Applied Intuition

By 2035, autonomous driving could create more than $300 billion in revenue in the passenger car market. From 2010 to 2021, established automobile manufacturers like GM, Honda, and Volkswagen poured $106 billion into ensuring their cars have the best self-driving capabilities before others beat them to market. In 2022, GM’s autonomous driving unit, Cruise, spent $2 billion with the hope of producing $50 billion in annual revenue by the end of the decade. Meanwhile, Waymo, a self-driving subsidiary of Alphabet, has spent over $3.5 billion testing its autonomous vehicles.

In 2023, Waymo drove a total of 7 million driverless miles, and Cruise drove 5 million driverless miles. Those numbers are up 345% since 2021, when the two companies drove 2.3 million and 882.5K miles, respectively. As companies race to achieve level 4 and 5 self-driving autonomy, more testing is required to reach a place where no driver intervention is needed. Not only is testing needed to innovate, testing is needed to pass public safety and regulation standards to even allow autonomous vehicles and Advanced Driver Assistance Systems (ADAS) on the road. The issue is that such testing is expensive and time-consuming. While Waymo has collected 7 million miles of real-world data, it has also collected data on 12.5 billion simulated miles. Simulated testing environments offer the flexibility to test rare scenarios drivers don’t usually face to better train vehicles and reduce the testing miles required by 99.99%, creating safer vehicles while reducing costs and enabling companies to progress faster.

Applied Intuition offers a platform enabling clients to design, test, and validate their autonomous systems in a virtual environment before testing on the road. Applied Intuition’s suite of products, focused on simulation and analytics, provides software infrastructure for self-driving testing. As it continues expanding its simulation and data suite, the company is extending to provide simulation testing for autonomous robotics, trucking, and defense vehicles.

Applied Intuition was founded in 2017 by Qasar Younis (CEO) and Peter Ludwig (CTO).

Younis was born on a farm in Pakistan. His uncle, an engineer at GM, sponsored him and his family to move to the US and live outside GM headquarters. After growing up in Detroit, Younis attended the General Motors Institute of Technology to study mechanical engineering. He spent the first 7 years of his career as a mechanical engineer in the automotive industry. After attending Harvard Business School, Younis started TalkBin, which was acquired by Google, where Younis met Ludwig for the first time. Before founding Applied Intuition, he served as the COO of Y Combinator.

Ludwig grew up in a household where most of his family were automotive engineers, starting with his grandfather, who had worked at GM for more than 30 years. His first job in high school was as a software engineer at an automotive tooling company. He went to the University of Michigan for his undergraduate and master’s degree. After college, he began his career as an Associate Product Manager at Google. As part of the program, one year in, Ludwig met Younis. The pair soon clicked and began working together on Android Automotive at Google.

They considered founding a self-driving company in 2013 but concluded that level 4 autonomy would likely only be a good business for larger companies with tons of capital to throw at the problem. After GM acquired Cruise in 2016, the pair started talking again and ultimately found the business model best suited to the AV environment at the time— simulation testing for autonomous vehicle companies. This gave rise to Applied Intuition.

Applied Intuition sells software that enables autonomous vehicle companies to simulate and test the software and hardware of their vehicles in as many circumstances as possible, helping to accelerate their pace of innovation by speeding up client feedback loops, testing at scale, and reducing development costs.

Simulation

Simian: Simian is Applied Intuition’s primary simulation tool which generates scenarios so developers can test their software in many different situations quickly. It’s valuable to clients because Simian can create thousands of scenarios achieving comprehensive coverage to identify capability gaps and suboptimal performance of their vehicles. This not only accelerates the development process but also enhances the safety and reliability of the autonomous systems before they are deployed on public roads.

Source: Applied Intuition

Spectral: Spectral, a sensor simulator, ensures the software is working as it should in reaction to how a car’s sensors work. If a sensor detects an object moving closer in front of the car, Spectral makes sure the car slows down or stops in reaction to that sensor. Spectral’s main concern is testing whether the car’s sensors (or perception system) would work well enough to detect and relay to the software that an object is coming closer. Spectral does this with a library of sensors in different scenarios, like cold weather or rain, to see if the sensors work as expected. It can also determine the best configuration of sensors with its prototyping feature. One of Spectral’s value propositions, according to Applied Intuition, is that it allows users to “comprehensively test robustness to long-tail cases” so that they can be sure about edge cases that are “difficult or dangerous to test in the real world.”

Source: Applied Intuition

Orbis: This offering provides continuous integration. A problem developers encounter when writing code for sown simulators, like Simian or a third-party one, before allowing the code to merge.elf-driving vehicles is that bugs in the code are costly and hard to find. This is especially true if they write a lot of code and try to merge it with the main codebase; things break, and everyone needs to fix it urgently. Orbis aims to solve this problem by ensuring that every time any code is about to get merged, it will run predefined scenarios with this new code added and seOrbis: This offering provides continuous integration. A problem developers encounter when writing code for self-driving vehicles is that bugs in the code are costly and hard to find. This is especially true if they write a lot of code and try to merge it with the main codebase; things break, and everyone needs to fix it urgently. Orbis aims to solve this problem by ensuring that every time any code is about to get merged, it will run predefined scenarios with this new code added and see how performance changes before allowing it to get merged. Orbis can run its own simulators, like Simian or a third-party one, before allowing the code to merge.

Source: Applied Intuition

Logstream: A good way to test an autonomous vehicle is on accurate world data. To learn from the situations that vehicles weren’t prepared for, Logstream will re-run those scenarios on existing code to help developers determine what went wrong and fix it. Once they do, Logstream will also add that scenario to the test suite so that all future code written will be checked against that scenario. Logstream is intended to make it so that the more experience a fleet has, the better it will become over time.

Source: Applied Intuition

Vehicle Dynamics

In 2022, after acquiring Mechanical Simulation, Applied Intuition started helping vehicle manufacturers simulate and test the mechanical parts of their vehicles in different environments. The software simulates the dynamic behavior of passenger vehicles, multi-axle trucks, and motorbikes. It uses a 3D multi-body dynamics model to reproduce the physics of the vehicle in response to controls from the driver, like steering, braking, and gear shifting in different and potentially difficult environments such as snow and ice.

Data & Maps

Strada: This offering provides log data exploration. AV and ADAS companies get thousands of hours of data about their vehicles that are often impossible for individuals to sift through. Strada automatically detects anomalies in the data from the vehicles and provides a video replay of when things went wrong so developers can easily find errors and fix them. The replays come with multiple cameras and satellite views. Strada ingests all drive logs and automatically attaches higher-level meta-data to events and objects, making them easy to search for in a SQL database.

Source: Applied Intuition

Meridian: This offering provides map editing and analysis for companies that would like to test their own software on real-world maps. With Meridian, companies can create their own 3D maps based on real-world locations, validate that everything about their existing maps is correct, or create their own synthetic maps to test for specifically difficult scenarios.

Synthetic Datasets: This offering provides labeled data for training machine learning algorithms. Oftentimes, the data a vehicle’s perception system can collect is too sparse to use for simulations and become useful. To expand to a new region with different roads or signage, AV companies would need to deploy their fleets in the physical world to learn what to do, which is time-intensive and expensive. Synthetic data solves the sparse data issue by filling the gaps in the data so that each recorded scenario can become another useful test case. It helps companies train on synthetic data before they start semi-supervised learning on real data, saving them money and training time.

Validation

Basis: This offering provides verification and validation management. After testing their vehicles in millions of scenarios and hundreds of thousands of miles on the road, AV and ADAS companies generally need to update their external stakeholders, like regulators, auditors, and compliance teams. Basis automatically takes the performance statistics from all tests and real-world drives and aggregates that information into a single dashboard to share with anyone from a non-technical background. It allows teams to customize which KPIs they want to track, centralize requirements, and test management across different testing methods (Simian, Spectral, etc.).

Source: Applied Intuition

Applied Test Suites: Applied Test Suites are pre-defined simulation test cases for vehicle software testing and safety validation. Creating scenarios or simulations, even with Applied Intuition’s Simian, can be very time-consuming for teams with limited bandwidth, especially when teams are getting started. That’s because teams need to set specific performance goals in line with regulatory benchmarks, which pre-defined test cases can help validate.

Customer

Applied Intuition’s core revenue comes from OEMs, like GM and Toyota, who use Applied Intuition to improve automation for driving and processes already in production in 2023, such as auto-parking and automated braking systems. The company has had success with automotive OEMs counting 17 of the top 20 as customers. It also works with tech companies like Kodiak Robotics, Voyage, and May Mobility.

Applied Intuition is venturing into numerous industries adjacent to simulation testing. For example, in the robotics sector, customers like Scania leverage Applied Intuition's technology for simulating and testing robots in warehouses and mines. Moreover, both the U.S. Army and the Defense Innovation Unit (DIU) have selected Applied Intuition to provide a comprehensive software development and testing platform for the Army's Robotic Combat Vehicle (RCV) program. In the trucking simulation domain, Applied Intuition has made a significant move by acquiring Embark for $71 million. Other industries Applied Intuition has recently expanded into includes construction & mining, agriculture, aerial, and autonomous mobile robots (AMRs).

Market Size

The simulation software market was valued at $7.8 billion in 2020 and is expected to reach $15 billion by 2026. The market is expected to grow at a CAGR of 11.7% from 2021 to 2026. To break this down further, the simulation software market that Applied Intuition is tackling would fall under a couple of core revenue buckets: the driving simulator market, the drone simulator market, the military simulation and testing market, and the smaller markets it serves as well.

The driving simulator market was valued at $1.5 billion in 2020 and is expected to reach around $6 billion by 2026. The drone simulator market size is expected to grow at a CAGR of 13.4% from 2022 to 2027 to reach $1.5 billion by 2027. As for the military, the Pentagon planned to spend $4.6 billion on unmanned and AI systems in the fiscal year 2020. Applied Intuition also sells into smaller markets, including agriculture, AMR, and construction and mining.

Cognata: Cognata is a simulation platform that provides a testing environment and simulation platform for AVs, ADAS, defense, agriculture, construction and mining, and warehouses. It was founded in 2016 and is headquartered in Rehovot, Israel. Cognata has raised $27.8 million in funding. Cognata is a direct competitor to Applied Intuition, competing on most of Applied Intuition’s products in the same industries Applied Intuition is going after.

Aurora: Aurora was founded in 2017 and went public in 2021. The company develops Aurora Driver, integrating software, hardware, and data services to automate cars and trucks. Unlike Applied Intuition, which concentrates exclusively on testing through simulation, Aurora adopts a more hands-on approach by directly developing and deploying self-driving technology. Aurora also bought Uber’s entire self-driving division in 2020.

Baidu’s Apollo: Baidu, based in China, is one the largest internet companies in the world, valued at $33 billion in April 2024. Apollo is Baidu’s open-source software platform for creating autonomous vehicles, introduced in 2017. In addition to Apollo’s self-driving software that can power OEMs with self-driving capabilities, one of Apollo’s core products is providing simulation for companies looking to create their own AV software. While the product isn’t as expansive as Applied Intuition’s (it seemingly does not do CLI or synthetic data creation), it has partnerships with Chinese automotive manufacturers like Chery, BYD Auto, Great Wall, and automotive manufacturers Kia and Ford.

Fortellix: Fortellix verifies and runs simulations for automated driving systems for safe and large-scale deployment. Fortellix was founded in Tel Aviv, Israel, in 2017. It has raised over $47 million in funding. Fortellix does not do vehicle dynamics simulations, nor can it do log re-simulation. Fortellix works with Volvo, among others, to verify their autonomous systems on highways and mines.

Parallel Domain: San Francisco-based Parallel Domain is a synthetic data generation platform for computer vision and autonomous systems. Parallel Domain was founded in 2017. In November 2022, Parallel raised a $30 million Series B round led by March Capital, bringing its total funding to $44 million. It competes directly with Applied Intuition on Spectral, the sensor simulator and tester, and synthetic datasets.

Nvidia: Nvidia forward integrated and introduced its Nvidia Drive Sim platform in 2015 in attempts to create an end-to-end platform for AVs. They have customers such as Mercedes-Benz, Volvo, and Cruise. Nvidia believes they have competitive advantage because of their end-to-end solution, enabling alignment across the entire architecture. Nvidia is investing heavily in their platform and has estimated $2-3 billion in annual AV revenue by 2025 or 2026 in its design win pipeline from its entire autonomous vehicle segment.

Applied Intuition offers software and services to help clients design, develop, and validate their autonomous systems. It licenses its software tools on a subscription basis to companies developing AV and ADAS. It works with customers to tailor its simulation and testing tools to their needs. This allows the company to charge a premium for customized solutions that address clients' unique challenges. Applied Intuition also provides consulting and support services to help clients implement and integrate its software tools into the customer’s development processes.

Applied Intuition counts 17 of the top 20 autonomous OEMs as customers. After becoming a core partner in how GM, Nissan, Hyundai, and others develop autonomous vehicles, Applied Intuition began expanding verticals. It offers simulation and testing services to industries like defense, agriculture, mining, construction, aerial, and others. In November 2022, Applied Intuition was selected by the U.S. Army and the Defense Innovation Unit (DIU) to provide autonomous software development and testing platform for the Army’s Robotic Combat Vehicle (RCV program) in a $49 million contract. In December 2022, it added 3 more high-ranking officials with deep DoD connections to its National Security and Defense Advisory Board to help guide its vision and conversations with the Department of Defense.

In March 2024, Applied Intuition raised a $250 million Series E at a $6 billion valuation from existing investors, such as Lux Capital, a16z, Elad Gil, General Catalyst, as well as “strategic investor Porsche Investments Management S.A. representing the well-known successful sports car manufacturer from Stuttgart-Zuffenhausen.”

Increased Regulation

Multiple European countries have created independent efforts to form AV and ADAS regulations rather than coordinating with each other. Different regulations have also popped up in China at the municipal level. As regulations from governments across the globe become more difficult to navigate, the more scenarios a self-driving car company would need to pass before it could launch in different countries or even bordering cities in the same country. That’s where pre-defined scenarios targeting different regulations from Applied Intuition become essential for companies looking to expand. It would cost too much to do all the testing for different regulations in the physical world.

Data Advantage

It’s unclear if Applied Intuition has usage rights to the anonymized tests or data that its customers create on the platform. If it does, it likely has more data about autonomous vehicles than any provider, considering it works with 17 of the top 20 OEMs. With this amount of data it can create a moat by offering its customers a data network of the best datasets to speed up training time and attract new customers by selling already trained AV and ADAS software directly to other OEMs looking to start an AV or ADAS unit.

The Infrastructure of Autonomy

Due to expanding regulations and simulation testing significantly reducing costs because it drops the amount of miles required by 99.99%, Applied Intuition has the potential to become the software infrastructure for autonomy. $160 billion has already been poured into autonomy, yet this technology is still in its infancy. In order for autonomy to be more ubiquitous, all these vehicles need training to achieve level-5 autonomy. Applied Intuition could be the software infrastructure to cost-effectively deliver that required training.

Diverse Customer Base

Applied Intuition has just as many top OEMs, like GM, Toyota, Nissan, and VW for customers as it does startups like Voyage, May Mobility, and Kodiak Robotics. It even landed a $49 million contract with the military. By continuing to serve diverse customers across startups, established OEMs, and the military within different regions and regulatory environments, Applied Intuition can significantly reduce the core risk of the business and increase revenue potential.

Generative AI

Applied Intuition creates value for its customers by providing synthetic data and simulation testing for its customer’s specific use cases. As generative AI becomes better at generating synthetic data based on samples, vertical synthetic data creation for AVs and ADAS could become less necessary — limiting the dependence on Applied Intuition. MostlyAI, for example, given any dataset, can produce what-if scenarios and predict and replace missing values, covering two key use cases for Applied Intuition. If these generalized solutions get good enough, it would have potentially dire implications for Applied Intuition.

Regulatory Risks

Self-driving cars can be seen as a threat to jobs. The Upstate Transportation Association has urged New York to ban self-driving vehicles from state roads for the next 50 years, claiming that the “damage” to the economy will be too great. Pushbacks like this can be expected from most driver associations across the country. Opposition is also likely to stem from injuries and deaths that occur due to self-driving vehicles, which may increase regulatory hurdles further. After seeing many injuries and deaths because of Tesla’s self-driving vehicles, Dan O'Dowd ran for the US Senate to try to ban self-driving cars across the state of California. While these campaigns have not been successful, banning self-driving cars for a long enough period, or erecting significant enough regulatory barriers to effectively prevent the deployment of autonomous vehicles, could stop investment in the sector, hurting Applied Intuition’s customers and ultimately hurting Applied Intuition.

As autonomous systems across every industry become more prevalent, they have become increasingly reliant on virtual testing software that saves them the money and time involved in real-world testing. Applied Intuition’s products for simulation and testing are being increasingly leveraged by OEMs, though the market offers several direct rivals, including companies like Cognata and Apollo. If Applied Intuition can become the de facto provider of synthetic data generated from the advances of generative AI, it may be able to maintain its position in the market.ket will last is hard to say.