人間とロボット車両のインタラクションにおける計画フレームワークへの到達可能性に基づく安全保証の導入
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
到達可能性解析を用いて、人間が運転する車両との衝突を避けつつ、上位プランナの軌道追従と安全確保を両立する100Hzの安全コントローラを開発し、実車の車線変更実験で危険な相手行動にも衝突回避できることを示した。
著者: Karen Leung, Edward Schmerling, Mengxuan Zhang, Mo Chen, John Talbot, J. Christian Gerdes, Marco Pavone
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
