On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
This work addresses the critical problem of ensuring safety for autonomous vehicles in interactive scenarios with human drivers, particularly when human actions are uncertain or dangerous, which is a significant concern for the deployment of AV technology.
This paper introduces a minimally-interventional safety controller for autonomous vehicles that ensures collision-free interaction with human-driven cars and static obstacles. The controller leverages reachability analysis to track a high-level planning trajectory while maintaining the availability of a collision-free escape maneuver, even when the other car takes dangerous actions. Experiments on a full-scale steer-by-wire platform demonstrate the autonomous vehicle's ability to avoid collisions in traffic weaving scenarios, deviating minimally from the planned trajectory only when necessary for safety.
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road -- a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This paper introduces a minimally-interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart while respecting static obstacles such as a road boundary wall. We leverage reachability analysis to construct a real-time (100Hz) controller that serves the dual role of (i) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (ii) assuring safety by maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic weaving experiments wherein two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner's expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety.