On Infusing Reachability-Based Safety Assurance Within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions
| Autor: | J. Christian Gerdes, John Talbot, Karen Leung, Mo Chen, Marco Pavone, Edward Schmerling |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science 010401 analytical chemistry Control (management) Probabilistic logic Control engineering 02 engineering and technology 01 natural sciences Human–robot interaction 0104 chemical sciences Model predictive control 020901 industrial engineering & automation Control theory Reachability Anticipation (artificial intelligence) Safety assurance |
| Zdroj: | Springer Proceedings in Advanced Robotics ISBN: 9783030339494 ISER |
| DOI: | 10.1007/978-3-030-33950-0_48 |
| Popis: | 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. We leverage reachability analysis to construct a real-time (100 Hz) controller that serves the dual role of (1) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (2) assuring safety through 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 the 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. |
| Databáze: | OpenAIRE |
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