Introducing AD3.0 with Agentic AI

Autobrains’ Continuous Self-Learning (CSL) framework enables learning from unlabeled data through high-dimensional sparse representations ("signatures").

This incremental, bottom-up approach continuously improves system performance, identifies FP/FN patterns, and supports robust operation across real-world autonomous driving scenarios.

A proprietary AI architecture for autonomous driving, protected by over 100 patents. Built on an agentic approach, it enables scalable, efficient deployment by structuring intelligence into coordinated AI agents. This architecture supports real-world autonomy across diverse scenarios while maintaining low compute requirements and high reliability.

Perception Fields models how surrounding objects influence vehicle behavior through structured virtual field representations.

Backed by over 30 patents, it enables real-time contextual understanding, immediate feedback on dynamic environments, and improved interpretability for both system development and regulatory validation.