Arene
A unified platform for automotive software development
Automated driving
Automated driving
Vehicle intelligence for everyone’s peace of mind
Vehicle intelligence for everyone’s peace of mind
From promise to mass production
Automated driving isn’t just about ideas; it’s also about the infrastructure needed to bring those ideas reliably into vehicles at scale. That’s why we’re taking a full-stack approach, pairing advanced AI models with Toyota’s expertise in safety, quality and scale to pave the way for vehicle intelligence people can trust.
One production-grade system, powering learning at scale
Automated driving covers a wide range of technologies that can augment or replace human control for a certain period of time. Our approach brings physical AI, safety guardrails and a proprietary learning loop into a single system that can be applied across this spectrum and beyond. Developers can improve driving capability with greater visibility and control, while drivers experience system behavior that remains consistent across regions, conditions, and vehicle platforms.
Safety-governed physical AI
Our layered architecture combines an AI decision-making system with a safety framework grounded in local driving rules and Toyota’s safety principles. The system reasons over the full driving scene to generate continuous driving decisions, while the safety framework enforces validated constraints so behavior remains predictable and repeatable across conditions.
A proprietary Active Learning Loop
Our Active Learning Loop improves vehicle behavior by learning from diverse, OEM-scale driving data. Signals from vehicles operating across regions, environments and model years feed a continuous learning cycle. This helps improve everyday performance while identifying rare edge cases and long-tail scenarios earlier in development.
A scalable driving stack
A unified, scalable driving stack that supports automated driving across multiple levels of assistance and automation. By sharing learning, validation and system components across automated and assisted driving products, the stack reduces duplication and allows improvements to carry forward as vehicles, markets, and capabilities evolve.
Purpose-built tooling for developers
Our purpose-built tooling empowers developers to build, test and refine driving intelligence with speed and confidence. Designed for the realities of automated and assisted driving technology development, these tools bring data analysis, simulation, validation and deployment into a single workflow, helping teams surface issues in real time and reduce friction across the development cycle.
Safety-governed physical AI
Our layered architecture combines an AI decision-making system with a safety framework grounded in local driving rules and Toyota’s safety principles. The system reasons over the full driving scene to generate continuous driving decisions, while the safety framework enforces validated constraints so behavior remains predictable and repeatable across conditions.
A proprietary Active Learning Loop
Our Active Learning Loop improves vehicle behavior by learning from diverse, OEM-scale driving data. Signals from vehicles operating across regions, environments and model years feed a continuous learning cycle. This helps improve everyday performance while identifying rare edge cases and long-tail scenarios earlier in development.
A scalable driving stack
A unified, scalable driving stack that supports automated driving across multiple levels of assistance and automation. By sharing learning, validation and system components across automated and assisted driving products, the stack reduces duplication and allows improvements to carry forward as vehicles, markets, and capabilities evolve.
Purpose-built tooling for developers
Our purpose-built tooling empowers developers to build, test and refine driving intelligence with speed and confidence. Designed for the realities of automated and assisted driving technology development, these tools bring data analysis, simulation, validation and deployment into a single workflow, helping teams surface issues in real time and reduce friction across the development cycle.
Inside Woven by Toyota’s Active Learning Loop
With access to global fleet data, our automated driving stack learns across markets, vehicle platforms and over years of use. That collective experience becomes a feedback loop — refining models, improving comfort and closing long-tail scenarios faster than any one region or test fleet could. It’s how our automated driving technologies grow smarter, safer and more predictable over time.
Data-driven development
Continuous improvement of customer value
Vehicle data is collected
Sensor and system data from driving sessions are recorded for analysis
Issues and related data are identified
The system identifies driving scenes relating to the issue in question
Database's related scenes are found
New scenarios are added to a growing library of real-world driving situations
Database's related scenes are found
Similar scenarios from past data are retrieved to refine Al learning
Experts and experience guide training
Engineers select key driving scenes and define signals for what good looks like
AI learning and evaluation
The AI model is trained and tested against real-world scenarios
Updates to AI models delivered OTA
Improved Al models are sent wirelessly to vehicles, enhancing performance
Drivers enjoy automated driving features
Drivers experience our automated driving features in real-world conditions
Ready for wherever the road takes us
Engineered for real constraints
Designed to operate within production limits for compute, memory, sensors, timing and diagnostics — not just in lab environments.
Consistent performance, globally
Built to perform predictably across regions, road types, weather conditions, traffic patterns and local driving cultures.
Trust-centric user experiences
Interfaces and behaviors tuned for trust, comfort, clarity and predictability so advanced driving feels intuitive for customers.
Secure data operations
High-volume pipelines that process fleet data from vehicles around the world while enforcing strict privacy and data-handling governance standards.
The latest on automated driving
Agentic AI for a Zero-Accident Future: Accelerating MISRA Code Compliance to Strengthen Software Reliability
As vehicle software grows in complexity, ensuring reliability becomes critical. Learn how MISRA Copilot (an AI tool developed with Microsoft) helps automatically resolve coding violations, accelerate compliance and support more reliable automotive software development.
Driving the Future of Autonomous Mobility Forward — Together with Toyota and Waymo
Toyota and Waymo have announced a preliminary collaboration to accelerate autonomous driving technologies, with Woven by Toyota expected to play a key enabling role. This partnership aims to advance on a future with zero accidents and expand mobility through shared innovation.
Meet the people behind the work
Discover the voices and experiences behind our work, shaped by a shared commitment to safety, quality and innovation for others.
Join the AD/ADAS team
