Using natural language as a key tool for both training and explaining AI driving models.

Our system is designed as a universal neural network that processes raw sensor data, transforming it into actionable driving decisions. By training on a vast range of diverse data, we ensure the system can generate safe driving outputs in a wide variety of conditions.

Vehicle Flexibility

It can be applied across different platforms, from passenger vehicles to delivery fleets, allowing breakthroughs in one category to benefit all. This means that the core technology adapts smoothly, no matter the type of vehicle it's used on.

Autonomy Without Handbook

Unlike traditional autonomous systems that rely on detailed maps, our approach enables mapless navigation. This allows for easier deployment across new regions by leveraging data-driven learning without the need for HD maps.

Self-Supervised Learning

The system is built using self-supervised learning, which allows it to learn driving behavior from raw data without needing manually labeled datasets. This not only speeds up the training process but also significantly reduces the costs associated with data curation.

This approach enables us to scale and adapt autonomous driving solutions faster and more efficiently, opening up possibilities for more flexible and widespread autonomous systems.

AOP Safety

Our Safety system takes a groundbreaking approach to driving safety by focusing on an AI’s deep understanding of the environment. Instead of relying on traditional techniques, this method ensures that safety comes from an AI that processes the road and surrounding world much like a human driver, enabling it to naturally interpret complex driving scenarios and make safer decisions.

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