Reachability-Based Decision-Making for Autonomous Driving: Theory and Experiments

Abstract

We describe the design and validation of a decision-making system in the guidance and control architecture for automated driving. The decision-making system determines the timing of transitions through a sequence of driving modes, such as lane following and stopping, for the vehicle to eventually arrive at the destination without colliding with obstacles, hence achieving safety and liveness. The decision-making system commands a transition to the next mode only when it is possible for an underlying motion planner to generate a feasible trajectory that reaches the target region of such next mode. Using forward and backward reachable sets based on a simplified dynamical model, the decision-making system determines the existence of a trajectory that reaches the target region, without actually computing it. Thus, the decision-making system achieves fast computation, resulting in reactivity to a varying environment and reduced computational burden. To handle the discrepancy between the dynamical model and the actual vehicle motion, we model it as a bounded disturbance set and guarantee robustness against it. We prove the safety and liveness of the decision-making system and validate it using small-scale car-like robots.