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

• Published in: IEEE transactions on control systems technology 2021-09, Vol.29 (5), p.1907-1921
• Main Authors: Ahn, Heejin, Berntorp, Karl, Inani, Pranav, Ram, Arjun Jagdish, Di Cairano, Stefano
• Format: Article
• Language: English
• Subjects: Autonomous driving; Autonomous vehicles; Decision making; Decision theory; Dynamic models; formal methods; Motion planning; Safety; Trajectories
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2020.3022721

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.