scale adoption of autonomous vehicles, the current automotive cybersecurity

technologies leave a significant gap. A study commissioned by SAE International and Synopsis found that 84% of the nearly

16,000 IT professionals surveyed in the automotive industry were concerned that cybersecurity practices were not keeping

up with the evolving technologies. The automotive industry also suffers from a serious drawback as off-the-shelf solutions

cannot be applied to commercial autonomous vehicles due to the data processing limitations of embarked processors and

their specific operational environment. The emerging cybersecurity threats and rising concerns about the associated risks

have brought together leaders from across the commercial and passenger vehicle segments to collaborate and develop

solutions to ward off the emerging cybersecurity threats.

▪ Upstream’s solutions secure drivers, vehicles, and mobility services from misuse and cyber-attacks. Upstream

Security helps corporations mitigate connectivity risks and ensure the safety and security of smart mobility

solutions protecting connected and autonomous vehicles. Upstream C4 is a data-driven cloud-based platform that

integrates with automotive data streams of vehicles and promises to detect incidents as they happen in real-time

and allocating a rating based on perceived impact and severity.

▪ GuardKnox creates a coding architecture for autonomous cars that operates everything from the general vehicle

systems (including sensors) to tools that enhance a car’s user experience (infotainment systems, center consoles,

etc.).

Software Stack Developers and Computing Hardware – The Brain of the Truck

▪ NVIDIA (NASDAQ: NVDA) and Apollo are the leading software stack developers for autonomous trucks. Software stack

developers lay the foundation on which OEMs and other stakeholders develop autonomous vehicles. Autonomous vehicle

software stacks are platforms that can handle the tasks of perception, decision, and control and are developed by specialists

to control multiple domains of autonomous vehicle functionality. Autoware, OpenVDAP, NVIDIA, and Apollo are examples

of stack developers for autonomous vehicles. Additionally, Luminar Technologies (NASDAQ: LAZR) has developed

Sentinel – the industry’s first full-stack autonomous solution for series production vehicles. NVIDIA DRIVE OS, an open-

source software, is a foundational software stack from NVIDIA (NASDAQ: NVDA), which consists of an embedded RTOS,

hypervisor, NVIDIA CUDA libraries, NVIDIA Tensor RT, etc. Embark and Volvo are prominent technology developers using

the NVIDIA software stack.

Chart 33: Autoware’s Complete Open-Source Software Stack for Autonomous Vehicles

Source: Intro-act, Autoware

36 | P a g e

▪ NVIDIA DRIVE AGX, MobileEye EyeQ5, Texas Instruments TDA3x, Zynq UltraScale+ MPSoC ZCU104 and Google TPU v3 are

some of the most prominent computing hardware for autonomous vehicles. Computing hardware are graphic processing

units (GPUs) and specialist architectures to support real-time data processing from various sensors. The development of

multi-core central processing units (CPUs) and general-purpose GPU computing enable algorithms to run in parallel with

minimum latency, which is a critical requirement for the self-driving brain which an autonomous vehicle needs, apart from

serial processing capability, and parallel compute modules. These are important for image and LiDAR processing,

segmentation, object detection, and mapping. There are multiple computing hardware based upon Graphic Processor Unit

(GPU), Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGA), and Application-Specific Integrated Circuits

(ASICs) designed for computing in autonomous vehicles.

Chart 34: Key Computing Hardware for Autonomous Vehicles

Source: Intro-act, arXiv

▪ NVIDIA (NASDAQ: LAZR) and Intel (NASDAQ: INTC) are the major players in the computing hardware market.

▪ NVIDIA’s DRIVE AGX is the world’s most powerful System-on-Chip (SoC) available to developers of software or

technology for an autonomous vehicle. NVIDIA launched the DRIVE AGX platform, in 2018, to power the next-

generation robotics and autonomous vehicles. This new platform was ~10x more powerful than its erstwhile

NVIDIA Drive PX2 platform and is capable of processing 320 trillion operations per second (TOPS). This is an open,

scalable software and hardware solution enabling companies to seamlessly develop and test customized

autonomous driving technology apart from streamlining production and is capable of receiving over-the-air

updates.

▪ The platform’s DRIVE Software makes the perception capabilities of the platform very sophisticated enabling it to

zero in on and classifying a broad range of obstacles. The DriveNet deep neural network enables the car to detect

and classify objects in the surrounding environment and track them from one frame to the next. LaneNet and

OpenRoadNet, on the other hand, enable the vehicle to identify lane markings and detect drivable space. The

software also comes with a data recording tool, which allows manufacturers to collect time-stamped data from

various sensors for training, testing, and validation purposes. We believe NVIDIA will remain a leader in powering

autonomous vehicle technology as they move ahead on the road to deployment.

37 | P a g e

Chart 35: NVIDIA DRIVE Hyperion Developer Kit

Source: Intro-act, NVIDIA

▪ Intel’s MobileEye offers EyeQ5 – an industry-leading – ASIC-based solution to support fully autonomous (Level

5) vehicles. Mobileye is Intel’s Israel-based autonomous driving unit and its EyeQ5 is designed based on 7nm-

FinFET semiconductor technology providing 24TOPS computation capability with a 10 watts’ power budget.

Chart 36: Intel and Mobileye Autonomous Driving Solutions

Source: Intro-act, Intel

38 | P a g e

Connectivity, Mapping, & Big Data Plays Complete the Autonomous Trucking Ecosystem

▪ Connectivity is another key part of the industry ecosystem as it allows suppliers to provide cost-effective and reliable

ways to collect, share, and analyze data efficiently in the autonomous trucking market. Connectivity has a critical role to

play in making autonomous trucks, which will be most widely used in platoons or convoys and are capable of delivering

significant cost efficiency to fleet operators. However, these autonomous trucks, apart from communicating with the back-

office systems, need to communicate efficiently and accurately with other trucks in the formation. In order to do so, several

different types of devices, sensors, controllers and applications need to speak to each other and share data seamlessly and

on a real-time basis. Due to this requirement the connectivity module acts as the brain of the autonomous truck. It receives

and transmits data in real-time and is the interface for all connectivity-related services. All this data can be used to make

trucks safer and improve customer service. Connectivity also offers other advantages to fleet operators including 1) allowing

fleet operators to manage their fleet, 2) reduce downtimes, and 3) automatically notify the fleet operator and the workshop

for carrying out repair or maintenance. Samsara, Telogis (now Verizon Connect), Omnitracs, Trimble (NASDAQ: TRMB),

and KeepTruckin are some of the most prominent players offering connectivity solutions to the autonomous trucking

industry.

▪ HD maps extend the line of sight of an autonomous vehicle beyond the next corner – that is how an autonomous vehicle

navigates its path to its destination and avoids a collision. High definition (HD) maps, purposefully built for robotic systems,

are a necessity for the next level of autonomous driving to meet the need for high quality and more detailed maps. This

level of detailing can be provided only by HD maps which have centimeter-level accuracy. These HD maps enable the vehicle

to see beyond the line of sight of a human driver providing an accurate representation of the road ahead and information

on the surrounding environment. Given the critical role played by HD maps in the successful adoption of autonomous

vehicles, it is estimated that the HD maps market for autonomous vehicles will reach $16.9 billion in 2030 growing at a

CAGR of 31.7% from $1.4 billion in 2021, per MarketsandMarkets.

▪ HD maps can represent lanes, geometry, traffic signs, road surface, and the location of objects like trees in the

form of layers. These maps generally have at least one of the layers containing 3D geometric information of the

world in high detail to enable precise calculations and need to be updated, from the data collected from a variety

of sensors and cameras installed on autonomous vehicles, continuously to help improve the contextual awareness

of autonomous vehicles.

▪ An HD map is typically organized into five layers including 1) base map, 2) geometric map, 3) semantic map, 4)

map priors, and 5) real-time knowledge. It is worth noting that the real-time knowledge layer – the top-most

layer in an HD map – is dynamically updated and contains real-time traffic information which can also be shared

with other autonomous vehicles in the fleet. This feature is of significant advantage to fleet operators who are

likely to run the autonomous trucks in platoons or convoys.

▪ HD maps also help autonomous trucks in path planning. Path planning is a core and critical feature of autonomous

truck technology. HD maps, which continue to receive real-time updates, are very helpful in finding the optimal

route to the destination.

▪ TomTom (Euronext Amsterdam: TOM2), HERE Technologies, Waymo (NASDAQ: GOOGL), NVIDIA (NASDAQ:

NVDA), Baidu (HKG: 9888), Dynamic Map Platform, NavInfo (SHE: 002405), Zenrin (TYO: 9474), and Civil Maps

(US) are some of the most prominent players working towards creating robust HD maps.

▪ However, significant progress is still needed on the mapping front as the lack of a robust database for HD map remains a

major challenge for the autonomous vehicle industry. HD maps are useful only when they continue to get updated in real-

time by using the data captured by various sensors, cameras, and LiDAR which helps autonomous vehicles to make better

decisions while performing driving tasks. While there are major players around the world capturing this geospatial data, it

39 | P a g e

is often in commercial or proprietary formats, and automobile companies and OEMs may lack the resources and

technologies to collect it in its native format without quality loss. This limits their ability to provide an accurate map for their

autonomous vehicle. Resource-rich players, as a result, are developing their in-house HD maps, with Waymo and Uber being

the most prominent names in this category. These companies are using their vehicles that are equipped with various sensors

to map the data across cities. An in-house developed HD map allows these companies to retain control over the

maintenance strategy, addition of new features and information, and maintain accuracy. These are all negotiable

requirements in developing an autonomous vehicle.

▪ Lack of standardization is also a major challenge in HD mapping. With several players developing their own

proprietary HD map, standardization is emerging as a major challenge in their optimal use. The absence of a single

authoritative source or database of HD mapping data in the era of rapidly increasing mapping information creates

the problem of standardization. As such, the lack of a single automotive-grade navigation base is one of the crucial

obstacles to the full commercial readiness and safety of self-driving cars. In the absence of standardization, map

integrity and reliability remain a key concern forcing government bodies, companies, and associations to work on

the standardization of HD maps.

▪ Autonomous trucks will consume data in the same as they consume fuel thus, forming an indispensable part of the

autonomous vehicle. It is impossible to visualize the existence of an autonomous vehicle in the absence of breakthrough

development in big data capabilities. It is big data that makes almost all the critical components of an autonomous vehicle

including sensors useful. The absence of big data can render an autonomous vehicle useless since it will not know what to

do with the data it receives. To put it in perspective, an autonomous car gets ~4,000 GB of data per day which needs to be

further processed. In an autonomous vehicle, information from various built-in sensors is processed and analyzed in

milliseconds by the power of big data allowing vehicles to undertake a safe journey between two points.

▪ SAP (XETRA: SAP), Microsoft Azure (NASDAQ: MSFT), Alibaba Cloud (HKG: 9988), Cloudera (NYSE: CLDR),

Qualcomm (NASDAQ: QCOM), IBM (NYSE: IBM), and AWS (NASDAQ: AMZN) are some of the key players in this

market.

Chart 37: Big Data Use Cases – Autonomous Vehicles

By Autonomous Vehicles

Outside the Autonomous Vehicles

▪ See and sense – receive information; plan and act

based on gathered data

▪ Remote monitoring of health of autonomous vehicle by

manufacturer and sending an alert to customer

▪ Map surroundings in great detail

▪ Efficiently manage fleet of autonomous trucks

▪ Identify range, speed, and distance using cameras and

LiDAR

▪ City planning and engineering

▪ Communicate with other vehicles and share

information

▪ Plan preventive maintenance schedules

Source: Intro-act, Intellias

40 | P a g e

Commercialization Roadmap

Autonomous Trucks Will Redefine the Role of Trucks in the U.S. Freight Market

▪ Trucks will continue to remain the dominant mode of freight transportation in the U.S. through 2045. The U.S. freight

transportation system moved ~18.6 billion tons of goods in 2018, with trucks carrying nearly 61% of the total shipments,

per data from the Bureau of Transportation Statistic’s (BTS) Transportation Statistics Annual Report 2020. The total value

of goods shipped through trucks totaled ~$11.5 trillion and also accounted for ~61% of the value of all goods moved in the

U.S. during the year. BTS further estimates that by 2045, total freight volume in the U.S. will increase to 25.5 billion tons, of

which 14.8 billion tons or ~58% of the total will be moved by trucks. According to the latest edition of American Trucking

Associations’ annual data compendium – ATA American Trucking Trends 2020 – the trucking industry generated $791.7

billion in revenue in 2019, moving 11.8 billion tons of freight. The revenue generated by the trucking segment accounted

for 80.4% of the nation’s freight bill and moved ~68% of surface freight between the U.S. and Canada and ~83.1% of the

cross-border trade with Mexico. As such, truck freight continues to remain the dominant and preferred mode for freight

movement within the U.S. as well as for moving goods between the U.S., Canada, and Mexico. Overall, we believe that

trucking will continue to remain the preferred mode for moving goods on the surface and will continue to attract various

stakeholders including investors and innovators.

Chart 38: Truck Will Remain the Dominant Mode for Freight Movement Through 2045

Source: Intro-act, BTS

▪ Major long-haul freight corridors will get more crowded through 2045 posing significant challenges for truck drivers and

fleet operators; however, autonomous trucks will emerge as a solution by improving efficiency and better-planned

movement of trucks. Freight movement within the U.S. is highly concentrated on select corridors and between specific

industrial hubs. The high concentration of freight trucks on these routes increases congestion and throws multiple

challenges including delayed deliveries, increased possibility of accidents, and mental and physical fatigue among others. It

is worth noting that 79% of all freight goods moved within the U.S. happens only across the top 10% corridors and the most

valuable of these corridors are concentrated in the country’s 100 largest metropolitan areas including diverse economies

such as New York and Los Angeles.

▪ The U.S. has ~88,000 trade corridors, however, 10% of corridors account for movement of 79% of total freight

moved within the U.S. and one percent (~888 trade corridors) moving ~38% of all freight moved. As such, we

believe the top 10% of the trade corridors are likely to be the first targets for autonomous trucks to hit the

11.0 11.3 14.8

1.6 1.6

1.9

1.0 1.0

1.2

3.4 4.7

7.5

2012 2018 2045

Billions of Tons

Truck Rail Water Other

65% 61% 58%

10% 8% 8%

6% 5% 5%

20% 25% 30%

20%

40%

60%

80%

100%

2012 2018 2045

Truck Rail Water Other

17.0

18.6

25.5

41 | P a g e

highways. Also, long-haul truck density is likely to increase through 2045 and we believe that autonomous trucks’

better planning and vehicle-to-vehicle communication will facilitate the smooth movement of freight trucks.

Chart 39: Average Daily Long Haul Freight Truck Traffic on the NHS, 2012

Chart 40: Forecasted Average Daily Long Haul Freight Truck Traffic on the NHS, 2045

Source: Intro-act, FHWA Office of Freight Management and Operations, Freight Analysis Framework (FAF)

42 | P a g e

▪ The U.S. truckload market is highly fragmented; however, rising adoption of autonomous trucks will drive consolidation

and lead to a dominance of technologically advanced and deep-pocketed players in the trucking industry. According to

the U.S. Department of Transportation, as of August 26, 2020, the number of for-hire carriers on file with the Federal Motor

Carrier Safety Administration totaled 944,799 whereas private carriers totaled 713,491. Of these, ~91% owned fewer than

six trucks whereas ~98% of operators owned fewer than 20 trucks with the primary reason for a fragmented truck market

being the low entry barrier. However, the adoption of autonomous trucks will change this industry dynamic as owner-

operators as well as small- and mid-sized fleet operators may not have the capital required to invest in autonomous trucks.

They are also not likely to have the technology required to support the 24/7 operations of the trucks with minimal human

involvement. We believe that as autonomous trucks hit the highways, the competitive landscape of the industry will change

due to consolidation in the industry with deep-pocketed players controlling the bigger pie as opposed to the relatively

smaller market share enjoyed by them as of now.

▪ As of now, pure-play companies such as Plus (NASDAQ: HCIC), Embark (NYSE: NGAB), TuSimple (NASDAQ: TSP),

Locomation, Aurora (NASDAQ: RTPY), and Waymo (NASDAQ: GOOGL), among others have taken a lead in self-

driving technology while OEMs are trailing based on technology development. Pure-play operators are banking

upon their first-mover advantage, which will give their fleets a low-cost advantage. As a result, only fleets with

established relationships and technology will have access to autonomous trucks where owner operator or small-

to-medium size fleet operators will be able to purchase them from the mass market once OEMs launch their

models. We believe a consolidated market will encourage fleet owners to increase capital expenditure, further

leading to enhanced profit margins and improved efficiency in the medium to long term.

▪ Commercialization is in sight as leading technology players are set to roll out their solutions in a phased manner.

Plus, one of the leading players, completed a driverless Level 4 truck demonstration on a highway this August

representing a significant milestone not only for the company but the whole industry. It is now targeting mass

production of the FAW J7 L3 truck powered by PlusDrive is expected to start in 3Q21 followed by launching pilot

operations of a fully driverless truck for use in a dedicated environment in 2022. Embark, another leader in

autonomous trucking technology, is targeting driver out in 2023 and full commercial deployment in 2024. Other

players also have phased roll-out planned starting 2022 (refer to table below).

Chart 41: Leading Players – On Path to Commercialization

Embark

Plus

Locomation

TuSimple

Aurora

Product

Commercialization

/Deployment

Roadmap

– 2024: Phase 1

roll out in

sunbelt states

– 2026: Rollout for

the remainder of

the lower 48

– 2021: PlusDrive

(SL4) – Highway

Only

– 2022-23:

PlusDrive

Navigate (SL4) –

Ramp-to-Ramp

Highway

– 2024: PlusDrive

Full Autonomy

(L4)

– 2022:

Commercialize

Autonomous

Relay Convoys

(ARC) System

– 2023: Launch

Drone Follower

System

– 2024+: Launch

hub-to-hub

system and then

dock-to-dock

system

– 2024:

Commercial

production with

Navistar

– 2025:

Commercial

production with

Traton

– 2022-23: Validate

Aurora Driver

(Trucks)

– 2023-24: Launch

Aurora Driver

(Trucks)

– 2024+: Expand

Aurora Driver

(Trucks)

Source: Intro-act, Company Reports

43 | P a g e

Technology Developers are Targeting the Class 8 Truck Segment for Level 4 Automation

▪ The U.S. Federal Highway Administration (FHWA) classifies trucks into eight different categories and Class 8 trucks are

best suited for automation, given the multiple benefits they offer for developing the technology. The FHWA classifies

trucks into eight different classifications based upon the Gross Vehicle Weight Rating (GVWR). The GVWR is decided by the

truck manufacturer considering the combined weight of the strongest weight-bearing components, such as the axles; and

the weaker components, such as the body, frame, suspension, and tires and indicates the maximum truck weight plus what

it’s able to carry when fully loaded (the fully loaded weight of a truck includes the truck’s own weight plus the fuel, cargo,

passengers, and even the trailer tongue and sets a safety standard used to prevent overloading of trucks). Once set by the

truck manufacturer, the classification of the truck cannot be changed for its entire life. The eight different classifications are

simply named Class 1 through Class 8 with trucks weighing up to 6,000 pounds classified as Class 1 and those weighing more

than 33,001 pounds as Class 8 trucks. Class 1 through 3 typically covers non-commercial vehicles such as minivans, cargo

vans, SUVs, and pickup trucks whereas Class 4 and above are commercial vehicles. Class 8 trucks, which mostly operate on

highways, are best suited for automation – as a result, this is the key target segment for developers of autonomous trucks.

(We discuss the suitability of this segment later in the report.) The classification of trucks is useful information and is used

to decide the following:

▪ Duration for which a truck can operate continuously – For vehicles that weigh 10,001+ pounds, drivers need to

follow FMCSA’s Interstate Truck Driver’s Hours of Service.

▪ Type of permit required: Vehicles with GVWR of >10,001 pounds are subject to federal and state safety

regulations for the safe operation of commercial motor vehicles. In case of hauling oversized pieces of equipment,

identifying marks (like signs) and relevant permits are required.

▪ Need to stop at weighing stations – Trucks over a certain weight are required to stop at weighing stations.

▪ Maintenance tools – The engineers and technicians at workshops use the truck classification to assess the

requirement of tools, space, and other things needed for repair and maintenance.

▪ Driver’s eligibility – Knowing the truck classification helps decide whether a driver will be skilled enough to drive

the truck since the licensing regulations vary depending upon the class of trucks. A driver does not need a CDL to

operate vehicles in Class 1 through Class 6.

Chart 42: Classification of Trucks by FHWA

Source: Intro-act, AFDC

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Chart 43: Classification of Trucks by FHWA

Source: Intro-act, World Economic Forum

▪ New registration of Class 8 trucks – the most lucrative segment for Level 4 Automation – is likely to surpass the 200,000

mark by 2023. The registration of commercial trucks in the U.S. softened in 2020 due to the COVID-19 pandemic with total

registration for Class 3 to Class 8 trucks falling to 655.4k compared to 731.6k in 2019, per IHS Markit. IHS Markit, which

defines “commercial truck” as a truck owned by a commercial operation or government (federal/state/local) and includes

trucks belonging to Class 3 through Class 8, expects new commercial truck registration to remain under pressure in 2021 as

well with the registration of Class 8 trucks dropping below the 200k mark, reflecting lower orders received by truck

manufacturers in 2020. The softer demand for Class 8 trucks is due to the COVID-19 pandemic’s impact on the overall

economy. However, with a recovery expected in new registration in 2022 and beyond, the class 8 registrations will once

again creep above the 200,000 mark in 2023.

▪ Class 8 trucks have a very high freight capacity and simpler operating conditions, making them the most

suitable, amongst different classes of the truck, for automation. Class 8 trucks not only have a high freight

capacity but also spend a considerable portion of their journey on highways which is easier to navigate and cover.

As such, the combination of high freight capacity and simpler operating conditions makes the Class 8 segment

most suitable for automation.

▪ Point-to-point model use of Class 8 trucks makes its operations simpler. Class 8 trucks are primarily used for

transporting goods from one warehouse to other – thus operating with a point-to-point model. The nature of

their origination and destination requires them to spend most of their time on the highways as they move

between two transfer hubs. The unique operating model between two operational hubs limits variables and

minimize the challenges of deploying self-driving vehicles in congested urban environments or complicated

distribution center lots. Autonomous trucks in this point-to-point model will be driven by humans from their origin

45 | P a g e

point to a dedicated space near an interstate on-ramp before they shift to an autonomous mode on the highway.

Closer to their destination, the truck would navigate to a transfer facility near an offramp, where a human driver

would take back the control to steer the truck to its final destination.

Chart 44: Point-to-Point Movement is Best Suited for Autonomous Trucks

Source: Intro-act, Deloitte

▪ Fleet operators with deep pockets are more likely to buy autonomous trucks as they are the typical buyers of

new Class 8 trucks. The typical cost of buying a new traditional Class 8 truck ranges between $150,000 to $200,000

and hence requires deep pockets. This huge capital investment makes it difficult for smaller owner-operators to

buy new Class 8 trucks; and this initial investment will likely increase with autonomous trucks. As such, fleet

operators will end up being the dominant customers of Class 8 trucks. These fleet operators are likely to invest in

capital-intensive autonomous trucks which will help them realize cost efficiency, improve delivery timelines, and

enhance the safety of operations.

▪ According to IHS Markit, Class 8 trucks will continue to dominate the commercial truck market and its share (based

upon new registration) will increase from 31% in 2020 to 33% in 2024. It is, therefore, no surprise that major

players including Daimler (XETRA: DAI), Volvo (STO: VOLV-B), Navistar, PACCAR (NASDAQ: PCAR) are focusing

on Class 8 trucks for Level 4 Automation at present.

Chart 45: New Registration of Commercial Trucks in the U.S. (2019-24)

Source: Intro-act, NTEA

46 | P a g e

Chart 46: Class 3-8 Truck Sales in the U.S. (in ‘000s)

Source: Intro-act, Statista

▪ Class 8 trucking automation is looking even more lucrative than the development of autonomous cars. As technological

hurdles continue to emerge in the development of self-driving or autonomous cars, technology developers of autonomous

cars have gone on a back foot and pushed their deadline for the commercial launch of autonomous cars. On the other hand,

progress in trucking automation has looked quite promising, specifically within the Class 8 truck segment. This is also

reflected in the rising investor interest as investors are more inclined towards betting on the trucking segment rather than

on autonomous cars.

▪ Increasing deployment of autonomous trucks on the roads, compared to autonomous cars, is another key

metric suggesting that trucking automation is far ahead in the race to automation when compared to

autonomous cars.

▪ There are five levels of automation and trucking automation is presently focused on Level 4 automation. The wide variety

of technology aimed at the development of autonomous vehicles necessitated the classification of these technologies based

on the level of automation. The International Society of Automotive Engineers’ (SAE) Levels of Driving Automation, thus,

came out with the classifications which are both descriptive and technical. Its classification of various levels of automation

is based on how the dynamic driving task is divided between the human driver and the machine and ranges from Level 0

(no automation) to Level 5 (fully autonomous and no human interference). In all the other intermediate levels of

automation, the driving task is divided between the human driver and the machine.

▪ Level 1: Level 1 automation allows for driver assistance typically using adaptive cruise control to adjust speed

based on following distance.

▪ Level 2: In addition to automation as provided in Level 1, it also incorporates an active lane-keeping assist feature

which leads to partial automation.

161 195 223 254 264 283 296 317 301 327 349

12 10 912 13 14 14 19 21 22 22

31 42 55 60 67 72 72 79 81 85 93

41 40 47 52 55 62 63 72 78 52

41 47

48 54 59 60 62 64 66 51

107

171

195 185

220

249 193 192 251

276

192

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

47 | P a g e

▪ Level 3: Building on Level 2 automation, Level 3 is considered conditional automation where the driver is required

to resume control of the vehicle when prompted to do so. It is also in some sense a leap from non-automated to

automated driving.

▪ Level 4: Level 4 is classified as high automation level with the vehicles operating on the basis of inputs solely from

vehicle-embarked sensors or via a combination of self-sensor input and inputs from sensors embarked on other

vehicles and infrastructure that are communicated to the vehicle to the vehicle in near real-time. Autonomous

trucking technology companies are presently focused on Level 4 automation in which trucks operate without any

real-time input from human drivers. However, autonomous driving at Level 4 automation is likely to face

challenges from adverse weather conditions or when running on bad roads.

▪ Level 5: Level 5 automation will lead to vehicles driven by machine without any human interference irrespective

of geographic or weather conditions. Level 5 automation in trucks is likely to become a reality by 2030.

Chart 47: Five Levels of Autonomous Driving

Source: Intro-act, Interesting Engineering, International Society of Automotive Engineers

48 | P a g e

Chart 48: Level 5 Automation in Trucks is Likely by 2030

Source: Intro-act, International Transport Forum

▪ Middle mile is the longest and simplest portion of the supply chain, making it an attractive stretch to run autonomous

vehicles. The middle mile in the freight supply chain is defined as the stretch between two major regional hubs and is

typically located along highways. Middle mile typically connects terminals near an interstate to a terminal located not too

far off an interstate exit. Operating an autonomous truck in this middle mile is easier as trucks move on defined routes and

highways have fewer edges. Also, high average speed and long braking distance make these simpler for machines to

maneuver. However, this also means that perception technology will have to be more effective as it will need to see much

further ahead on the road. The efficiency of autonomous vehicles increases with the length of the middle mile. Also, the

movement of driverless trucks on geofenced lanes on highways makes virtual monitoring easy. On the other hand, the first

mile (the distance traveled between an origin, like a warehouse, and the highway) and the last mile (the distance between

an exit off a highway to a destination site – a factory, warehouse, or store) are much more complex for an autonomous

truck to maneuver.

▪ First mile and last mile pose a significant challenge to developers of autonomous vehicle technology due to the

presence of numerous edges, making it a non-preferred segment for automation.

▪ Leading autonomous trucking technology developers including Embark, TuSimple, Gatik, NuPort Robotics Inc.

are presently focusing on the middle mile segment to test their autonomous trucks.

49 | P a g e

Chart 49: Middle Mile is the Best Suited Segment for Trucking Automation

Source: Intro-act, World Economic Forum

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Deployment Will Start in Southern U.S. and Gradually Expand to the North

▪ Growing number of companies deploying autonomous trucks on pilot routes, coupled with a few trucks generating

revenue, suggests that commercialization is not too far off and is driving investor confidence higher. Autonomous trucks

are steadily advancing on the path to commercialization with more and more companies deploying autonomous trucks on

public roads. The deployments include those that are for demonstration purposes as well as for regular revenue generation,

and in both cases, shippers have entrusted the machine with their cargo and more critically safety of fellow road users. With

this confidence of users and fleet operators in the autonomous trucks, full-scale deployment is now a question of where

autonomous trucks are going to be available first and when they will be available fully.

Chart 50: Present Status of Autonomous Truck Deployment – Trial and Revenue Generating Routes

Source: Intro-act, Deloitte

▪ Deployment of autonomous trucks is likely to happen in three phases, starting with the Southwest corridor which is likely

to be the first to witness autonomous trucks taking over traditional trucks. Under Level 4 automation, an autonomous

truck is likely to be controlled by the machine throughout its journey except in certain specific conditions, such as

unfavorable weather, when the control is passed back to a human driver.

▪ The Southwest freight corridor in the U.S. offers friendly weather conditions and is likely to be the first stretch

to witness the commercial deployment of autonomous trucks. Texas, Arizona, and New Mexico followed by

Oklahoma will be among the first few states to allow full-scale deployment of autonomous trucks on their roads,

according to Deloitte. Other factors making these states a preferred option include amongst others 1) favorable

regulation, 2) massive freight economy, 3) strong highway infrastructure, and 4) potential opportunity for a public-

private business model.

51 | P a g e

Chart 51: Stage I – Southwest Likely to be The First Region to Deploy Autonomous Trucks

Source: Intro-act, Deloitte

▪ California and Oregon could be the next in line to adopt autonomous trucks followed by Florida as the network

expands to the Southeast. The autonomous truck freight network is then gradually expected to move North.

Chart 52: Stage II – Expansion Covers Southeastern States Through Florida and Virginia

Source: Intro-act, Deloitte

52 | P a g e

Chart 53: Stage III – Autonomous Trucks Reach Northern U.S.

Source: Intro-act, Deloitte

▪ Deployment of autonomous trucks will start with platooning in the initial phases and shift to fully autonomous trucks by

2027. Deployment of fully autonomous trucks, without drivers from loading to delivery, is likely to commercialize by 2027,

per McKinsey.

▪ We are presently in phase II of a four-phase rollout where truck platooning (of two trucks) with a driver in a lead

vehicle is being tested and deployed. This phase is likely to be spread over a three-to-five-year period, giving

connected vehicles an opportunity to develop and utilize algorithm links on highways while the driver takes

control of trucks when the truck leaves the highway. In this phase, Level 4 autonomous trucks will operate on

fenced highways with teleoperated or telemonitored drivers.

Chart 54: Truck Platooning Will be the First Viable Application for AV Technology

Source: Intro-act, World Economic Forum

53 | P a g e

▪ In the next wave of deployment, likely to span between 2025 and 2027, autonomous trucks will navigate

highways independently in driverless mode in platoons. Drivers will drop off the trucks at specific points on the

highway and again take control at the other end of the highway. This is typically classified as Level 4 automation

with constrained autonomy.

▪ Although full automation (Level 5 autonomy) is the end goal, it is likely to see the light of the day in 2027 and

beyond. In this wave, autonomous trucks will be able to travel end-to-end without any driver interference. It is

worth noting that fully autonomous trucks have the potential to reduce the total cost of ownership by ~45%

whereas Level 4 automation is likely to lead to 9% cost savings for fleet operators.

Chart 55: Deployment of Autonomous Trucks Will Happen in Four Stages Through 2027

Source: Intro-act, McKinsey

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Autonomous Trucks – Favorite Destination for AV-Focused Investors

▪ Autonomous trucks are leading the race to attract investors focused on the autonomous vehicles segment. While multiple

challenges present themselves to developers of autonomous cars, autonomous trucks have raced ahead to bring their

products on road. Even as robotaxi projects continue to disappoint – leading to so-called autonomous disillusionment within

the autonomous vehicle industry – autonomous truck trial projects continue to grow their safely driven miles on highways.

As a result, investor focus is shifting toward autonomous trucks (long-haul), grocery delivery, and automated warehouse

robots. This shift in focus is also driven by the fact that investors have realized that industry-focused autonomous vehicle

projects could be much more valuable due to 1) their ability to get to market much faster – compared to autonomous cars,

and 2) the potential opportunity they provide businesses to scale in a cost-efficient manner.

▪ The cumulative investment in autonomous vehicles is expected to reach ~$110 billion between 2015 and 2025,

despite the slowdown due to the COVID-19 pandemic, according to Alix Partners. The consulting house expects

the pace of investment in autonomous vehicles to slow down due to the pandemic, a sentiment also reflected in

a KPMG survey where 27% of respondents believed that COVID-19 is likely to have a significant negative impact

on investments in this space. The scaled-down investments in the space also reflect the shift in capital allocation

by OEMs and suppliers who are now prioritizing projects that can positively impact their bottom line in the next

two to three years. This monetary tightening is likely to force players who do have strong revenue generation

visibility out of business, leading to consolidation in the market and allowing stronger players to lead the race to

commercialization of autonomous vehicles. We believe that developers of autonomous trucks, such as Plus and

Embark, will stand to benefit from this shift in investor preference.

Chart 56: Cumulative Autonomous Vehicle Investment – 2015-25

Source: Intro-act, Alix Partners, FT

55 | P a g e

▪ Venture capital (VC) funding for autonomous vehicle startups regained momentum in 2Q20 and we expect this

momentum to remain strong over the next few years. VC investors bounced back strongly in 2Q20, allocating $3.8 billion

– a record level – to autonomous vehicle startups, led by Waymo’s $3 billion fund raise, per Pitchbook. Waymo raised this

late-stage VC funding from leading investors including Silver Lake, CPPIB, and Mubadala at a post-money valuation of $30.8

billion. The VC funding momentum in this sector continued in 3Q20 with funding into autonomous vehicles totaling $673.1

million during the quarter.

▪ It is estimated that following $5.8 billion of funds raised by autonomous vehicle startups through the first nine

months of 2020, full-year allocation to the sector reached a record level of $8.4 billion, up 33% y/y, with $2.5

billion capital raised in 4Q20.

▪ It is, however, worth noting that the VC preference is shifting from companies focused on developing full-stack

autonomous vehicles to those that specialize in a single aspect of autonomy, such as mapping or localization and

other companies who are augmenting the autonomous vehicles. One of the most attractive segments for the VCs

has been LiDAR (or LiDAR-based perception solution) – companies focused on this space raised $1.3 billion in VC

funding in 2019.

▪ Autonomous vehicle has been ranked as one of the top 10 innovation-led technologies, according to GlobalData,

and it is no wonder that it is attracting significant venture capital interest. Various autonomous vehicle startups,

steadily evolving to fulfil the software and hardware required for successful development of autonomous vehicles,

have attracted significant number of venture capitalist who have cumulatively poured in $40 billion+ in this

technology, per Pitchbook, since 2000. Trucks Venture Capital, with investments in 20 AV startups, leads the pack

of VCs with interest in this technology.

Chart 57: VC Funding for Autonomous Vehicle Companies Continued to Remain Strong in 2020…

Source: Intro-act, Pitchbook, DrakeStar. Data as of September 30, 2020.

56 | P a g e

Chart 58: … Dominated by Early-Stage VC Funding Deals

Source: Intro-act, Pitchbook, DrakeStar. Data as of September 30, 2020.

Chart 59: Waymo Topped the VC Funding List for 2020 Among Full Stack Autonomous Vehicle Companies…

Company

Date

Subsegment

Deal Size

($Million)

Deal Stage

Waymo

12-May-20

Passenger cars & Robotaxis

3,000

Late-Stage VC

Nuro

9-Nov-20

Passenger cars & Robotaxis

500

Late-Stage VC

Didi Chuxing

29-May-20

Passenger cars & Robotaxis

500

Series A

Pony.ai

25-Feb-20

Passenger cars & Robotaxis

462

Series B

TuSimple

25-Nov-20

Trucking Stack

350

Series E

Pony.ai

5-Nov-20

Passenger cars & Robotaxis

267

Late-Stage VC

WeRide

23-Dec-20

Passenger cars & Robotaxis

200

Series B

Inceptio Technology

9-Nov-20

Trucking Stack

120

Early-Stage VC

PlusAI

21-Nov-20

Trucking Stack

100

Late-Stage VC

Inceptio Technology

26-Apr-20

Trucking Stack

100

Series A

Seegrid

15-Sep-20

Logistics Applications

Series B

Vecna Robotics

7-Jan-20

Logistics Applications

Series A1

FiveAI

3-Mar-20

Passenger cars & Robotaxis

Series B

Clearpath Robotics

22-Sep-20

Agricultural Applications

Early-Stage VC

Clearpath Robotics

1-Jun-20

Agricultural Applications

Series C

Neolix Technologies

29-Mar-20

Logistics Applications

Series A1

Gatik

23-Nov-20

First-Last Mile Delivery

Series A

Aimotive

22-Jun-20

Passenger cars & Robotaxis

Late-Stage VC

57 | P a g e

Locomation

16-Oct-20

Trucking Stack

Seed Round

Boxbot

5-Mar-20

First-Last Mile Delivery

Series A

Vi Pioneers

5-Mar-20

Industrial Applications

Series A2

Locomation

8-Jun-20

Trucking Stack

Seed Round

Source: Intro-act, Pitchbook, DrakeStar

Chart 60: … whereas Trucks Venture Capital Topped the List of VCs Funding AV Startups Since 2000

Venture Capital

# Investment in AV Startups

Key Investments

Trucks Venture Capital

May Mobility, Cruise, Gatik

New Enterprise Associates

Outrider, Drive.ai, Built Robotics

Toyota AI Ventures

May Mobility, Apex.AI, Nauto

IDG Capital

Pony.ai, TuSimple, Zoox

Maniv Mobility

Oryx Vision, Drive.ai, Phantom Auto

Andreessen Horowitz

Waymo, Applied Intuition, Lyft

Baidu Ventures

Hesai, Apollo Taxi, Nio

Sequoia Capital China

Pony.ai, PlusAI, Nio

Fontinalis Partners

Gatik, Ike Robotics, nuTonomy

FM Capital

Gatik, Optimus Ride, Outrider

Lux Capital Management

Aeva, Zoox

SAIC Capital

AutoX, PlusAI

Source: Intro-act, Pitchbook, Business Insider

Chart 61: LiDAR Developers Attracted the VC Funds the Most in 2020

Company

Date

Subsegment

Deal Size

($Million)

Deal Stage

Luminar

8-Sep-20

LiDAR

170

Late-Stage VC

Cepton Technologies

5-Feb-20

LiDAR

Series C

Uhnder

1-Nov-20

RADAR

Series C

Ouster

8-Sep-20

LiDAR

Series B

Opsys Technologies

7-Jan-20

LiDAR

Series B

Ouster

26-Jun-20

LiDAR

Series B

Trilumina

13-Mar-20

LiDAR

Series B

Enview

8-May-20

LiDAR

Series A1

SiLC Technologies

4-Mar-20

LiDAR

Seed Round

Aeye

11-Mar-20

LiDAR

Late-Stage VC

Zvision

7-Apr-20

RADAR

Series A1

SOS Lab

1-Apr-20

LiDAR

Series A

NewSight Imaging

18-May-20

LiDAR

Series A

Regulus Cyber

18-May-20

LiDAR

Series B

Psionic

24-Jun-20

LiDAR

Seed Round

Source: Intro-act, Pitchbook, DrakeStar

58 | P a g e

▪ We believe that the autonomous vehicle industry is now set to enter its next phase of funding and will start receiving

capital from public equity investors – as demonstrated by the recent IPO and announced SPAC deals – which will result

in value discovery and multiple expansion. The development, testing, and deployment of an autonomous vehicle is a capital

intensive and time-consuming process and requires companies to pour in huge sums of money before they can build a

revenue-generating model. While VCs have met the funding requirements of this industry so far, we believe that the

encouraging progress toward commercialization will now attract public investors to this space. As a result, we believe that

the autonomous vehicle industry is now on the cusp of finding a place in public institutional investor’s portfolio. This is

evident from the recent public listing and SPAC deals announced in this space.

▪ Of the 11 autonomous vehicle companies that have raised or are raising public capital, TuSimple (NASDAQ: TSP)

is the only one to go public through the IPO route. TuSimple became the first and remains the only autonomous

vehicle company to raise public capital through an IPO – it raised $1.35 billion in April 2021, which valued the

company at $8.5 billion. Founded in 2015, the company had already received VC funding to the tune of ~$650

million prior to the public raise and has autonomous truck operations in U.S. and China.

▪ Plus (NASDAQ: HCIC), Embark (NYSE: NGAB), and Aurora (NASDAQ: RTPY) lead the list of autonomous

trucking/vehicle companies tapping public investor capital through the SPAC route. The list of autonomous

vehicle companies looking to raise public capital through the SPAC route was dominated by LiDAR companies in

2020, with seven of the 10 companies going public through SPAC deals in 2020 and 2021 belonging to this

segment. Of these seven companies, five have already raised $1.8 billion capital whereas the remaining two are

targeting a cumulative capital raise of $733 million in 2H21, per Pitchbook. However, steady progress toward

commercialization has now attracted investors to full-stack developers. Aurora is planning to raise $2 billion

through a SPAC merger. At the same time, the other two full-stack developers, Embark and Plus, have also signed

deals with leading SPACs to go public. (See chart below.)

▪ SPACs provide autonomous vehicle companies an opportunity to raise funds from public investors within a

short time frame. SPACs are shell companies that are formed to raise capital through an IPO and then, following

the IPO, use the proceeds to acquire one or more unspecified businesses. SPACs offer numerous benefits for

companies looking to go public as compared to a traditional IPO. First, SPACs give companies an opportunity to

go public in 4-6 months compared to the 12-18 months required for a conventional IPO. Another benefit for

companies seeking to go public via SPACs is favorable underwriter discounts (5.5% vs 7% for traditional IPOs).

Finally, and most importantly, SPACs offer disruptive (and often pre-revenue) companies the chance to go public

and attract institutional investor interest, an otherwise difficult proposition for pre-revenue companies.

Chart 62: Autonomous Vehicle – SPAC Activity

Company

Announcement

Date

Completion Date

Capital Raise

($ Million)

Valuation

($ Million)

Value Chain Focus

Aeva

2-Nov-20

15-Mar-21

560

2,100

LiDAR

Luminar

24-Aug-20

3-Dec-20

420

3,400

LiDAR

Innoviz

11-Dec-20

6-Apr-21

380

1,400

LiDAR

Ouster

22-Dec-20

12-Mar-21

300

1,900

LiDAR

Velodyne

2-Jul-20

30-Sep-20

150

4,000

LiDAR

Aurora

15-Jul-21

2H21

2,000

11,000

Full-Stack Developer

Embark Trucks

23-Jun-21

2H21

614

5,160

Full-Stack Developer

Plus

10-May-21

2H21

500

3,300

Full-Stack Developer

AEye

17-Feb-21

3Q21

455

2,000

LiDAR

Quanergy

22-Jun-21

2H21

278

1,100

LiDAR

Source: Intro-act, Pitchbook

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Key Names for Investors to Monitor

▪ We believe investors evaluating the Autonomous Trucking space should focus on the following names. We discuss each

of these in detail below.

▪ Plus (SPAC Deal – NASDAQ: HCIC)

▪ Embark Trucks (SPAC Deal – NYSE: NGAB)

▪ TuSimple (NASDAQ: TSP)

▪ Aurora (SPAC Deal – NASDAQ: RTPY)

▪ Locomation (Private)

▪ Waymo (NASDAQ: GOOGL)

Plus (NASDAQ: HCIC)

▪ Plus (formerly Plus.ai) is a global provider of self-driving truck technology that makes trucks safer, more efficient, more

comfortable, and better for the environment. The long-haul trucking market across the U.S. and China is worth about $1.2

trillion ($670 billion in the U.S. and $530 billion China). However, freight operators and logistics services providers face

multiple challenges, including high driver turn-over, driver wage inflation, spiraling insurance costs, and stringent

regulations, which collectively limit their operating ability and compress margins. The answer to these challenges is

PlusDrive, a commercialized full stack supervised autonomous solution and the first and necessary step toward Level 4

driver-out autonomy.

Chart 63: Plus is Well Positioned to Capitalize on the Autonomous Freight Opportunity

Source: Intro-act, Plus Investor Presentation

▪ Plus’ autonomous solution PlusDrive boasts of industry-leading technology that positions the company as a leader in the

autonomous trucking market. Plus’s cutting-edge autonomous driving technology solution is comprised of three pillars,

including: 1) Proprietary full-stack “L4” software that is designed for safety, customized for trucks, optimized for

performance, and scaled for machine learning; 2) A mass-production ready, low cost, high-performance hardware

platform that enables a truck to drive autonomously; and 3) A data engine that leverages real-world driving data to

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continuously upgrade algorithms and ultimately reach L4 autonomy. By 2024, Plus estimates accumulating 8 billion miles of

data, which the management believes will be sufficient to demonstrate that PlusDrive is safer than a human driver. The

company’s technology leadership is also reflected in its portfolio of 200+ patents and patent applications, covering over 30

countries.

▪ PlusDrive uses advanced sensing technologies, including radar, LiDAR, and cameras to provide a 360-degree

sensing system. Data gathered through the sensors help the system identify objects nearby, plan its course,

predict the movement of those objects, and finally control the vehicle to make its next move safely. Plus’s

advanced multi-modal sensor system solves vibration and long-range camera drift problems for mass production

as well as adequately addresses adverse weather and lighting for commercial deployment.

Chart 64: The Three Pillars of Plus’ Autonomous Driving Technology

Source: Intro-act, Plus Investor Presentation

▪ In addition, PlusDrive provides significant value to its customers and accelerates the development of the

company’s L4 fully autonomous system. Plus’ Supervised L4 (SL4) is expected to increase fleet operator’s gross

profit per truck by 30% to 70%. Further, its L4 autonomous driving technology is projected to reduce total fleet

operating expense by 38% percent in the future, when trucks can be operated without a driver. The 38% cost

efficiency unlocked by Level 4 technology translates to savings of over $250 billion in the U.S., and over $200

billion in China.

▪ The company is a strategic leader in commercialization with a clear commercialization roadmap that starts with the mass

production of PlusDrive this year and reaches full autonomy with L4 trucks by the end of 2024. Plus is pursuing an

incremental commercialization roadmap to full Level 4, starting this year with PlusDrive, a driver-in and highway-only

product, progressing to PlusDrive Navigate in 2022 and 2023, where it plans to extend the operational design domain for

ramp-to-ramp highway operation. And then as the company achieves billions of miles, it will be ready to go to PlusDrive

fully autonomous in 2024. The company has a commercially validated software and is starting to deliver products to its

customers in the U.S. and China this year, utilizing its full-stack L4 autonomous driving technology and has received over

7,000 units of orders or pre-orders for its products.

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Chart 65: Plus Plans to Reach Full Autonomy by the end of 2024

Source: Intro-act, Plus Investor Presentation

▪ Plus plans to begin mass production of its autonomous driving solution, PlusDrive, starting in 2021 with FAW,

the world’s largest heavy-truck manufacturer, which produced more heavy-duty trucks in 2020 than both the

U.S and European markets. In China, Plus will power the flagship product of FAW starting this year with the mass

production of the FAW J7L3, which was jointly developed by Plus and FAW. In the U.S., Plus was selected as the

provider of 1,000 autonomy-enabled trucks to Amazon and has already started delivering its initial batch of

PlusDrive-enabled retro-fit units. In addition, Plus is working with some of the largest fleets in the U.S. and China

to pilot commercial freight operations. The company is also working with IVECO, one of the top global truck

manufacturers in the world, to jointly develop autonomous trucks that will be deployed across China, Europe, and

other geographies. It is also working with Cummins Inc. on using autonomous technology in trucks powered by

natural gas. The company recently announced a tie up with tire major Goodyear where both the companies will

explore how Plus' autonomous driving system can incorporate feedback from Goodyear's connected tires into

Plus' online, machine learning-based fuel economy efficiency, to further improve fuel economy.

Chart 66: PlusDrive Product Roadmap

Source: Intro-act, Plus Investor Presentation

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▪ Results from commercial pilots run by the company in both China and the U.S. augur well for its success. Plus

ran a commercial pilot with SF Express in China, where it operates on a route that is about 500 miles each way.

Throughout this pilot, Plus has been running a mix of manual and autonomous operation (90%) to demonstrate

fuel savings (10%-20%). It also ran a similar pilot for a key customer in the U.S., where it retrofitted five trucks and

ran commercial freight with different trailers and trailer loading. In over 140 runs with a mix of manual and

autonomous, Plus demonstrated a high degree of autonomous operation (90%+) and fuel savings (10%-20%),

resulting in a commercial contract. It is important to note that while demonstrating fuel savings is a key part of

these pilots, for customers, the full proposition of fuel savings, safety and driver comfort is what makes Plus’

product very attractive.

▪ The company has a scalable and profitable path to the driver-out autonomy and expects to grow its sales to $7.2 billion+

by 2025, with a gross profit margin of ~25% and an EBITDA margin of 22%. Plus expects to start generating revenue in

2021 from mass-produced and retrofitted trucks as the company begins its SL4 truck production and delivery in China and

the U.S. in 2021. Its go-to-market strategy for today's driver-in supervised Level 4 solution is to offer PlusDrive as a

technology product to fleet customers. In China, it sells integrated autonomous trucks to fleet customers, and in the U.S.,

Plus sells PlusDrive as an add-on option to the fleet customers. Over time and through billions of real-world miles, Plus

expects to collect the data required to demonstrate the safety of PlusDrive to be operated without a driver. Its go-to-market

strategy when it reaches full driver-out Level 4 for autonomy is to offer the PlusDrive solution as a service to fleet customers,

i.e., Plus will co-sell together with its truck OEM partners. Fleets purchase trucks from truck OEMs, and they purchase the

PlusDrive solutions from the company by paying it upfront fee and a recurring annual service fee. Driven by this

commercialization strategy, Plus expects to grow its sales to $7.2 billion+ by 2025, with a gross profit margin of ~25% and

an EBITDA margin of 22%. The company aims to turn cash flow positive by 2023 and despite the massive growth over the

next four years, its market penetration will stand at ~7%, suggesting a long growth runway.

Chart 67: Plus – Financial Forecast and Key Takeaways

Source: Intro-act, Plus Investor Presentation

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▪ Providing sustainability benefits while driving growth. The company’s technology delivers a multitude of benefits in terms

of improved safety, efficiency, reliability, comfort, and sustainability. Its L4 autonomous driving system will make the roads

safer while reducing operating costs by approximately 38% and reducing carbon emissions by approximately 1.1 million

tons between 2021 and 2024. Its L4 system is also projected to improve asset utilization, increasing revenue per truck by

100%. At the same time, with proprietary algorithms that are constantly optimizing fuel use, PlusDrive-enabled trucks save

an estimated 10% to 20% in fuel costs.

▪ Plus’ strong value proposition is reflected in its strong ecosystem of customers, partners, and investors; this ecosystem

has been boosted by the addition of Amazon.com Inc (AMZN) both as a customer as well as an investor. Plus’ business is

supported and validated by a strong ecosystem of customers, partners, and investors. This list includes some of the largest

and most technologically advanced shippers, carriers, freight platforms, OEMs, automotive component suppliers, as well as

many resourceful and forward-looking investors in the U.S. and China. (See chart below.) With this ecosystem partners, Plus

is well on its way to deliver safer, scalable, and sustainable solutions for the transportation industry.

▪ In a further validation of its investment potential, Plus recently disclosed that global retail major AMZN has

contracted Plus to purchase at least 1,000 Plus Retrofit units for its delivery fleet in a deal worth up to $150

million. While these vehicles will still require a human driver inside, the deployment of the retrofitted fleet will

still be a massive milestone for the autonomous trucking industry in general and Plus in specific, given the global

spread of AMZN’s retail and logistics operations, suggesting that the contract represents a huge opportunity and

competitive advantage for Plus. In addition, AMZN has the right to buy preferred shares of Plus via a warrant at

a price of $0.46647 per share – that amounts to a ~20% stake based on Plus’s shares outstanding before its

planned merger with Hennessy Capital Investment Corp. V. (Discussed in detail below.) The fact that AMZN chose

Plus over other players like TuSimple (TSP), Embark, and Aurora for this contract is a validation of Plus’s technology

and strategy.

Chart 68: Validation – Customers, Partners, and Investors

Source: Intro-act, Plus Investor Presentation

▪ On May 10, Plus announced that it is going to become a publicly listed company through a business combination with

Hennessy Capital Investment Corp. V (NASDAQ: HCIC) that values Plus at ~$3.3 billion. Hennessy Capital Investment Corp.

V is a special purpose acquisition company (or SPAC) which raised $345 million in its IPO in January 2021 and is listed on the

Nasdaq Capital Market (NASDAQ: HCIC). The business combination is expected to deliver up to approximately $500 million

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in gross proceeds to Plus at closing, including approximately $345 million of cash held in HCIC V’s trust account, assuming

no redemptions by HCIC V’s public stockholders. The business combination is further supported by a fully committed

common stock PIPE at $10.00 per share of $150 million, including investments from funds and accounts managed by

BlackRock and the D. E. Shaw group, among other institutional investors.

▪ Under the terms of the business combination, Plus’s existing shareholders will convert 100% of their ownership

stakes into the combined company and are expected to own approximately 80% of the post-combination

company at close.

▪ The proposed business combination has been unanimously approved by the Boards of Directors of both Plus and

HCIC V and is expected to close in the third quarter of 2021, subject to the satisfaction of the necessary regulatory

approvals and customary closing conditions, including the approval of HCIC V’s shareholders. Upon closing, Plus

will be a publicly traded company and its common stock is expected to trade on the NYSE under the ticker symbol

“PLAV”.

▪ Plus plans to use capital from the HCIC transaction to accelerate commercialization and product adoption by

building out its data engine, increasing its R&D efforts, strengthening its supply chain and deepening its

collaborations with partners in the automotive industry, and driving faster production adoption. (See chart

below.)

▪ Investor appetite for Plus is also visible in earlier fund raises by the company. Plus raised $200 million in a

funding round in February that attracted new investors such as Guotai Junan International Holdings and Citic

Private Equity Funds Management Co. It raised an additional $220 million from investors co-led by FountainVest

Partners and ClearVue Partners in March.

Chart 69: Capital from the HCIC Transaction Creates Incremental Opportunity to Accelerate Commercialization and

Product Adoption

Source: Intro-act, Plus Investor Presentation

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Embark Trucks (NYSE: NGAB)

▪ Embark Trucks, Inc. is an autonomous vehicle software-as-a-service (SaaS) company focused exclusively on the U.S.

trucking market and has been operating America’s longest-running self-driving truck program. Founded in San Francisco

by CEO Alex Rodrigues and CTO Brandon Moak, Embark is a leading developer of software for self-driving trucks. Over the

last five years, Embark has operated America’s longest running road-testing program for self-driving trucks to refine the

company’s sophisticated self-driving software – purpose-built to navigate Class 8 trucks on long-distance freight trips.

Embark partners with leading carriers, who pay a per-mile subscription cost for Embark’s Driver software to deploy and

enable self-driving trucks within their fleets. The company’s primary offering is Embark Driver, its highly differentiated and

advanced software stack. Embark delivers this software as a subscription, partnering with leading carriers who pay a per

mile license fee and providing them with Embark’s Driver software as well as a suite of supporting services. These include

Guardian, Embark’s cloud-based dispatch and monitoring solution, which delivers 24/7 monitoring and remote assist

capabilities. Embark has also developed Embark Universal Interface, a standard sensor module and compute module

designed to interface with most major steering and braking actuators. This modular integration approach is designed to

enable its carrier partners to purchase an Embark Driver compatible option from their preferred manufacturers.

Chart 70: Embark is the Longest-Running Self Driving Program in the U.S.

Source: Intro-act, Embark Investor Presentation

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▪ There are two key things that distinguish Embark as a leader in the U.S. AV trucking market: 1) world-class self-driving

software, and 2) a business model based on partnering with carriers. We discuss both in detail below.

▪ Led by a world class engineering team, Embark is focused on building the best self-driving software. Embark’s engineering

team stands out for its deep experience with the self-driving truck technology and for having a pragmatic balance of

expertise across both academia and industry. Its Co-Founder and CEO Alex Rodrigues has deep experience in robotics, has

developed 11 different robotics platforms, won a Robotics World Championship, and built the first self-driving vehicle to

operate on public roads in Canada. Embark’s engineering team has been commercially testing self-driving trucks on U.S.

highways longer than anyone else, a key competitive advantage in our view. This singular and disciplined focus on the

trucking market in the United States has allowed Embark to achieve many industry-first technology milestones – including

the first self-driving truck to drive coast-to-coast – and positions Embark to be a leader in autonomous trucking software.

Chart 71: High-Level Architecture of Embark’s Self-Driving Software

Source: Intro-act, Embark Investor Presentation

▪ By focusing its efforts on the well-defined problem of autonomously driving trucks to and along highways, Embark has

been able to develop proprietary cutting-edge techniques across the stack – in perception, fusion, and planning – that

allow its trucks to drive smoothly and reliably like a human.

▪ In perception, Embark has been a pioneer in cutting edge active learning techniques that learn from real-world

experience to rapidly and continuously improve its perception performance. Embark’s perception has used the

million plus miles driven by its trucks to build a machine learning system that identifies the most relevant

detections and provides the most useful insights into critical edge cases. With the most valuable data

automatically identified, Embark can focus its labeling and training efforts to provide the quickest, most effective

feedback loop for Embark Driver, resulting in constantly improving performance.

▪ Every second as the truck drives, its perception data is then fed into Embark’s proprietary VisionMap Fusion

System (VMF). Vision-Map Fusion builds on top of the HD mapping approach generally used in the market. While

HD mapping is a useful starting point, it treats maps as static and unchanging, resulting in poor performance on

interstate highways – which are almost always undergoing construction – and maps which are highly resource

intensive to create and maintain. Embark’s focus and experience dealing with highway construction has allowed

it to flip this paradigm on its head. Vision-Map Fusion layers on top of a traditional mapping stack, but instead of

treating the map as static it treats the map as a dynamic changing environment. VMF leverages Embark’s cutting-

edge non-linear-optimization techniques to update the map in real-time using detailed road geometry data from

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Embark’s LiDAR and Camera sensors. This allows Embark’s driver software to detect and respond to new situations

where the map may be outdated on the-fly. For example, earlier this year, Embark released the first public

demonstration of a self-driving truck detecting and responding to lane closures in a construction zone – without

mapping them ahead of time.

Chart 72: Embark’s proprietary VisionMap Fusion System (VMF)

Source: Intro-act, Embark Investor Presentation

▪ Finally, Embark’s Prediction and Planning Stack is what enables Embark Driver to deliver smooth driving in

traffic and other challenging driving conditions that safety drivers have described as human-like—a key to

superior performance. This integration of planning and prediction maintains the critical feedback loop between

actions the self-driving vehicle takes and how those actions cascade and affect the likely actions of other road

users over time. This feedback loop is particularly critical in highway driving situations where maneuvers like lane

changes in heavy traffic often need to be planned far in advance.

▪ Based on its experience with unique challenges of highway driving, Embark’s engineering team has developed

a proprietary integrated prediction and planning stack that is able to simulate up to 1200 different potential

scenarios per second. Each of these 1200 simulations explore up to 60 seconds into the future and together they

cover a wide variety of possibilities about how the actions of its truck would impact the predicted actions of other

vehicles. By having a broad understanding of all the possibilities, the truck can make smart decisions about where

exactly the best spot to be is at any given time.

▪ Thanks to its best-in-class technology and emphasis on safety, Embark has an industry-leading safety track-

record, with more than one million real world miles driven without a DOT-reportable incident.

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Chart 73: Embark’s Prediction and Planning Stack

Source: Intro-act, Embark Investor Presentation

▪ Embark’s technology is expected to unlock significant value for its customers – the carriers – through four factors that

will drive adoption: 1) compelling economics for carriers, 2) improved competitiveness for shippers, 3) alleviation of

driver shortage, and 4) fulfilling sustainability and safety goals of its customers. In a purely economic sense, Embark's

system should allow the carrier to get almost three times as many miles out of the same investment. Where a human driver

is limited to 60 hours of driving time per week by the federal hours of service, a driverless truck can operate 24/7. On top

of that fairly significant increase in revenue from almost 3x more usage, driverless trucks should be able to nearly double

the profit margin for the carrier, even after accounting for Embark's subscription cost. We believe this change in the business

model for the carriers is transformative. Further, according to management estimates, use of Embark’s technology can

result in up to a 10% increase in fuel efficiency per mile, double the daily range from 500 to 1,000 miles, and reduce delivery

time by 40%. In addition to the economic benefits, Embark provides customers with a superior product that fulfills their

sustainability and safety goals by driving a reduction in carbon emissions due to better speed management. Finally, Embark

addresses a critical strategic barrier for customers by helping them solve the growing driver shortage, while also improving

the quality of life for drivers by allowing them to drive locally and go home to their families every night.

Chart 74: Embark’s Technology is Expected to Provide Compelling Economics for Carriers

Source: Intro-act, Embark Investor Presentation

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▪ The second key differentiator for Embark is its business model that based on partnering, not competing, with carriers.

Embark’s growth plan does not involve it building, owning, or operating trucks in competition with the current trucking

carrier industry. Rather, Embark provides software and logistics support to established carriers as customers, allowing them

to deliver better, cheaper, and faster freight service. Under Embark’s model, carriers continue to own and autonomously

operate trucks purchased from their chosen OEM which are factory equipped by the OEM with the required hardware. This

asset-light business model is expeditiously scalable without significant industry upheaval or requiring unreasonable

investment by Embark. Building direct relationships with carriers and offering modular integration across OEM platforms

is a proven approach in the trucking industry – pioneered by companies like Cummins Engine – and positions Embark to be

the partner of choice for multi-OEM fleets. By focusing on offering best-in-class software and partnering with leading

carriers who own and operate self-driving fleets, Embark will be able to rapidly scale its business and efficiently bring its

self-driving truck technology to market.

Chart 75: Embark’s Business Model Offers Meaningful Operational Savings and Collaborates Rather than Competes

with Carriers

Source: Intro-act, Embark Investor Presentation

▪ As part of this strategy, Embark – through its Partner Development Program – is currently working with shippers and

carriers including Anheuser-Busch InBev, HP Inc., Werner Enterprises, Mesilla Valley Transportation, and Bison Transport,

to help prepare their fleets to integrate self-driving technology and scale with Embark’s technology. Embark is working

with shippers and carriers to analyze their nationwide freight networks at a lane level and develop detailed rollout plans for

converting portions of these networks to autonomous. It is hauling with carriers and shippers to refine operations, paving

the way for partners to quickly, safely and effectively scale autonomous trucking. The company is also working closely with

carriers to define business model details such as dispatching, monitoring, maintenance, liability and transfer hub availability,

resulting in a clear path to scale.

Chart 76: Top Shippers and Carriers are Partnering with Embark

Source: Intro-act, Embark Investor Presentation

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▪ Embark has a well-defined commercialization strategy with a two-stage roll out, the first of which starts in 2024, and a

path to rapid profitable growth upon commercialization. The company has planned a two-phase rollout based on

geography. It expects a phase 1 rollout in 2024 based in the sunbelt states, with a phase 2 rollout for the remainder of the

lower 48 commencing in 2026. At its root, Embark is a software-as-a-service (SaaS) business, which means customers

subscribe to it service and the company receives a payment per mile. As a result, the company’s financial projections are

based on two primary items: (1) the number of autonomous miles within Embark’s operational design domain (ODD), and

(2) the associated per mile pricing. Management estimates its total addressable market in the U.S. to be ~300 billion miles

and believes transfer point hauls are only economical to carriers at greater than 300 miles and direct-to-customer hauls

greater than 100 miles. Given this context, ~86% of these lanes are therefore economical for autonomous trucking. Adjust

this to capture Embark’s ODD, which is estimated at ~80% of these lanes, and is expected to grow to about 90% over time

as Embark’s technology continues to develop. This is Embark’s serviceable market. The cost of a human-driven truck is $1.76

per mile and management believes that the use of its technology can bring it down by ~45% or 80 cents per mile to $0.96

per mile. Embark will share a portion of these net savings with it partners and estimates a gross revenue rate of $0.44 per

mile. Based on these estimates, the company expects to grow its revenue to $2.7 billion by 2025 with a gross margin of

69%, a number that is expected to grow to ~74% over the long term as the company scales its business across the U.S.

Chart 77: Two Phase Roll Out

Chart 78: Embark – Financial Projections

Source: Intro-act, Embark Investor Presentation

▪ On June 23, Embark announced that it will go public through a merger with Northern Genesis Acquisition Corp. II (NYSE:

NGAB), in a deal that assigns the company a pro forma implied enterprise value of ~$4.55 billion and market

capitalization/equity value of ~$5.16 billion. Northern Genesis II is a $414 million operator-backed SPAC which is highly

differentiated and consisting of proven entrepreneurial business builders that have a long history of creating shareholder

value. Most recently, Northern Genesis was the SPAC sponsor in the Lion Electric mobility transformation story.

▪ Upon closing of the transaction, the combined company is expected to receive approximately $614 million of

gross cash proceeds, comprised of approximately $414 million of cash held in the trust account of Northern

Genesis 2, assuming no redemptions by NGAB stockholders, and a $200 million fully committed PIPE at $10 per

share. The PIPE is supported by top-tier anchor investors including Canada Pension Plan Investment Board, Knight-

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Swift Transportation, Mubadala Capital, Sequoia Capital, and Tiger Global Management, together with the

Northern Genesis management team and its associated institutional investors.

▪ Embark’s existing shareholders and management are rolling over 100% of their equity, confirming their

commitment and belief in the company’s long-term growth potential.

▪ The Boards of Directors for both Embark and Northern Genesis 2 have unanimously approved the proposed

business combination, which is expected to be completed in the second half of 2021, subject to, among other

things, the approval by Northern Genesis 2’s stockholders.

▪ Proceeds from the transaction are expected to fund Embark’s growth through commercialization in 2024, with

the most notable uses of capital being team expansion (70%), on-road testing (13%), OEM Co-Development (12%),

and expanding coverage map (5%).

Chart 79: Transaction Overview: Embark – Northern Genesis Acquisition Corp. II

Source: Intro-act, Embark Investor Presentation

▪ Embark also recently announced that Elaine Chao, former Secretary of Transportation and Labor, has joined Embark’s

Board of Directors. Secretary Chao’s extensive public and private sector leadership experience will further strengthen

Embark’s position in the AV industry by helping Embark in its dialogue to establish the regulatory framework to further it

AV policy leadership through the next stage of deployment. Prior to serving as Secretary of Transportation, Secretary Chao

established a distinguished career both in and out of government, serving for eight years as the U.S. Secretary of Labor and

as President and CEO of United Way of America. The addition of Secretary Chao strengthens Embark’s already strong

leadership team and board of directors, that now includes business leaders, engineering leaders, and world class investors

and regulatory experts.

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TuSimple (NASDAQ: TSP)

▪ TuSimple (TSP) is one of the leading autonomous tucking companies with industry-leading technology, world class

partners, and $1.8bn+ in funding to date. TSP has developed industry-leading autonomous technology specifically designed

for semi-trucks, which has enabled it to build the world’s first Autonomous Freight Network (AFN) in partnership with

world-class shippers, carriers, railroads, freight brokers, fleet asset owners, and truck hardware partners. Since its founding

in 2015, TSP has developed a fully integrated software and hardware solution enabling which is one of the world’s most

advanced Level 4 driver-out autonomous semi-truck technology. Hallmarks of its proprietary semi-truck specific

technology include 1,000-meter perception range, 35 second planning horizon, high-definition maps with accuracy within

five centimeters, and an integrated L4 autonomous semi-truck design comprising of a fully redundant sensor suite and

components. Long-range perception, advanced planning and decision-making, and highly accurate mapping are critical

capabilities for the autonomous operation of semi-trucks, which are heavy, articulated vehicles that need to be able to

operate at highway speeds. TSP believes that it is the first and only company to demonstrate these capabilities and achieve

L4 autonomous semi-trucks driving on both highways and surface streets as well as the first company to autonomously haul

a paid freight load.

Chart 80: TuSimple (TSP) – Overview

Source: Intro-act, TuSimple (TSP) Investor Presentation

▪ TSP’s Autonomous Freight Network (AFN) is aimed at addressing the trucking industry’s most pressing challenges and

can revolutionize the way freight moves. AFN is designed to provide a comprehensive, turnkey, autonomous freight

solution that supplies users with access to purpose-built L4 autonomous semi-trucks operating on HD digital mapped routes

connecting a nationwide network of terminals. Key advantages of AFN solution design include: 1) improved safety, 2)

reliable freight capacity – AFN provides users with reliable autonomous freight capacity as a service which is unencumbered

by prevailing truck driver shortages., 3) higher efficiency – TSP’s L4 autonomous semi-truck solution will reduce freight

operating costs by up to 50% per mile, and 4) environmental impact – TSP expects its solution to deliver over 10% better

fuel efficiency than traditional trucking through optimized truck control and driving operations which can deliver a

measurable reduction in carbon emissions.

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▪ Commercial adoption will be driven by the benefits that AFN can deliver to industry stakeholders. AFN leverages TSPs’

proprietary L4 autonomous semi-trucks, HD digital route mapping capabilities, and TuSimple Connect cloud-based

autonomous operations oversight system to provide substantial benefits to the key truck freight industry stakeholders. The

“plug and play” nature of the solution will allow any truck freight market participant to access and benefit from TSP’s

autonomous freight capacity. Shippers, carriers, and railroads gain access to reliable and safe freight capacity at a

substantially lower annual total cost of ownership when direct labor is removed from the per mile cost structure. Removing

the driver from long haul operations allows shippers, carriers, and railroads to reallocate scarce driver resources to customer

facing first and last mile routes. Freight brokers benefit from the reliability of autonomy, which allows them to more

efficiently match demand with the lowest cost long haul freight capacity.

Chart 81: TSP’s AFN Benefits a Wide Range of Industry Stakeholders

Source: Intro-act, TuSimple (TSP) Investor Presentation

▪ Another driver of adoption will be AFN’s ability to accommodate multiple service models to meet client requirements

and offer superior customer experience. The two service models accommodated by AFN are:

▪ Carrier-Owned Capacity. Shippers, carriers, and railroads that prefer to own their fleet will be able to purchase

TSP’s L4 autonomous semi-truck from a semi-truck original equipment manufacturer (OEM) partner and subscribe

to TuSimple Path—a turnkey product to enable autonomous operations across TSP’s network. TuSimple Path

includes features such as on-board autonomous driving software, TuSimple Connect cloud-based autonomous

operations oversight system, HD digital route mapping support, and emergency roadside assistance. Users will

pay TuSimple a per mile, usage-based fee for access to TuSimple Path and benefit from lower overall freight costs

with an expected payback period of less than one year on their upfront incremental capital investment to

purchase TSP’s purpose-built L4 autonomous semi-trucks.

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▪ TuSimple Capacity. TSP’s fleet of L4 autonomous semi-trucks, financed through third party fleet asset owners,

will serve users that desire access to safe, reliable, low cost, and more environmentally friendly freight

transportation without owning semi-truck assets. Users of TuSimple Capacity can range from relatively smaller

users of freight logistics to large shippers, carriers, and railroads seeking to supplement their own captive fleet

for incremental freight capacity. TSP will charge users of TuSimple Capacity a per mile rate to ship freight, which

it expects will be at a meaningful discount to prevailing market freight rates. TSP’s competitive advantage in terms

of pricing will be enabled by its anticipated cost structure, which is expected to be significantly lower than that of

human-operated semi-trucks.

▪ TSP has created a world class ecosystem of partners consisting of shippers, carriers, railroads, freight brokers, fleet asset

owners, OEMs, Tier 1 components suppliers, and third-party service providers that will de-risk commercialization of AFN,

enable rapid adoption of its autonomous freight solution, and allow it to build an attractive, network-based business

model.

▪ TSP is working in partnership with leading semi-truck OEMs Navistar and TRATON as well as components

partners to build the world’s first purpose-built L4 autonomous semi-truck to be operated exclusively on its

network. Through its partnership with Navistar, TSP intends to produce a line of purpose-built L4 autonomous

semi-trucks for the North American market at scale by 2024 in Navistar’s manufacturing facilities.

▪ In the first few months of accepting reservations for its L4 autonomous semi-truck, TSP has accepted over 6,775

reservations from approximately ten customers, each of whom has significant freight operations. Approximately

75% of its reservations were made by customers who operate commercial truck fleets and who are also equity

investors in the company. This collaborative approach to create semi-trucks designed and built with integrated

auto-grade components and sensors will increase AFN’s reliability at scale.

Chart 82: TuSimple Has a Adopted a Partnership-Driven Approach to Scaling Its Business Globally

Source: Intro-act, TuSimple (TSP) Investor Presentation

▪ In parallel, TSP has developed a robust ecosystem of shippers, carriers, railroads, freight brokers, fleet asset

owners, and third-party service providers, including UPS, McLane, U.S. Xpress, Werner, Schneider, and CN, that

provide critical validation and enhance the network effect benefits of TSP’s approach. The continued growth its

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AFN infrastructure and partnerships will continue to improve TSP’s user experience and drive more users to its

platform which will allow the company to further densify its strategic terminal network and reinforce rapid

network growth.

▪ In April 2021, TuSimple (TSP) listed on Nasdaq to become the first publicly listed autonomous trucking pure play in the

market. On April 19, 2021, the company closed its initial public offering (IPO) and concurrent private placement, in which it

issued and sold 27,027,027 shares and 874,999 shares, respectively, of its authorized Class A common stock at $40.00 per

share, resulting in net proceeds of $1.0 billion after deducting underwriting discounts and commissions of $50.1 million and

offering costs. The company expects to use the net proceeds from the IPO for working capital and other general corporate

purposes, including funding its operating needs through the commercial launch of its products in 2024, as well as acquire

or invest in complementary products, technologies, or businesses.

▪ In the latest quarter (2Q21), TSP reported multiple milestones, including 1) opening of a new Dallas-Fort Worth

facility to meet growing demand, 2) partnership with Ryder to scale its Autonomous Freight Network (AFN), 3)

partnership with Geotab to conduct industry-first telematics study on AV trucking safety performance, 4)

achievement of more than 4.5 million cumulative road miles. This, coupled with the company’s industry leading

technology and global commercialization plans, make TuSimple (TSP) one of the key autonomous trucking names

for investors to monitor.

Chart 83: TuSimple – Income Statement

Source: Intro-act, TuSimple (TSP) S1 Filing

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Aurora (NASDAQ: RTPY)

▪ Founded in 2017 by self-driving luminaries Sterling Anderson, Drew Bagnell, and Chris Urmson, Aurora is focused on

delivering the benefits of self-driving technology safely, quickly, and broadly. The company is led by a management team

with deep technical and industry experience, including Co-Founder and CEO Chris Urmson who led Google’s self-driving car

team for 7 years, Co-Founder and Chief Product Officer Sterling Anderson who launched Tesla’s Model X, and Co-Founder

and Chief Scientist Drew Bagnell who is a leading machine learning expert. This visionary founding team is backed by ~ 1,600

Aurora employees – 1,400+ in product and engineering with 175+ PhDs – that are developing next-generation technology

and building the business to bring it to market at scale.

▪ In addition to its world class team, Aurora has three differentiators that make it a formidable player in the self-driving

technology market:

▪ Next-generation technology built for scale

▪ Partnerships with industry leaders

▪ Rapid market entry sequence

▪ Next-generation technology built for scale. The Aurora Driver is being developed as an L4 autonomous driver system that’s

designed to power multiple vehicle types, from passenger sedans to Class 8 trucks, to move safely and efficiently through

the world without a human driver. Aurora’s industry-defining technology was developed to accelerate progress toward the

wide-scale deployment of the Aurora Driver. Aurora’s technical investments across the self-driving stack are extensive, and

include, for example, its holistically designed and deeply integrated hardware and software, including its long-range, multi-

modal sensing suite with FirstLight Lidar, which provides differentiated performance (distance, interference immunity,

simultaneous range and velocity) and unlocks high-speed driving, which is key to deploy first in trucking. Other examples

include its robust Simulation technology that creates a paradigm shift in testing safety, efficiency, and speed. And its HD

mapping system, the Aurora Atlas that enables rapid updates and scalability.

Chart 84: The Aurora Driver is a Common Platform Across Transportation Verticals

Source: Intro-act, Aurora Investor Presentation

▪ Partnerships with industry leaders across the mobility spectrum are another key differentiator for Aurora. Aurora’s truck

manufacturing partners, Volvo Group (which includes Volvo Autonomous Solutions) and PACCAR (which includes the

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Peterbilt and Kenworth brands) collectively represent approximately 50 percent of the Class 8 trucks sold in the U.S. market.

As long-term committed partners, Volvo and PACCAR will help accelerate the development, validation, and deployment of

self-driving trucks. Aurora is also expected to scale rapidly in passenger mobility with the support of Toyota, the world’s #1

OEM supplier, and Uber, the largest ride-hailing network globally by market-cap. The company’s disruptive technology

delivers multiple tangible benefits to its industry partners. These benefits include 1) speeding up service and supply chains,

2) alleviating driver shortage, 3) increasing safety, 4) improving energy efficiency, and 5) optimizing vehicle utilization and

design. As further validation of the company’s strong value proposition, the Volvo Group, PACCAR, and Uber are also

committed investors in the PIPE associated with Aurora’s merger with Reinvent Technology Partners Y (discussed below.)

Chart 85: Aurora’s Partnerships Will Accelerate the Commercialization of the Aurora Driver

Source: Intro-act, Aurora Investor Presentation

▪ Aurora has a strong commercialization roadmap and is expected to launch first in the trucking segment in late 2023 and

expand to the last-mile delivery and ride-hailing segments in future years. Delivered as a service, and built for scale, the

Aurora Driver is positioned to address the industry’s most compelling opportunities in the estimated $9.4 trillion global

market that includes trucking, passenger mobility, and local goods delivery. Aurora expects to launch first in trucking, a $700

billion market with attractive unit economics, in late 2023. Leveraging the self-driving capabilities matured in trucking,

Aurora is expecting to rapidly expand into adjacent verticals including last-mile delivery and ride-hailing. The common core

of the Aurora Driver facilitates efficient development and rapid adaptation to trucking and ride-hailing as it requires only

minor adaptations for different vehicles and use cases.

▪ The development, launch, and scale of the Aurora Driver is expected to happen in five phases (below), and expand across

the continental U.S. over eight years:

▪ Phase 1. Lay the foundation (2017-2020).

▪ Phase 2. Develop and refine (2021-2022).

▪ Phase 3. Validate (Truck: 2022-2023; Rides: 2023-2024).

▪ Phase 4. Launch (Truck: 2023-2024; Rides: 2024-2025).

▪ Phase 5. Expand (Truck: 2024+; Rides: 2025+).

▪ In a major step toward commercializing its self-driving technology, Aurora announced that it will become a public

company by merging with Reinvent Technology Partners Y (NASDAQ: RTPY) in a deal that values Aurora at $11 billion

and will leave the company with $2.5 billion in cash at closing. Reinvent Technology Partners Y is a special purpose

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acquisition company led by Mark Pincus, Michael Thompson, and Reid Hoffman and takes a “venture capital at scale”

approach to investing. Investors and Aurora partners have committed $1 billion in a PIPE and the proposed transaction

represents an equity value of $11 billion for Aurora. Investors in the PIPE include Baillie Gifford, funds and accounts managed

by Counterpoint Global (Morgan Stanley), funds and accounts advised by T. Rowe Price Associates, Inc., PRIMECAP

Management Company, Reinvent Capital, XN, Fidelity Management and Research LLC, Canada Pension Plan Investment

Board, Index Ventures, and Sequoia Capital, as well as strategic investments from Uber, PACCAR, and Volvo Group. Existing

Aurora stockholders are expected to own approximately 84% of the pro forma combined company following the close of

the proposed transaction. The pro forma implied market capitalization of the combined company is $13 billion, at the $10.00

per share PIPE subscription price and assuming no public shareholders of Reinvent exercise their redemption rights.

▪ The proposed transaction is expected to close in the second half of 2021, subject to the satisfaction of customary

closing conditions, including the approval of shareholders of Reinvent and the stockholders of Aurora. Upon

closing of the proposed transaction, the combined company will be named Aurora Innovation, Inc. and be publicly

traded, with its common stock expected to be listed on Nasdaq with the ticker symbol AUR.

▪ The combined company is expected to have approximately $2.5 billion in cash at closing, and Aurora intends to fund its

development operations with proceeds from this transaction. The company expects to use this $2.5 billion to fund product

development and deployment of the Aurora Driver through launch, and generate partner development revenue and pre-

commercialization revenue before widespread launch. Aurora anticipates beginning to generate revenue from trucks

without vehicle operators in late 2023, with a small fleet of 20 trucks owned and operated by Aurora. In the medium to long

term, Aurora will deploy is capital-efficient driver-as-a-service model to scale its revenue and expand gross margins. The

company anticipates a break-even in 2027 and massive growth potential in market penetration in both trucking and

passenger mobility markets post-2027.

Chart 86: Aurora – Financial Projections

Source: Intro-act, Aurora Investor Presentation. Fiscal Year End December 31.

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Locomation

▪ Locomation is a Pittsburgh-based startup that is developing an autonomous driving technology platform to offer human-

guided autonomous truck convoying. The company was founded in 2018 by veterans from Carnegie Mellon’s National

Robotics Engineering Center, as well as trucking industry leaders with extensive knowledge and experience in all aspects of

the trucking industry. Locomation aims to be a pioneer in truck convoying by combining human-guided autonomous relay

convoys with custom freight network optimization services.

▪ The company’s first product, Autonomous Relay Convoy (ARC), is based on this platooning model, which involves two

trucks – a leader and a follower – and two drivers. Both trucks are equipped with Locomation's autonomous system, which

provides the network and scheduling algorithms that allow both trucks to start at the same time and place and head in the

same direction. One driver pilots the leading truck while the second driver rests in the other truck, which follows the leading

truck autonomously. Radar and other sensors (cameras, LiDARs, etc.) installed on both trucks ensure that they are at a right

distance apart, typically 50 ft. to 80 ft between each other and connected vehicle technology ensures that the follower

truck, travelling without driver guidance, brakes at the same time as the leader truck. When the driver of the leading vehicle

has driven for 11 hours – the maximum amount of time permitted by the U.S. Department of Transportation – or simply

wants to take a break, the trucks swap places to allow each driver to take turns leading the convoy and resting at regular

intervals. This enables the two trucks to travel 20+ hours a day, rather than 11 hours a day for one truck and one driver.

▪ In August 2020, Locomation successfully completed its first on-road pilot transporting commercial freight in

collaboration with Missouri-based transportation logistics company Wilson Logistics and risk management consultancy

Aon. The deployment involved two Locomation trucks hauling Wilson’ trailers and freight on a 420-mile-long route

stretching from Portland, OR to Nampa, ID along I-84 – one of the most challenging road conditions in terms of

curvatures, grades, and wind gusts. Per Aon’s safety assessment, risk factors related to accident and loss were

significantly lower, demonstrating that the Locomation’s autonomous technology can be seamlessly integrated and

deployed in a real trucking operation on a long-term basis.

Chart 87: Locomation’s ARC System Deployment and Operation

Source: Intro-act, Locomation

▪ Locomation’s ARC system offers truck carriers improved efficiency, better operating margins, higher asset utilization, and

lower fuel costs. Using the ARC system, trucking companies can safely operate two trucks 20-22 hours a day while shipping

the cargo two times more, two times faster, and two times farther. Carriers can also choose routes with the company’s

freight network analysis and custom optimization services that best match their customers’ demands while maximizing the

operational efficiency provided by autonomy. At full commercialization, which Locomation hopes to achieve by 2022, the

ARC system can provide carriers with a 30% boost in their operating margins through a combination of enhanced asset

utilization of up to 130% and a reduction in expenses of up to 8% owing to decreased fuel consumption that can remove

over 40 metric tons of carbon dioxide from the air per convoy annually. These improvements could result in margin growth

of 15%-17% for trucking companies, including the cost of Locomation’s solution.

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▪ While Locomation’s system is not entirely autonomous yet, the company is building a portfolio of autonomous truck

technologies to achieve full automation in four phases. The first phase involves commercializing the ARC system in 2022.

The second phase, known as the ‘drone follower’ system, is intended for shorter haul routes of <250 miles and is expected

to be launched in 2023. This system will consist of one driver and two trucks, one in the lead and one trailing behind

autonomously. The third phase, called the ‘hub-to-hub’ system, will allow trucks to operate without humans between hubs

on an interstate and will be followed by the fourth phase, the ‘dock-to-dock’ system, which will include non-interstate roads.

The company’s efforts to achieve full autonomy are also evident through its collaboration with a premier testing, research,

and innovation center for the automotive industry – Transportation Research Center (TRC) in East Liberty, OH. Locomation

will actively test at TRC to advance its autonomous truck platforms alongside TRC’s extensive research and development

team in order to drive further innovation on the testing and validation of the company’s autonomy technology.

Chart 88: Locomation Aims to Achieve Complete Autonomous Trucking in Four Phases

Source: Intro-act, Locomation

▪ Locomation boasts of the world’s first commercial autonomous truck order from Wilson Logistics and has also garnered

the attention of other trucking industry leaders like PGT Trucking. In September 2020, Locomation announced its first fleet

purchase order for autonomous technology from Wilson Logistics. Under the terms of the deal, a minimum of 1,120 Wilson

Logistics tractors will be equipped with Locomation’s ARC technology, with the first units delivered in early 2022. In addition,

the deal amends the commercial agreement between the two companies to extend through 2028. Then in June 2021,

Locomation announced an eight-year agreement with the leading flatbed transportation provider PGT Trucking to deploy

1,000 ARC systems on over 30 separate ARC segments, aiming to improve PGT Trucking’s operational efficiency, reduce

costs and carbon footprint while increasing overall safety.

▪ In addition to signing commercial contracts, Locomation has received $60+ million in funding over four rounds thus far,

according to Crunchbase. When combined, these agreements are worth hundreds of millions of dollars. It is estimated that

Wilson Logistics and PGT Trucking operating in ARC segments will potentially generate $1 billion+ annually in new freight

transportation revenue at a 30% lower cost, further contributing to Locomation’s topline and making it one of the key AT

names for investors to monitor.

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Waymo (NASDAQ: GOOGL)

▪ Waymo (NASDAQ: GOOGL) is an autonomous driving technology startup that is building driving technology for use in

large Class 8 trucks under its self-driving truck development division “Waymo Via”, for local and long-haul delivery and

logistics, in addition to operating a commercial self-driving taxi service “Waymo One”. Since its inception in 2009 as the

Google self-driving car project in California, Waymo's autonomous vehicle technology has logged over 20 million miles of

real-world driving and over 15 billion miles of simulated driving as of now. This expertise in testing its technologies on

passenger cars laid the groundwork for the company’s trucking fleet service to solve the unique challenges that large Class

8 trucks experience, including safe turning, braking, and lane changes among others.

▪ Waymo began road testing its autonomous driving technology – known as Waymo Driver – on large Class-8 trucks for

long-haul deliveries in CA and AZ in 2017, and formally launched Waymo Via in March 2020. Waymo Driver is deployed

across Waymo’s different business lines: for ride-hailing service, as well as on all forms of goods delivery, including trucking

and local delivery. This means that the trucking fleet also uses the same autonomous driving system and the same suite of

custom-built sensors (just configured differently) as its passenger fleet Waymo One. Waymo Driver has two components:

hardware and software. On the hardware side, it has sensor suite that comprises LiDAR, cameras, radar, and a powerful AI

compute platform. They collectively provide a 360-degree view of the world. On the software side, Waymo Driver uses all

the data collected by its sensors to answer four important questions: Where is the vehicle? What is going on around the

vehicle? What happens next? How should the vehicle proceed? Together, the hardware and software work to create a

comprehensive image of the environment surrounding the vehicle, allowing it to traverse highways safely. Moreover, to

enable the scaled deployment of the Waymo Driver and to tackle more environments, Waymo launched its 5th generation

Driver in March 2020.

▪ Since Waymo started testing in 2017, its Class 8 autonomous trucks have driven in a wide variety of cities and

environments, from Arizona to Texas and through California and Georgia. Currently, Waymo is testing Waymo Driver-

operated Class 8 Heavy duty trucks in California, Arizona, Texas, and parts of New Mexico, along the I-10 corridor.

Chart 89: Waymo Trucks Feature Multiple LiDAR, Radars, and Cameras to Reduce Blind Spots

Source: Intro-act, Waymo

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▪ Waymo is progressing toward streamlined path to commercialization for autonomously driven trucks by scaling its

operations in multiple states, working closely with top freight partners and truck OEMs, and investing in self-driving

technological innovation.

▪ In August 2021, Waymo announced that it is building a dedicated trucking hub in the Dallas-Fort Worth area as the

company grows its footprint in Texas and continues driving across I-10, I-20, and I-45. Located in South Dallas, this 9-

acre hub will be constructed from the ground up specifically for Waymo Via and its autonomous driving operations. This

hub will not only strengthen Waymo’s operations in Texas, but it will also be well-suited to serve long range routes across

the Southwest and connect with the company’s Phoenix operations center.

▪ At the same time, Waymo also partnered with Ryder for fleet management services, such as fleet maintenance,

inspections, and roadside assistance, across all of Waymo Via hubs and testing sites in Texas, Arizona, California, Ohio,

and Michigan. Ryder brings nearly 90 years of fleet management experience and has a national network of 500+ facilities,

which will be a key component of Waymo’s Driver-as-a-Service (DaaS) model. Waymo will be able to optimize the

performance of its autonomous trucks by combining Ryder’s deep maintenance expertise and its proprietary AV fleet

data to create solutions customized for Waymo’s technology.

▪ In June 2021, Waymo announced partnership with J.B. Hunt to autonomously transport loads in Texas for one of J.B.

Hunt’s top customers. J.B. Hunt is one of the largest, most reputable, and most innovative transportation logistics

companies in the U.S. and is an ideal collaborator to explore how the Waymo Driver can be integrated across fleets and

enhance safety and efficiency. Waymo will deploy its Waymo Driver-assisted Class 8 trucks on one of the most highly

utilized freight corridors in the country, I-45, hauling goods between facilities in Houston and Fort Worth.

▪ In October 2020, Waymo signed a broad, global, strategic partnership to deploy autonomous SAE L4 technology with

Daimler Trucks – one of the world's largest commercial vehicle manufacturers. Their initial effort will combine Waymo's

industry-leading automated driver technology with a unique version of Daimler's Freightliner Cascadia, to enable

autonomous driving and will be available to customers in the U.S. in the coming years.

▪ Further, to deepen its investment in autonomous delivery and freight transportation, Waymo announced opening a

research and development facility for trucking in Menlo Park, CA and collaboration with the Transportation Research

Center in East Liberty, OH, to jointly develop a custom testing environment for its 5th generation Waymo Driver.

▪ Valued at over $30 billion, Waymo has raised over $5 billion+ in investments to date (per Crunchbase) and is backed by

big ticket investors including its parent company Alphabet (NASDAQ: GOOGL), Silver Lake, Andreessen Horowitz, and

Fidelity Management & Research among others. The company’s latest funding round was in June 2021, when it raised $2.5

billion from Alphabet, Andreessen Horowitz, AutoNation, Canada Pension Plan Investment Board, Fidelity Management &

Research, Magna International, Mubadala Investment Company, Perry Creek Capital, and Silver Lake. This funding round

follows Waymo’s first external funding round of $2.25 billion in March 2020, which later increased to $3.2 billion after an

extension of that round in July 2020. Per Pitchbook, Waymo is valued at $30 billion+, excluding its most recent funding

round, which makes the company an attractive large cap AT company for investors to keep an eye on.

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