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, 2022
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. ☐
If
securities are registered pursuant to Section 12(b) of the Act, indicate by check mark whether the financial statements of the registrant
included in the filing reflect the correction of an error to previously issued financial statements. ☐
Indicate
by check mark whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation
received by any of the registrant’s executive officers during the relevant recovery period pursuant to §240.10D-1(b). ☐
Indicate
by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Exchange Act). Yes ☐
No ☒
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, as of June 30, 2022 (the last business day of the registrant’s most recently completed second fiscal quarter),
computed by reference to the closing price for the Common Stock on such date ($1.15), as reported on the Nasdaq Capital Market, was $11,277,345.
The number of shares of Common Stock, $0.00001 par value, outstanding on
March 15, 2023 was 33,719,592 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 48
Item 14. Principal Accountant Fees and Services 53
Item 15. Exhibits and Financial Statement Schedules 54
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,” “our,”
and Cyngn refer to CYNGN Inc. and its consolidated subsidiaries.
ii
PART
1
Item
1. Business
General
We
are an autonomous vehicle (“AV”) technology company that is focused on addressing industrial uses for autonomous vehicles.
We believe that technological innovation is needed to enable adoption of autonomous industrial vehicles that will address the substantial
industry challenges that exist today. These challenges include labor shortages, lagging technological advancements from incumbent vehicle
manufacturers, and high upfront investment commitment.
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 universal 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”)
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 more than ten different
vehicle form factors that range from stockchasers and stand-on floor scrubbers to 14-seat shuttles and electric forklifts 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 technical and commercial milestones
2
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 total addressable market (“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.
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.
3
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.
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.
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. Our solutions also do not require infrastructure investments to enable autonomous vehicle operation.
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, resulting in an ever-improving EAS offering.
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 labor 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
vehicle-agnostic capability to deploy AV technology on diverse vehicle fleets has been proven through its deployment on more than ten
different vehicle form factors that we have operated autonomously. DriveMod has been commercially released for the Columbia Stockchaser,
and a commercial agreement with a customer is subsidizing the development of DriveMod for electric forklifts towards commercial availability.
Other autonomous vehicles were deployed as prototypes or as a part of proof-of-concept project. More than five past deployments
have been at customer or beta customer sites. For one deployment we were paid $166,000. Other past deployments were part of our normal
R&D activities and product validation that was performed with beta customers.
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 is made up by 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.
The
core vehicle-agnostic DriveMod software stack is targeted and deployed to different vehicles through DriveMod Kits, which are
the AV hardware systems that take into account the specific needs of operating the DriveMod software on a specific target vehicle. Then,
after prototyping and productization, DriveMod kits streamline the integration AV hardware and software integration onto vehicles at
scale. The DriveMod Kit for Columbia Stockchasers is commercially released and available at scale. Subsequently, we expect to create
different instances of DriveMod Kits to support the commercial release of new vehicles on the EAS platform, such as the electric forklifts
and other industrial vehicles.
Figure
5: Overview of 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.
8
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.
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.
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
and distribution applications require the shortest timeline for deployment due to the common use of material handling vehicles in these
environments and the commercial availability of our DriveMod Stockchaser solution. Manufacturing 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 -powered
industrial vehicles at multiple manufacturing and distribution facilities. These vehicles were previously deployed as prototypes or as
a part of proof-of-concept project. Of these past deployments, one was paid. Future deployments will primarily be commercial deployments
with limited prototype or proof-of-concept deployments that may be engaged opportunistically for strategic value.
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
We
anticipate that our technology will generate revenue through three main methods: deployment, EAS subscriptions, and DriveMod customization.
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 can reinforce our deployment capability with integration and services
from third party partners. Working directly with our OEM partners as well as with third party experts ensures that we can deploy our
technology globally and at scale.
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.
DriveMod
Customization and Non-Recurring Engineering (“NRE”)
DriveMod’s
capability as an AV software stack will continue to expand in several dimensions—most notably, in the number of vehicles that DriveMod
can operate autonomously and the maneuvers that a DriveMod-powered vehicle can execute. Targeting DriveMod to operate new vehicle types
autonomously as well as the expansion of autonomous maneuvers that can be executed by DriveMod are both customizations that may generate
revenues from OEMs or end customers through NRE contracts. New customizations yield project-based revenues that are assessed based
on the scope of the deployment. This revenue stream is substantiated by the commercial contract that was entered into with an end customer
for deploying DriveMod on electric forklifts.
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. 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 more than ten different
vehicle platforms, utilizing various combinations of Light Detecting and Ranging (“LiDAR”), camera, radar, 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