An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
This addresses safety-critical challenges for autonomous vehicles operating with environment uncertainty, offering a novel approach to real-time safety guarantees.
The paper tackles the problem of ensuring safety for autonomous vehicles in unknown environments by proposing a real-time safety analysis method based on Hamilton-Jacobi reachability, which provides strong safety guarantees and is demonstrated in simulation and hardware with a vision-based, learning-based planner.
Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely in the face of environment uncertainty, such as unseen obstacles. Current safety analysis tools enable autonomous systems to reason about safety given full information about the state of the environment a priori. However, these tools do not scale well to scenarios where the environment is being sensed in real time, such as during navigation tasks. In this work, we propose a novel, real-time safety analysis method based on Hamilton-Jacobi reachability that provides strong safety guarantees despite environment uncertainty. Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors. We demonstrate our approach in simulation and in hardware to provide safety guarantees around a state-of-the-art vision-based, learning-based planner.