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 the proposed approach in simulation and on a hardware test-bed to provide safety guarantees around a state-of-the-art vision-based, learning-based planner.

Bajcsy, Bansal, Bronstein, Tolani, Tomlin An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments

Simulated Sensors and Planners. The vehicle trajectories for the problem setting for both planners (RRT and Spline planners) and both sensors (LiDAR and Camerasensors) with the safety controller computed from each of the three candidate safety approaches. The proposed framework is able to safely navigate the vehicle to the goal in all cases. When the planner makes unsafe decisions,the safety controller intervenes (the states marked in red) to ensure safety.

We test the proposed approach in hardware using a TurtleBot 2 with a mounted stereo RGB camera. For planning, we use a state-of-the-art neural-network-based planner that uses the current RGB image to determine a candidate next state. We ran the experiment with and without the safety controller and show corresponding videos below. Without the safety controller, the learning-based planner struggles with making sharp turns near the corner, and eventually collides into the obstacle (the chair, in this case). However, when the learning-based planner is used within the proposed safety framework, the safety controller is able to account for this unsafe situation and safely steer the vehicle away from the obstacle.