Verifiable Obstacle Detection
This work addresses safety-critical perception issues for autonomous vehicles, offering a verifiable alternative to neural network-based systems, though it is incremental as it builds on existing classical methods.
The authors tackled the problem of verifying obstacle detection for autonomous vehicles by analyzing a classical LiDAR-based algorithm, establishing strict bounds on its capabilities to determine sensor properties that meet safety standards.
Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networks are not fully verifiable, making them unsuitable for safety-critical tasks. In this work, we present a safety verification of an existing LiDAR based classical obstacle detection algorithm. We establish strict bounds on the capabilities of this obstacle detection algorithm. Given safety standards, such bounds allow for determining LiDAR sensor properties that would reliably satisfy the standards. Such analysis has as yet been unattainable for neural network based perception systems. We provide a rigorous analysis of the obstacle detection system with empirical results based on real-world sensor data.