Safety by architecture
Safety engineered through independent, verifiable layers with authority over the AI, designed to ensure critical decisions remain aligned with safety priorities.
Products
Driving the physical AI evolution
Pioneering automotive. Realizing humanoids.
Physical AI is tested within real-world complexities. It requires intelligence that can understand dynamic surroundings, reason through uncertainty, and act in real time across a vast range of scenarios.
Mobileye’s approach to physical AI is built upon four principles designed to enable both autonomous vehicles and humanoids to perform within the physical world that is too complex to script and too dynamic to predict.
Edge-case led learning
Learning built on the long tail of unexpected scenarios, designed to enable systems to perform where physical AI is challenged most.
Purpose-built stack
AI, sensing, compute, and the machine designed as a unified vertically integrated system, to support optimization of the performance of each component and the system as a whole.
Scale-first engineering
Cost, performance, and manufacturability optimized from the prototype stage, accelerating the path from innovation to mass-market deployment.
What are the essential elements of physical AI at scale?
AI must be pragmatic
Physical AI depends on more than a single model. Mobileye’s approach combines advanced learning, purpose-built components, and system-level logic across perception, planning, decision-making, and control. Each layer is designed to perform its role efficiently within one integrated system, while optimizing performance of the whole.
AI must be lean
Autonomous machines must operate within real-world constraints, including compute, power, cost, latency, reliability, and manufacturability. Mobileye’s approach is designed around efficient architectures that balance AI capability with the practical requirements of deployment.
EyeQ™ System-on-Chip
AI must be adaptable
The physical world is too varied to script in advance. Mobileye develops AI systems designed to address diverse scenarios, environments, and use cases while staying grounded in structured engineering and validation. This approach supports scalable autonomy on the road and informs Mobileye’s expansion into humanoids.
Products overview
AI must be safe
Mobileye systems are built with robust safeguards, including multiple formal safety models. Redundancies are also employed in key technologies across our hardware, software, and AI processes to support safe decision-making. Our focus is on building systems that are not only intelligent but also grounded in rigorous validation and designed for real-world deployment.
What are the essential elements of physical AI at scale?
AI must be pragmatic
Physical AI depends on more than a single model. Mobileye’s approach combines advanced learning, purpose-built components, and system-level logic across perception, planning, decision-making, and control. Each layer is designed to perform its role efficiently within one integrated system, while optimizing performance of the whole.
AI must be lean
Autonomous machines must operate within real-world constraints, including compute, power, cost, latency, reliability, and manufacturability. Mobileye’s approach is designed around efficient architectures that balance AI capability with the practical requirements of deployment.
EyeQ™ System-on-Chip
AI must be adaptable
The physical world is too varied to script in advance. Mobileye develops AI systems designed to address diverse scenarios, environments, and use cases while staying grounded in structured engineering and validation. This approach supports scalable autonomy on the road and informs Mobileye’s expansion into humanoids.
Products overview
AI must be safe
Mobileye systems are built with robust safeguards, including multiple formal safety models. Redundancies are also employed in key technologies across our hardware, software, and AI processes to support safe decision-making. Our focus is on building systems that are not only intelligent but also grounded in rigorous validation and designed for real-world deployment.
Mentee by Mobileye
The humanoid built for the complexities of the physical world
Mentee is designed to perform where AI meets reality. Built with few-shot learning, large-scale simulation and vertically integrated robotics, Mentee is designed to learn new tasks in hours not weeks, to perform at a high success rate in the real world, and to continuously improve through simulation to be able to work safely alongside people in homes and workplaces.
Explore Mentee
Mobileye’s physical AI foundation reflects decades of experience in real-world driving intelligence, applying the same engineering principles that shaped its automotive AI leadership to humanoid robotics within dynamic and complex physical environments.
Mobileye’s physical AI foundation reflects decades of experience in real-world driving intelligence, applying the same engineering principles that shaped its automotive AI leadership to humanoid robotics within dynamic and complex physical environments.
Frequently asked questions about physical AI
Physical AI refers to artificial intelligence that can perceive, understand, and act in the physical world. Unlike AI systems that work only with digital information, Physical AI is used in machines such as vehicles, robots, and other autonomous systems that must interact with real-world environments. These systems use sensors, software, and computing to make decisions and perform tasks based on what they observe around them.
Generative AI is designed to create content such as text including code, images, audio. Physical AI is designed to understand and respond to the real world. While generative AI might generate a written answer to a question, a physical AI system is designed to interpret its surroundings, make decisions, and take actions safely and reliably in dynamic environments. The two technologies can complement each other, but they are built for different purposes.
Examples of physical AI include advanced driver assistance systems (ADAS), autonomous vehicles, warehouse robots, delivery robots, industrial automation systems, intelligent machines, and humanoid robots. These systems can use AI to perceive their surroundings, understand what is happening around them, and perform actions in response.
Physical AI systems typically combine several technologies, including artificial intelligence, computer vision, sensor processing, machine learning, mapping, simulation, and high-performance computing. Many systems also rely on cameras, radar, lidar, or other sensors to gather information about their environment and support decision-making.
At Mobileye, humanoid development also incorporates advanced training and learning techniques such as few-shot learning, designed to enable systems to learn new tasks from a small number of examples, and Real2Sim2Real, a development approach that is designed to use real-world data to create realistic simulations and then transfers those learnings back into real-world operation. These methods are intended to help physical AI systems adapt more efficiently to complex and changing environments.
At Mobileye, humanoid development also incorporates advanced training and learning techniques such as few-shot learning, designed to enable systems to learn new tasks from a small number of examples, and Real2Sim2Real, a development approach that is designed to use real-world data to create realistic simulations and then transfers those learnings back into real-world operation. These methods are intended to help physical AI systems adapt more efficiently to complex and changing environments.
Physical AI systems are trained using data collected from real-world environments, such as images, video, sensor readings, and operational data. Developers use this data to teach systems how to recognize objects, understand situations, and respond appropriately. Training is often supplemented with simulation, which allows systems to encounter a wider range of scenarios than would be practical to collect in the real world alone.
In autonomous vehicles, physical AI may help the vehicle perceive its surroundings, identify other road users, understand road conditions, predict how traffic may behave, and make driving decisions. It supports enabling functions ranging from collision avoidance and driver assistance to highly automated and fully autonomous driving systems.
In robotics, physical AI helps robots understand their environment and carry out tasks with greater autonomy. This can include navigating through a workspace, recognizing and handling objects, following instructions, and adapting to changing conditions. Physical AI is used in applications ranging from industrial automation and logistics to service robots and humanoid robots at home.
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Diagnosing the long tail: how Mobileye turns edge cases into targeted training
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