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, 2025
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)
1344 Terra Bella Avenue, Mountain View, CA 94043
(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: None
Indicate by check mark if the registered is a
well-known seasoned 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-T (§232.405
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 voting and non-voting
common equity held by non-affiliates as of June 30, 2025 (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 ($14.40), as reported on the Nasdaq Capital
Market, was $101,365,430.40.
The number of shares of Common Stock, $0.00001
par value, outstanding on March 26, 2026 was 13,608,281 shares.
DOCUMENTS INCORPORATED BY REFERENCE
None
TABLE OF CONTENTS
Page
PART I 1
Item 1. Business 1
Item 1A. Risk Factors 16
Item 1B. Unresolved Staff Comments 36
Item 1C. Cybersecurity 37
Item 2. Properties 37
Item 3. Legal Proceedings 37
Item 4. Mine Safety Disclosures 37
Item 6. [Reserved] 38
Item 7A. Quantitative and Qualitative Disclosures About Market Risk 46
Item 8. Financial Statements and Supplementary Data 46
Item 9A. Controls and Procedures 47
Item 9B. Other Information 48
Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections 48
PART III 49
Item 10. Directors, Executive Officers and Corporate Governance 49
Item 11. Executive Compensation 53
Item 14. Principal Accountant Fees and Services 60
Item 15. Exhibit and Financial Statement Schedules 61
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FORWARD-LOOKING STATEMENTS
This Annual Report on Form
10-K (this “Annual Report”) 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 to “we,” “us,” “our,” and Cyngn refer to CYNGN Inc. and
its consolidated subsidiaries.
Unless otherwise expressly
provided in this Annual Report, all historical per share data, number of shares issued and outstanding, stock awards, and other common
stock equivalents set forth herein relating to our common stock have been adjusted to give effect to a reverse stock split of our common
stock in a ratio of 1-for-150 effected on February 18, 2025.
ii
PART 1
Item 1. Business
Company Overview
Cyngn Inc. is 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, high labor costs, and work safety.
We integrate our full-stack
autonomous driving software, DriveMod, onto vehicles manufactured by Original Equipment Manufacturers (“OEM”) by integration
directly into vehicle assembly. We design the DriveMod 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, nearly 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 markets, we believe
we can source and execute revenue-generating opportunities more quickly.
Our long-term vision is for
our Enterprise Autonomy Suite (“EAS”)-which includes the DriveMod autonomous driving stack as well as the Cyngn Insight and
Cyngn Evolve tools for fleet management, analytics, and data collection-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 tow tractors and stand-on floor scrubbers to 14-seat
shuttles and electric forklifts in a combination of commercially released products, prototypes, and proof of concept projects, demonstrating
the extensibility of our AV building blocks.
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. The DriveMod Stockchaser with a 6,000-lb towing capacity became commercially available in early 2023
and was first commercially deployed with our partner-customer US Continental (“USC”), a California-based manufacturer of quality
leather and fabric care products. We then launched the DriveMod Forklift and the DriveMod Tugger as we continued to expand our vehicle-type
portfolio fleet through our OEM partnerships with BYD and Motrec, respectively. The DriveMod MT160 Tugger with a 12,000-lb towing capacity
was commercially released in 2024 in partnership with Motrec and is now deployed with multiple customers.
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We secured paid projects with
leading global customers like Arauco, along with additional projects from big brands in the Global 500 and the Fortune 100. Paid development
projects of this nature are selectively pursued to springboard new products or technology advancements, yielding promising outcomes such
as the DriveMod Forklift. The primary focus of the company is to achieve and expand production deployments with its commercially released
DriveMod vehicles. As of the end of 2025, those commercial deployments include the named accounts of John Deere, G&J Pepsi, Coats
Automotive, and USC, as well as other business awards that have not yet been publicly disclosed. Our patent portfolio expanded with 16
new U.S. patent grants in 2023 3 granted in 2024, and 2 granted in 2025 bringing the total grants to 24.
Figure 0: Summary of recent Cyngn technical
and commercial milestones
We intend to continue to pursue
and win additional license agreements with companies that depend heavily on the use of material handling vehicles and that all recognize
the need for automation to i) compete in today’s economy, ii) combat the significant labor shortages and escalating costs, and iii)
improve safety. Our approach to securing these opportunities will be a continued direct sales effort coupled with increasing our network
of industrial vehicle dealers that already have significant sales of industrial vehicles.
Overview: Automation and Autonomy in Industry
5.0
The fifth industrial revolution
is upon us with self-driving industrial vehicles operating alongside human workers and encompasses the benefits from the fourth industrial
revolution of smart factories and automated supply chain logistics, with big data connectivity. According to Research Nester, they forecast
the global industry 5.0 market to experience remarkable CAGR growth from 2022 - 2030 led by industrial internet of things, artificial
intelligence with the alliance of humans and collaborative robots.
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 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.
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Automation has long played
a role in industrial sectors. The larger industrial automation market has grown significantly by riding the wave of new technology and
innovation experienced during Industry 4.0. This consists 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. However, Industry 4.0 has its limitations in industrial
autonomy. With the convergence of AI/ML, robotics, connectivity, mapping and interoperability, autonomous vehicle technology is the next
leap-frog advancement in materials handling and supply chain logistics efficiency and safety propelling manufacturing into the Industry
5.0 phase.
Figure 1: Illustration of the progression from
Industry 1.0 to Industry 5.0.
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 a Grand View Research report, the material handling equipment market was valued at $213.4 billion in 2021. They expect the
CAGR from 2022 to 2030 of 5.7% to be driven by increased worker safety awareness, rising requirements for managing bulk materials and
further adoption of Industry 4.0 initiatives with the use of IoT. Further with the rising need for reducing downtime and focus on improving
supply chain efficiency, self-driving industrial vehicles play an important role to achieve these objectives as manufacturing transitions
to Industry 5.0. 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, industrial vehicles in the MTE category are
largely still driven manually. A March 2023 report released by MHI and Deloitte finds that 74% of supply chain leaders are increasing
their supply chain technology and innovation investments with 90% saying they are planning to spend more than $1 million, an 24% increase
from 2022 levels. Thirty-six percent plan to spend more than $10 million, up 19% from the prior year. According to an article by
Meteor Space, “Important Warehouse Automation Statistics you can’t Ignore,” the use of AI in warehouse management systems
has surged, with 70% of large-scale warehouses adopting AI-driven solutions by 2024 to optimize inventory management, demand forecasting,
and route planning.
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Supply chain, logistics, and
manufacturing operators are increasingly facing labor shortages, rising costs, and operational inefficiencies, which we believe is accelerating
the adoption of automation technologies. Industry data (Descartes’s Study) indicates that approximately 76% of supply chain operations
are currently impacted by labor shortages, and in the United States there are significantly more job openings than available workers,
contributing to persistent workforce gaps. The manufacturing sector alone could face millions of unfilled positions over the next decade,
further increasing pressure on companies to automate repetitive and labor-intensive tasks. Industrial truck and material handling vehicle
operators represent a meaningful component of operating costs within warehouses and manufacturing facilities, with median annual wages
in the United States in the mid-$40,000 range, excluding overtime, benefits, and other employer-related costs, according to U.S. Bureau
of Labor Statistics data.
In addition to labor shortages
and rising labor costs, workplace safety expenses represent a significant financial burden for industrial operators. Worker injury claims
can average tens of thousands of dollars per incident, and total workplace injury costs in the United States exceed hundreds of billions
of dollars annually. According to the Economic Policy Institute, over
$250 billion are spent on workplace injuries each year. Autonomous industrial vehicles have the potential to reduce both
labor dependency and safety risks by automating repetitive material transport tasks, which may lower operating costs and improve productivity.
According to PWC’s “Industrial Mobility: How autonomous vehicles can change manufacturing” report, only 9% of manufacturers have
currently adopted autonomous technologies, but this is expected to grow as the benefits of improved efficiency and reduced costs become
more apparent. As a result, we believe automation is becoming increasingly essential for industrial operations, with a substantial
majority of manufacturing leaders identifying automation as critical to future success. Despite these trends, adoption of autonomous industrial
vehicle technology remains in the early stages, suggesting a significant opportunity for future market growth as organizations seek solutions
to workforce shortages, cost pressures, and supply chain resilience challenges.
Automating industrial vehicles will address the
following challenges:
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 2022 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).
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.
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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 Ricoh & ABI Research Report.
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.
Business Model: The Enterprise Autonomy Suite
for Industrial Vehicles
A number of business models
have been developed to support industrial autonomy where software is the enabling technology that’s transforming supply chain logistics
under Industry 4.0 and 5.0. Software as a Service (SaaS) is the initial working business model for the company but this is not entirely
accurate as vehicle hardware plays an integrated role in automating materials handling. Autonomy requires both the movement of “bits”
(or software) and the movement of “atoms” (or hardware). Robots as a Service (RaaS) is a useful business model given the high
cost of AGVs and AMRs where industrial customers may want to explore different buying models for both software and hardware usage to align
with their needs for capital expenditures and operational expenditures. In our served markets where customers primarily purchase and own
their industrial vehicle fleet, we deliver the software that enables self-driving vehicle capability. As such, our EAS software is designed
to provide level-4 “high automation”, fully autonomous driving without the need for a human in the vehicle. Cyngn’s
business model is thereby more attuned to Driver as a Service (DaaS) as our EAS software integrated with the vehicle hardware enables
the customer to remove the human driver for self-driving functionality.
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 developed 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.
5
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.
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-premise 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.
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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.
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.
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 solutions have been commercially released for the Motrec MT160 Tugger, with BYD ECB50+ Forklift
targeted next. 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. Other past deployments were part of our normal R&D activities and product validation
that was performed with beta customers.
7
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).
Our Products
EAS is a suite of technology
and tools that consists of 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 hardware providers covers the end-to-end requirements
that enable vehicles to operate autonomously with advanced navigation capabilities. 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.
8
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 of AV hardware and software onto vehicles at scale. The DriveMod Kit for Motrec
MT160 Tuggers are released to mass production 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 and 5.0 technology.
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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.
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.
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 6: 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.
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.
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Corporate Strategy
360° Sales and Marketing
We are building a go-to-market
ecosystem that we believe to be highly leveraged by using our partners as the foundation of our growth strategy. We plan to 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 can be conducive to short deployment timelines due to the similar use of material handling vehicles across these environments.
We have already deployed DriveMod-powered industrial vehicles at multiple manufacturing and distribution facilities of varying sizes,
including facilities as large as 4 million square feet.
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.
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.
EAS License
EAS is a comprehensive suite
of technologies comprised of DriveMod, our modular industrial vehicle autonomous driving software; Cyngn Insight, our customer-facing
fleet management and data analytics platform; and Cyngn Evolve, our internal AI and machine learning development and simulation infrastructure.
Together, these solutions create a foundation for extracting new and valuable enterprise data insights through advanced sensors, electronic
control units, and vehicle connectivity that power DriveMod’s autonomous functionality.
Through Cyngn Insight, we
monetize these data insights by offering configurable cloud dashboards for fleet and asset management, operational performance monitoring,
remote vehicle operations, and predictive analytics. Cyngn Evolve continuously enhances our AI models using real-world and synthetic data,
enabling ongoing performance improvements and validated software releases.
Collectively, the data generated
and analyzed across the EAS suite not only drives customer value but also provides meaningful insights to OEM partners as they optimize
product roadmaps and integrate autonomous capabilities to meet the evolving demands of industrial automation.
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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 Strategy
Our go-to-market
strategy hinges on strategic collaboration and is based on a set of three basic principles:
● Collaborate with industrial vehicle OEMs and their dealer and service networks
● 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 that can lead to revenue opportunities across the entire marketplace.
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, hardware partners
like Motrec and BYD provide complementary solutions that stand to benefit from Cyngn’s autonomy software. Thereby, we leverage our
R&D resources with existing core competencies of our technology partners through collaboration, resulting in a more efficient asset-light
approach in product development and manufacturing.
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.
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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 subsystems, 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.
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 and 5.0. Autonomous vehicles are an enabling technology that gives us the opportunity to add more value to customers.
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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 or Toyota, Yale and Vecna Materials
Handling), robotics providers (such as Seegrid Corporation for pallet and tow AMRs), 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 (“US DoT”)
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.
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