UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
Washington, D.C. 20549
FORM 10-K
(Mark One)
☒ ANNUAL REPORT PURSUANT TO
SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
For the fiscal year ended December 31,
2021
or
☐ TRANSITION REPORT UNDER
SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
For the transition period from [____] to [____]
Commission file number 001-40932
CYNGN INC.
(Exact name of registrant as specified in its charter)
(Address of principal executive offices) (Zip Code)
Registrant’s Telephone number, including
area code: (650)924-5905
Securities registered pursuant to Section 12(b)
of the Act:
Title of Class Trading Symbol(s) Name of each exchange on which registered
Common Stock, Par Value $0.00001 CYN NASDAQ Stock Market LLC
Securities registered pursuant to Section 12(g)
of the Act:
Title of Each Class Name of Each Exchange On Which Registered
N/A N/A
Indicate by check mark if the registered is a
well-known seasonal issuer, as defined in Rule 405 the Securities Act.
Yes ☐ No ☒
Indicate by check mark if the registrant is not
required to file reports pursuant to Section 13 or Section 15(d) of the Act.
Yes ☐ No ☒
Indicate by check mark whether the registrant:
(1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months
(or for such shorter period that the registrant was required to file such reports) and (2) has been subject to such filing requirements
for the last 90 days.
Yes ☒ No
☐
Indicate by check mark whether the registrant
has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-K (§229.405
of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files).
Yes ☒
No ☐
Indicate by check mark whether the registrant
is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company, or an emerging growth company.
See the definitions of “large accelerated filer,” “accelerated filer,” “smaller reporting company,”
and “emerging growth company” in Rule 12b-2 of the Exchange Act.
Large accelerated filer ☐ Accelerated filer ☐
Non-accelerated filer ☒ Smaller reporting company ☒
Emerging growth company ☒
If an emerging growth company, indicate by a check
mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting
standards provided pursuant to Section 13(a) of the Exchange Act. ☐
Indicate by check mark whether
the registrant has filed a report on and attestation to its management’s assessment of the effectiveness of its internal control
over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm
that prepared or issued its audit report. ☐
Indicate by check mark whether the registrant
is a shell company (as defined in Rule 12b-2 of the Exchange Act). Yes ☐ No ☒
The registrant was not a public company at June 30,
2021, the last business day of the registrant’s most recently completed second fiscal quarter, and therefore it cannot calculate
the aggregate market value of its voting and non-voting common equity held by non-affiliates at such date. The registrants Common Stock
began trading on the Nasdaq Capital Market on October 20, 2021. The aggregate market value of the registrant’s Common Stock outstanding,
other than shares held by persons who may be deemed affiliates of the registrant, at December 31, 2021, computed by reference to the closing
price for the Common Stock on such date ($4.50), as reported on the NASDAQ Capital Market, was $53,013,438.
The number of shares of Common Stock, $0.00001
par value, outstanding on March 24, 2022 was 27,094,430 shares.
DOCUMENTS INCORPORATED BY REFERENCE
None.
TABLE OF CONTENTS
Item 1. Business 1
Item 1A. Risk Factors 15
Item 1B. Unresolved Staff Comments 35
Item 2. Properties 35
Item 3. Legal Proceedings 35
Item 4. Mine Safety Disclosures 35
Item 6. [Reserved] 36
Item 7A. Quantitative and Qualitative Disclosures About Market Risk 41
Item 8. Financial Statements and Supplementary Data F-1
Item 9A. Controls and Procedures 42
Item 9B. Other Information 43
Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections 43
Item 10. Directors, Executive Officers and Corporate Governance 44
Item 11. Executive Compensation 50
Item 14. Principal Accounting Fees and Services 55
Item 15. Exhibits, Financial Statement Schedules 56
i
FORWARD-LOOKING STATEMENTS
This Annual Report on Form
10-K contains forward-looking statements. These statements are based on our management’s beliefs and assumptions and on information
currently available to our management. The forward-looking statements are contained principally under the headings “Risk Factors,”
“Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and “Business.”
Forward-looking statements include statements concerning:
● our possible or assumed future results of operations;
● our business strategies;
● our ability to attract and retain customers;
● our ability to sell additional products and services to customers;
● our cash needs and financing plans;
● our competitive position;
● our industry environment;
● our potential growth opportunities;
● the effects of future regulation; and
● the effects of competition.
All statements in this Annual
Report that are not historical facts are forward-looking statements. We may, in some cases, use terms such as “anticipates,”
“believes,” “could,” “estimates,” “expects,” “intends,” “may,”
“plans,” “potential,” “predicts,” “projects,” “should,” “will,”
“would” or similar expressions that convey uncertainty of future events or outcomes to identify forward-looking statements.
The outcome of the events
described in these forward-looking statements are subject to known and unknown risks, uncertainties and other factors that may cause
our actual results, performance or achievements to be materially different from any future results, performances or achievements expressed
or implied by the forward-looking statements. These important factors include our financial performance and the other important factors
we discuss in greater detail in “Risk Factors.” You should read these factors and the other cautionary statements made in
this Annual Report as applying to all related forward-looking statements wherever they appear in this Annual Report. Given these
factors, you should not place undue reliance on these forward-looking statements. Also, forward-looking statements represent
our management’s beliefs and assumptions only as of the date on which the statements are made. We undertake no obligation to publicly
update any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by
law. Given these risks and uncertainties, readers are cautioned not to place undue reliance on such forward-looking statements.
Unless the context requires otherwise, references in this Annual
Report on Form 10-K to “we,” “us,” and “our” refer to Cyngn Inc. and its consolidated subsidiaries.
ii
PART 1
Item 1. Business
General
Research of the industrial
vehicle market (utility vehicles, trucks, and tractors utilized heavily in the manufacturing, distribution and logistics, mining, and
construction industries) projects the demand of such vehicles to reach as high as $80 billion by 2023, according to findings from the
“Global Material Handling Equipment” report by Freedonia Focus Reports. This growth will be propelled by the projected
rise in global manufacturing, construction, and e-commerce activity. Yet, according to the “Trends in Supporting and Scaling
Modern Automation” report by Ricoh & ABI Research Report, historically, less than 1% of industrial vehicle equipment shipped
by top manufacturers has been automated. Despite these low penetration rates, the benefits of industrial vehicle automation can produce
operational efficiency gains of upwards of 50%, according to the “Industry 4.0: Reimagining manufacturing operations after COVID
19” article by McKinsey & Company. As automation proliferates, these industries will gradually shift to service-based models
that will decrease upfront capital expenditures and create new revenue streams while unlocking new value in the supply chain. Our autonomous
vehicle (AV)technology is uniquely positioned to capitalize upon these changes by offering a heterogeneous autonomy solution that can
deliver self-driving capabilities and data insights to nearly every industrial vehicle on the market.
We integrate our full-stack autonomous driving software, DriveMod,
onto vehicles manufactured by Original Equipment Manufacturer (OEM) customers either via retrofit of existing vehicles or by integration
directly into vehicle assembly. We design the Enterprise Autonomy Suite (“EAS”) to be compatible with sensors and components
from leading hardware technology providers and integrate our proprietary AV software to produce differentiated autonomous vehicles.
Autonomous driving has common
technological building blocks that remain similar across vehicles and applications. By tapping into these building blocks, DriveMod is
designed to deliver autonomy to new vehicles via streamlined hardware/software integration. This vehicle-agnostic approach enables
DriveMod to expand to new vehicles and novel operational design domains (ODD). In short, almost every industrial vehicle, regardless of
use case, can move autonomously using our technology.
Our approach accomplishes
several primary value propositions:
We believe our market positioning
as a technology partner to vehicle manufacturers creates a synergy with incumbent suppliers that already have established sales, distribution,
and service/maintenance channels. By focusing on industrial use cases and partnering with the incumbent OEMs in these spaces, we believe
we can source and execute revenue-generating opportunities more quickly.
Our long-term vision
is for EAS to become a universal autonomous driving solution with minimal marginal cost for companies to adopt new vehicles and expand
their autonomous fleets across new deployments. We have already deployed DriveMod software on nine different vehicle form factors that
range from stockchasers and stand-on floor scrubbers to 14-seat shuttles and 5-meter-long cargo vehicles as part of prototypes
and proof of concept projects, demonstrating the extensibility of our AV building blocks.
1
Our recent progress contributes
to the validation of EAS with OEM partners and end customers. We also continue to build upon our ability to scale our products and generate
novel technological developments. See figure below for recent highlights:
Figure 0: Summary of recent
Cyngn milestones
We believe that the adaptability
of our technology will enable us to incrementally expand into new AV verticals and grow our total addressable market (TAM) from the billions
of dollars we are currently targeting in the industrial markets to the trillions of dollars that self-driving vehicles can capture
across all industries.
We believe that the ubiquity
of our technology will combine with our deep AV experience and enable us to incrementally expand into new AV verticals. Thus, we could
grow our TAM from the billions of dollars we are currently targeting in the commercial and industrial markets to the trillions of dollars
that self-driving vehicles can capture across industries (Source: Ark Invest. “Mobility-As-A-Service: Why Self-Driving Cars
Could Change Everything.”).
Automation and Autonomy in the Industrial Equipment
Market
Overview
Automation has long played
a role in industrial sectors. More recently, the larger industrial automation market has grown significantly by riding the wave of new
technology and innovation focused on addressing the needs of what is known as Industry 4.0, the outcome of the fourth
industrial revolution (Source: https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html). According to the “Industrial
Automation Market by Component, Mode of Operation, and End User” report by Meticulous Market Research Pvt. Ltd., in 2020, the industrial
automation industry was valued at US$164.2 billion and is expected to grow at 9.3% compound annual growth rate (CAGR), ultimately
reaching a market value of US$306.2 billion by 2027. Autonomous vehicles represent fundamental technology that will enable
the fourth industrial revolution.
2
Figure 1: Illustration of the progression from
Industry 1.0 to Industry 4.0.
Industrial automation is broadly
understood to consist of a wide range of technology solutions that provide varying levels of automation for critical software control
systems and industrial equipment. These components are essential to the operations and growth of global markets such as manufacturing,
distribution, transportation, construction, and mining. The relatively controlled and pre-defined operational environments industrial
companies operate in are what make them such a strong opportunity for companies looking to develop automation solutions, but enhanced
product capabilities will be required to achieve the promise of Industry 4.0 — capabilities that will be created by technological
advancements in AI/ML, robotics, connectivity, mapping, and interoperability.
Automation Solutions for Industrial Equipment
The Industrial Equipment market
covers a broad range of use cases and product categories, with automation solutions targeting Material Transport Equipment (MTE) heavily
utilized by the majority of industry market sectors. For our purposes, we can think of MTE to include all material handling equipment
directly related to material transit (this includes conveying equipment, monorail, hoists, storage & retrieval, and industrial vehicles).
According to the “Global Material Handling Equipment’ report by Freedonia Focus Reports, the MTE market is characterized by
steady growth, and the increase in global e-commerce activity and industry mechanization over the past decade are projected to grow
the industry to $160 billion in 2023, representing a 3.9% 5-year CAGR. Our belief is that these strong growth indicators
will drive increased need for more advanced technology that will address gaps in the current capabilities of automated MTE solutions.
Historically, MTE automation
has been heavily weighted in solutions related to storage/retrieval systems and conveyors because more rigid and repetitive environments
are better suited for the limited capability of existing automation solutions. By contrast, the vehicles in the MTE category, referred
to here as industrial vehicles, are largely still driven manually. As an example, automated guided vehicles (AGVs) and autonomous
mobile robots (AMRs) have illustrated the applicability of automated industrial vehicles for the manufacturing and distribution industries,
yet adoption rates of these technologies are lagging Industry 4.0 growth rates by as much as 4.5%, according to the “Global Material
Handling Equipment’ report by Freedonia Focus Reports.
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Analysis in the “Trends
in Supporting and Scaling Modern Automation” report by Ricoh & ABI Research Report estimates that the top 10 manufacturers of
industrial vehicles for manufacturing and distribution shipped approximately 883,000 units in 2019. However, fewer than 1% of material
handling vehicles shipped every year are automated, presenting a significant opportunity to automate industrial vehicles. The cost to
operate a non-autonomous material transport vehicle is reported to be $32.42 per hour, according to the U.S. Bureau of Labor Statistics
compensation data for transportation and material moving full-time employees. Further, it is estimated that each non-autonomous vehicle
is in operation for approximately 4,174 hours per year based on 16-hour per workday operation. The labor costs associated with humans
operating 883,000 non-autonomous vehicles for manufacturing and distribution yield our current market potential of $119B. The ability
to deliver more consistent operations, reduce accidents, and mitigate personnel issues like attrition and truancy through the use of AVs
could create additional market opportunity. Other relevant statistics we consider when evaluating our market potential are that there
are an estimated 20,000 warehouses in the US (50,000 globally) with an average of 175 employees per warehouse, according to Statista,
and there are an estimated 900,000 employees moving material within these US warehouses, according to “Industries at a Glance: Warehousing
and Storage: NAICS 493.” We believe that technological innovation is needed to enable adoption of autonomous MTE that will address
the substantial industry challenges that exist today.
These challenges include:
Labor
shortages — The hiring and retention of qualified workers is a critical concern for the markets that material transport
vehicles operate within. In fact, Deloitte’s 2020 and 2021 Material Handling Industry Report showed that over 50% of the 1,000
supply chain and manufacturing leaders surveyed rated hiring and employee retention as their biggest challenge (source: MHI Deloitte
Industry Report). Additionally, in a survey completed in late 2019 prior to the start of the COVID 19 pandemic, 73% of survey respondents
indicated that it takes more than 30 days for their companies to fill open positions. By 2030, the impact of unfilled open jobs
in manufacturing could cost the US economy more than $1 trillion. Compounding the issue further is the industry’s projected increase
in labor demand and cost. According to IBISWorld’s 2020 US Industrial Machinery & Equipment Industry Report, US industry labor
needs are expected to increase at an annualized rate of 3.7% to over 375,000 workers through 2025, driven by higher US industrial and
manufacturing activity. Average cost of manufacturing labor in the US has also increased by 20% since 2010 according to McKinsey.
Difficulty in scaling — The
traditional approaches to vehicle automation make scaling vehicle automation solutions difficult due to strains caused by service lifecycle
management and issues with dynamic deployability. Industrial automation customers are forced to coordinate operational components from
a variety of different vendors and lack a unifying architecture that allows the technology to scale effectively within and across sites.
Significant costs are also associated with expanding the scope of existing automation solutions as they are tightly coupled to specific
vehicles and often require an overhaul of the site infrastructure to overcome shortcomings in the automation technology. This can be especially
true in niche environments like mines, where the deployability challenges are compounded by unique sites that require heterogeneous fleets.
Furthermore, customer service, workforce training, and repair fall under service lifecycle management and must be taken into account along
with the technology in order to scale efficiently, according to the “Trends in Supporting and Scaling Modern Automation” report
by Ricoh & ABI Research Report.
Lagging technological advancement —
Manufacturers of material transport vehicles have core competencies in mechanical, electrical, and control systems while the end users
of the vehicles typically specialize in logistics, manufacturing, and material moving. There is limited expertise throughout the material
handling value chain in software algorithms, sensing, and high-performance computing. Considering the incumbents’ gaps in leading-edge AV
and AI technologies and the pressure existing suppliers face to ship manually-operated vehicles that address the multi-billion dollar
demand that already exists, we believe it is unlikely that existing stakeholders will be able to invest in the technological advancements
that will solve the industry’s fundamental challenges. Deloitte’s 2020 Material Handling Industry Report indicates that, through
2023, only 42% of survey respondents would invest in automation equipment at all, with just 20% reporting that they would invest in either
AVs or predictive analytics.
High barriers to adoption —
Many solutions for automated material transport require an all-or-nothing commitment from customers: either make a major upfront
investment to overhaul operations for automation, or postpone automation at the risk of falling behind competition. This all-or-nothing approach
to unlocking future return on investment (ROI) can be problematic for risk-averse companies that seek to adopt automation solutions.
Depending on fleet size, traditional automation solutions such as “robot-in-a-box” may command ROI horizons of up to 4 years.
Factoring in ancillary costs like installation, maintenance, on-site testing, integration, and deployment, can also represent a significant
annual cost burden, according to findings by Richoh & ABI Research Report. According to a PwC 2016 Digital Operations survey,
“cost advantage” was the most prevalent prompt cited (86% of responses) for adopting advanced industrial mobility technologies,
but in conjunction, “costs are prohibitive” was the most prevalent barrier (58% of responses) to adoption of semi-autonomous/autonomous
vehicles.
4
To combat these challenges,
we have built an Enterprise Autonomy Suite for industrial vehicles that leverages advanced in-vehicle autonomous driving technology
and incorporates leading supporting technologies like data analytics, fleet management, cloud, and connectivity. EAS provides a differentiated
solution that we believe will drive pervasive adoption of industrial autonomy and create value for customers at every stage of their automation
growth.
The Enterprise Autonomy Suite for Industrial
Vehicles
Our unique value proposition
stems from the concept that the growth of industrial autonomy requires an approach that deploys applied AV solutions within a system of
supportive resources rather than a technology feature that is tuned to a specific industrial vehicle.
Some companies manufacture
standard industrial vehicles then integrate industrial automation software for rigid tasks. Others develop new vehicle platforms to enable
more advanced automation capabilities, limiting the AV technology to a narrow use case. We develop an advanced autonomous vehicle
software, DriveMod, for industrial vehicles. DriveMod is a component of EAS that is operationally expansive, vehicle agnostic, and compatible
with indoor and outdoor environments. EAS centers around DriveMod’s on-vehicle AV software and is supported by our Cyngn Insight
and Cyngn Evolve technology and tools.
Figure 2: The core components that make up
our EAS product offering.
EAS is currently available as a private beta release to select customers.
Our approach drives value at every stage of
a company’s autonomy journey
EAS provides extensible industrial
autonomy solutions that can include data-driven actionable insights, partial autonomy to augment existing workflows and support human
drivers, and fully autonomous vehicle mobility. By offering flexible data and autonomous services through subscription-based business
models, we assuage the industry’s existing challenge of all-or-nothing adoption for autonomous vehicles. Installing DriveMod
onto any vehicle unlocks a collection of valuable product offerings that customers can activate over the air, creating lower barriers
to entry and enabling customers to benefit from novel data insights while adopting industrial AVs at their own pace.
5
EAS galvanizes the relationship between AVs
and data
Our EAS combines core autonomous
vehicle technology with a suite of tools and products that strengthen the ties between industrial business operations and the positive
network effects that underpin the relationship between data and AVs. DriveMod uses data from advanced sensors to navigate AVs, creating
a de facto mechanism for rich data collection. Vehicles equipped with DriveMod provide the means for us to collect data then organize,
analyze, and expose customers to novel insights. This makes the data collected during vehicle operation a new type of asset that adopters
of AV technology can take advantage of. Data can be stored in cloud or on-premises servers, according to customer requirements. We
intend to have our customers own the data collected at their facilities and for Cyngn to have the rights to use that data for certain
purposes, such as testing simulation and development. These data assets present a new opportunity to reveal previously unknown insights
about day-to-day operational processes that impact safety, efficiency, vehicle maintenance, and growth.
Figure 3: The EAS product flywheel
As the deployment of industrial
vehicles with DriveMod scales up, the amount and diversity of data flowing through Cyngn Insight expands, creating an accelerated feedback
loop and powers our ability to use Cyngn Evolve to further enhance DriveMod, and update the on-vehicle software over-the-air.
Continual Improvement Drives Technology Advancement
DriveMod’s building
blocks enable a more consistent cadence of upgrades, improvements, and customer-specific feature development that can be deployed
via over-the-air updates. These capabilities ensure that the deployed system stays in sync with the changing application demands
while allowing customers to focus on monetary and operational ROI. Our EAS plugs into business operations by creating and collecting real-time data
and aggregating it into configurable analytics dashboards that inform customer operations as well as future DriveMod releases, creating
a data set specific to each customer from high-resolution data collected during their operations.
6
Our Approach Augments and Upskills Workforces
Industrial vehicle autonomy
represents an opportunity to minimize the adverse impact that talent shortages, employee health, and safety have on a company’s
core operations. Autonomous vehicles can be relied upon to fill the voids that commonly create human resource issues like executing repetitive
tasks, working during undesirable hours, and operating in uncomfortable or hazardous environments. One Deloitte case study inspected a
parts manufacturing and fulfillment facility that utilized Autonomous Mobile Robots (“AMRs”) to pick products from the back
of their expansive distribution center. Introducing the AMRs saved employee time, provided respite from unstimulating tasks, and improved
both morale and productivity overall (Source: Deloitte industry report, 2020).
Furthermore, existing employees
can be exposed to cutting-edge technology and develop new valuable career development opportunities. For instance, a manufacturing
community in Wisconsin successfully retrained their employees to be skilled in AMR maintenance after AMRs were introduced to replace traditional
conveyors, according to the article “Are Autonomous Mobile Robots at the Tipping Point” by AutomationWorld.
We Designed for Scale
EAS provides a powerful solution
to scalability issues, especially for dynamic deployability and service lifecycle management. DriveMod’s modular capability to deploy
AV technology on diverse vehicle fleets has been proven through its deployment on nine different vehicle form factors that we have operated
autonomously. These vehicles were deployed as prototypes or as a part of proof-of-concept project. Of these deployments, two were
at customer sites. For one deployment we were paid $166,000 and the other was part of our normal R&D activities. Our AV development
and testing have included road vehicles that navigate complex dynamic environments. DriveMod is capable of perceiving more than 100 dynamic
objects per second and then using that perception information to navigate autonomously. This capability has been proven via road testing
in difficult driving settings like urban streets. In contrast, the industrial settings of our target market rarely encounter 100 dynamic
actors per minute, let alone per second. Scalability is further strengthened by EAS creating common interfaces and experiences that unify
customer data and AV operations within and across sites. Thus, proliferating our solutions with customers will be achieved by iteratively
adding onto an existing EAS, which minimizes the marginal cost associated with expanding AV operations. Additionally, the deployment of
EAS allows forall of the on-going administration, services, and vendors associated with managing the lifecycle of the
system to be integrated.
Figure 4: Illustration of DriveMod’s
ability to utilize key subsystems across multiple environments and vehicle platforms
(left: off-road utility vehicle; right: indoor material
handling vehicle).
7
Our Products
EAS is a suite of technology
and tools that we divide into three complementary categories: DriveMod, Cyngn Insight, and Cyngn Evolve.
DriveMod: Industrial Autonomous Vehicle System
We built DriveMod as a modular
software product that is compatible with various sensor and computer hardware components that are widely used throughout the autonomous
vehicle industry. Our software combined with sensors and components from industry leading technology providers covers the end-to-end requirements
that enable vehicles to operate autonomously with leading-edge technology. The modularity of DriveMod allows our AV technology to
be compatible across vehicle platforms as well as indoor and outdoor environments. DriveMod can be retrofitted to existing vehicle assets
or integrated into a manufacturing partner’s vehicles at assembly, providing accessible options for our customers to integrate leading-edge technology
whether their AV adoption strategies are evolutionary or revolutionary.
Figure 5: The major subsystems that make up
Cyngn’s autonomous vehicle technology (DriveMod)
DriveMod’s flexibility
combines with our network of manufacturing and service partners to support customers at different stages of autonomous technology integration.
This allows customers to grow the complexity and scope of their industrial autonomy deployments as their business transforms while continually
capturing returns throughout their transition to full autonomy. EAS will also grant customers access to over-the-air software upgrades,
ad hoc customer support, and flexible consumption based on usage and scale of operations. By lessening both the commercial and technical
burdens of traditional vehicle automation and industrial robotics investments, industrial AVs can become universally available to the
market, even reaching small and medium-sized businesses that may otherwise struggle to adopt Industry 4.0 technology.
Cyngn Insight: Intelligent Control Center
Cyngn Insight is the customer-facing tool
suite for managing AV fleets and aggregating data to extract business insights. Analytics dashboards surface data about the system’s
status, vehicle telemetry, and performance metrics. Cyngn Insight also provides tools to switch between autonomous, manual, and remote
operation when required. This flexibility allows customers to use the autonomous capabilities of the system in a way that is tailored
to their own operational environment. Customers can choose when to operate their DriveMod-powered vehicles autonomously and when
to have human operators operate the vehicles manually or remotely based on their own business needs. When combined, these capabilities
and tools make up the Cyngn Insight intelligent control center that enables flexible fleet management from any location.
8
Figure 6: An operator uses the Cyngn Insight
control center to operate vehicles remotely
Cyngn Insight’s tool
suite includes configurable cloud dashboards that aggregate diverse data streams at several levels of granularity (i.e., site, fleet,
vehicle, module, and component). We can collect data during “open loop” vehicle operation, meaning that the vehicles can be
operated manually while still collecting the rich data enabled by the advanced on-vehicle sensors and computers. This data can be
used for predictive maintenance, operational improvements, educating employees on digital transformation and more. For example, use
cases for performance management analytics driven by automation have experienced productivity increases of 20 – 70% according to
studies by Deloitte and McKinsey (source: MHI Deloitte Industry report: Industry 4.0: Reimagining Manufacturing Operations after COVID
19).
Cyngn Evolve: Data Optimization Tools
Cyngn Evolve is our internal
tool suite that underpins the relationship between AVs and data. Through a unifying cloud-based data infrastructure, our proprietary
data tools strengthen the positive network effects derived from the valuable new data created by AVs. Cyngn Evolve and its data pipelines
facilitate AI/ML training and deployment, manage data sets, and support driving simulation and grading to test and validate new DriveMod
releases, using both real-world and simulated data.
Figure 7: The Cyngn “AnyDrive”
simulation is part of the Cyngn Evolve toolchain. The simulation environment creates a digital version of the physical world. This allows
for customer data sets to be leveraged and augmented to achieve testing and validation prior to releasing new AV features.
9
As AV technology expertise
matures globally, there may be opportunities to monetize the sophisticated AV-centric tools of Cyngn Evolve. Currently, we believe
that AV development is confined to small groups of experts. Therefore, Cyngn Evolve is currently an internal EAS tool that we use to advance
DriveMod and Cyngn Insight, our customer-facing EAS products.
Our Strategy
360° Sales and Marketing
We are building a go-to-market ecosystem
that we believe will be highly leveraged by using our partners as the foundation of our growth strategy. We will utilize these relationships
to generate and cultivate customer demand, acquire new customers, and deliver additional services to our customers.
Key elements of our market
entry and expansion roadmap include:
Focus: manufacturing and distribution
material handling vehicles
Manufacturing requires the
shortest timeline for deployment and the market is expected to account for 52% of new material handling machinery demand by 2023, according
to findings by Freedonia Focus Reports. We have already deployed DriveMod on multiple vehicles that are commonly used in these applications.
These vehicles were deployed as prototypes or as a part of proof-of-concept project. Of these deployments, two were at customer sites
and of which, one was paid.
Broaden: address other industrial
vehicle use cases
The industries that utilize
material transport vehicles share trends, challenges, and opportunities. DriveMod has been architected to be vehicle agnostic and allow
for efficient expansion to industries such as mining, construction, yard operations, and agriculture.
Expand: develop autonomous vehicle
technologies across other sectors
According to a CB Insights
report, “33 Industries Other Than Auto That Driverless Cars Could Turn Upside Down,” autonomous vehicle technology brings
value to at least 33 industries. Because our core autonomous technology is universal, the Company has an opportunity to generate revenue
across a variety of industries. We believe that developing the sales and marketing infrastructure to access these markets is an essential
aspect of driving growth in these areas.
Revenue Sources
Although, we currently have
no paying customers, we anticipate that our technology will generate revenue through two main methods: deployment and EAS subscriptions.
Deployment
Deploying our EAS requires
us and our integration partners to work with a new client to map the job site, gather data, and install our AV technology within their
fleet and site. New deployments yield project-based revenues that are assessed based on the scope of the deployment. Our major collaborators
in this area are our OEM partners, and we reinforce our deployment capability with integration and services experts like Formel D. Formel
D is a globally active service provider to the automotive industry and to manufacturing supply chains. Working directly with our OEM partners
as well as with third party experts ensures that we can deploy our technology globally and at scale. These collaborative partnerships
are established through mutually beneficial, non-binding memorandums of understanding or partnering agreements for the purpose of
joint go-to-market efforts.
10
EAS Subscription
According to ABI Research,
the cloud robotics opportunity will grow from $3.3 billion in 2019 to $157.8 billion in 2030, accounting for 30% of the robotic
industry’s total worth (source: Cloud Robotics Market Predicted to Grow to $157.8 billion by 2030, article by Robotics & Automation
News). Sustained revenue growth will come largely from ongoing subscription revenues that enable companies to tap into an ever-expanding suite
of AV and AI capabilities as organizations transition into full industrial autonomy.
Industrial operations are
extremely rich with data. However, we believe this data is still being put to limited use, especially as it pertains to equipment transport
and autonomy performance. EAS creates a foundation for extracting new and valuable enterprise data insights by the nature of the advanced
sensors, electronic control units, and connectivity that supports DriveMod’s functionality. We can monetize the data insights in
a variety of ways by offering configurable cloud dashboards for fleet/asset management, operational performance data, and predictive analytics
to customers. In parallel, exposing this fleet and vehicle data will be a boon for our OEM partners as they evolve to optimize their product
roadmaps and better integrate our technology to serve the future needs of industrial autonomy.
Go-to-market
Our go-to-market strategy
hinges on strategic collaboration and is based on a set of three basic principles:
● Collaborate with industrial vehicle OEMs
● Land & expand with end customers
● Partner instead of compete on adjacent enabling technology
Collaborate — industrial vehicle OEMs
Our focus is on acquiring
new customers who are either (a) looking to embed our technology into their vehicle products or (b) upsell their existing clients with
our vehicle retrofits. We follow a named account coverage approach. After establishing a customer relationship with an OEM, we seek to
embed our technology into their product roadmap and expand our services to their many clients. We believe this category represents a substantial
opportunity to generate revenue as a single relationship with an OEM can lead to revenue opportunities across the entire marketplace.
For example, our partner, Columbia Vehicle Group with whom we have partnered through a non binding memorandum of understanding, provides
over 70 years of vehicle manufacturing experience and customer insight.
Land & expand — end customers
Our go-to-market strategy
is to acquire new customers that use industrial vehicles in their mission-critical operations. We pursue this strategy by being hyper-focused on
building a robust pipeline of prospective customers (“land”) and utilizing strategic sales channels that will result in coordinated
opportunities to accelerate growth (“expand”). Our archetypal customers are corporations that deploy fleets of heterogeneous
industrial vehicles across many sites. DriveMod’s flexibility is intertwined with the wide-ranging applicability of our EAS
and creates the unique leveraged opportunity of expanding across vehicles and sites with these major customers. After an initial win for
a first AV deployment with a customer, we can expand within the site to additional vehicle platforms, then expand the use of similar vehicles
to other sites operated by the customer and finally repeat across new vehicles and sites.
Partner instead of compete — technology
The scaling of Industrial
Autonomy will benefit from an ecosystem made up of different enabling technologies and services, such as hardware manufacturing, connectivity,
Internet of Things (IoT), and digital integration. Rather than trying to compete with other technology suppliers, we intend to rely on
our strategic collaborations that give both partners access to new markets and capabilities. For example, partners like Arilou, Symboticware,
and Airbiquity respectively provide complementary solutions in technologies like cyber security, digital asset management, and connectivity
while partners like Formel D and First Transit provide expertise that aids towards platform scale up and operational services. These collaborative
partnerships are established through mutually beneficial, non-binding memorandums of understanding or partnering agreements for the
purpose of joint go-to-market efforts.
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Our Technology
Autonomous vehicles must integrate
a suite of technologies to generate operational value. Our core competencies are in DriveMod, the on-vehicle AV technology stack
that is underpinned by AI and robotics expertise and paramount to enabling autonomous mobility. With Cyngn Insight, EAS integrates analytics,
visual dashboards, connectivity, cloud services, and other traditional software systems that allow customers to interact with and extract
insights out of our advanced AV technology.
Mapping & localization
Our proprietary system design
abstracts mapping and localization data so that DriveMod can use a variety of high-accuracy solutions to create the optimal mapping
and localization system for the given environment. Our mapping and localization system distills sensor data into contextually rich representations
of the physical world and extracts common insights like required stops and navigation boundaries. These common insights help to create
consistent AV operation across diverse sites, enabling our AVs to navigate both indoor and outdoor.
Perception
Granular, efficient perception forms the basis of advanced AVs. Perception
is one of the most complex sub-systems, requiring specialized data infrastructure and engineering expertise in AI/ML and high-performance computing.
We have built a modular sensor fusion pipeline that runs on a low compute footprint and creates the flexibility to customize our perception
stack according to application requirements. Our perception architecture streamlines DriveMod deployments on new vehicles. Our approach
addresses common industry challenges like integrating different sensor modalities and accounting for different sensor mounting positions.
We have now integrated DriveMod into nine different vehicle platforms, utilizing various combinations of Light Detecting and Ranging (“LiDAR”),
camera, radar, and ultrasonic, and positioning sensors.
Path planning
Our system’s ability
to react and adjust to real-time changes creates a more efficient workflow than basic automation solutions that can only stop/go
along a rigid path and require constant human hand-holding. The Cyngn path planning system provides thousands of trajectory candidates
per second, enabling more complex paths to be navigated that may include advanced behavior like carefully nudging around obstacles or
negotiating intersections.
Decision making
The Cyngn decision engine
holds the logic and decision-making rules that govern driving behavior. The decision engine pulls together insights from mapping,
perception, and path planning to enable more complex vehicle maneuvers and automated conflict resolution. The system is extensible to
introduce new capabilities with logic that is designed to achieve a high level of abstraction, which enables us to adopt new driving behaviors.
Actuation
A subsystem of our software
stack, Cyngn-by-Wire (CbW), addresses the basic requirements of mechanical vehicle components that must be met for DriveMod to make
a vehicle operate autonomously. Legacy electronic control units (ECU) that do not use Drive-by-Wire (DbW) technology that enables
software commands to electronically control vehicle actuation typically create a hurdle for integrating AV technology. CbW addresses this
issue by decoupling the hardware and software components of DbW systems. For vehicles with legacy ECU’s, CbW allows customers to
replace existing ECUs with DbW hardware that can be tuned to meet the needs of the selected vehicle platform using CbW software. When
vehicles have DbW ECUs already installed, the CbW software layer is configured and applied without the need for replacing the hardware.
Thus, CbW enables AV actuation across vehicle fleets with varying levels of vehicle age and sophistication.
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Competitive Environment
There is an increasing demand
for autonomous vehicle solutions in an effort to increase safety, improve efficiency, and enhance productivity to meet the goals set out
by Industry 4.0. Autonomous vehicles are an enabling technology that gives us the opportunity to add more value to customers.
For Industry 4.0 markets, the global management consulting firm McKinsey
& Company has published reports indicating that the upswing in adoption will be dependent on the ability for technology to provide
companies with solutions that give customers a pathway that balances cost constraints with short-term resilience and long-term growth
(source: “Industry 4.0: Reimagining manufacturing operations after COVID 19,” by McKinsey & Company). As market participants
develop their Industry 4.0 roadmap, technology partners that have an ability to adapt features to their changing needs will be required.
As a result, we believe there will continue to be a need for technology companies to help push the industry 4.0 markets forward.
The market for automated vehicle
solutions is burgeoning, and the advanced technology required to enable autonomous solutions in industrial environments is still developing.
As a result, we face competition from a range of companies seeking to develop autonomous vehicle solutions. These competitors include
traditional industrial vehicle manufacturers (such as Crown Equipment’s automated forklifts) robotics providers (such as Brain Corp
for floor care and Outrider for yard operations), and software companies (such as Oxbotica), as well as large corporate competitors that
provide a broad range of software, service, and logistics solutions across many markets. These competitors are also working to advance
technology, reliability, and innovation in their development of new and improved solutions.
We will continue to face competition
from existing competitors and new companies entering the industrial autonomy landscape. Many of our competitors either have technical
or strategic barriers that limit their product offerings to specific deployment environments, operations protocols, or vehicle form factors.
It is our belief that it will take a substantial period of time to develop features that satisfy the dynamic needs of industry customers.
Additionally, larger corporate competitors are likely to encounter roadblocks due to competitive overlap with end customers, limiting
their ability to address the needs of the broader industrial market. With specific regard to manufacturing and distribution, a number
of competitors have already begun to deploy products, but we believe the benefits stemming from our modular software-centric approach,
technical expertise in the area of autonomous vehicles, and the ubiquitous applicability of EAS gives us the potential to displace current
offerings and capture a significant share of this rapidly growing market.
Governmental and Environmental Regulations
Regulatory considerations
contribute to our current strategic position that targets enterprise customers with operations mostly confined to private property. This
decreases our exposure to regulations, which mitigates some deployment risks. Typically, we will satisfy regulatory requirements by adhering
to the protocols of the site operator (the end customer).
The regulatory environment
for autonomous industrial vehicles is still being developed. In 2016, the United States Department of Transportation (USDoT) issued regulations
that require the submission of documentation covering specific topics related to autonomy and government regulators, but these regulations
are targeted towards road vehicles. As the autonomous industrial vehicle regulatory environment continues to develop, it will be imperative
not only to comply with applicable standards but to be an active participant in the development of new standards. Outside of government
standards, third party organizations, industrial workplace advocates, and industry groups have and will continue to impose self-regulatory standards.
In certain cases, these standards may be contractually applicable to our systems, products, and operations. Thus, we expect and prepare
to comply with various standards, including Occupational Safety and Health Administration (OSHA), International Organization for Standardization
(ISO), International Electrotechnical Commission (IEC), or American National Standards Institute (ANSI) on a case-by-case basis.
U.S. and international regulations
related to data privacy are also of great importance to our company’s products, operations, and culture. Like the autonomous vehicle
regulatory environment, the regulatory framework for data privacy, protection, and security worldwide is continuously evolving and developing.
As a result, interpretation and implementation standards and enforcement practices are likely to remain fluid for the foreseeable future.
As our company expands its operations, the collection, use, and protection of any and all data assets will be internally scrutinized to
ensure compliance with this changing landscape.
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Decreasing the environmental
impact of industrial vehicles is a high priority. Research has shown that equipment utilization rate, configuration, and operational consistency
have a strong effect on the emissions released by industrial vehicle equipment (Source: Journal of the Air & Waste Management Association). A
key focus of Cyngn’s EAS will be working to minimize the environmental impact of industrial vehicles through new data insights that
can contribute to more sustainable practices. Our historic vehicle platforms have primarily been electric vehicles (EV). While electric
drivetrains are not a requirement for DriveMod’s technology, EVs are often an application requirement since the vehicles operate
alongside humans in enclosed spaces.
Intellectual Property
Our ability to drive impact
and growth within the autonomous industrial vehicle market largely depends on our ability to obtain, maintain, and protect our intellectual
property and all other property rights related to our products and technology. To accomplish this, we utilize a combination of patents,
trademarks, copyrights, and trade secrets as well as employee and third party non-disclosure agreements, licenses, and other contractual
obligations. In addition to protecting our intellectual property and other assets, our success also depends on our ability to develop
our technology and operate without infringing, misappropriating, or otherwise violating the intellectual property and property rights
of third parties, customers, and partners.
Our software stack has over
30 subsystems, including those designed for perception, mapping & localization, decision making, planning, and control. As of the
date of this report, we have 2 granted patents, 22 pending patent applications, and expect to file an additional 4 patent applications
by the end of 2022. We expect to continue to file additional patent applications with respect to our technology in the future.
Human Capital Resources
Our team is composed of energetic,
motivated and highly experienced visionaries. They include machine vision, AI, and autonomous software engineers from the greatest universities
in the world. Together with a highly talented and skilled support team, we solve real-world industrial applications in autonomy.
As of the date of this annual report, we had 42 full-time employees. The majority of our employees are based in Silicon Valley, California.
Our core values include focus
on impact, display curiosity, communicate proactively, apply good judgment, and demonstrate selflessness. We believe these values encourage
innovation and a team-oriented culture. Our employees have access to a wide range of training, different career paths, and, most