可达性
杠杆(统计)
计算机科学
规划师
预测(人工智能)
弹道
碰撞
主动安全
避碰
控制器(灌溉)
概率逻辑
模拟
控制工程
工程类
汽车工程
计算机安全
人工智能
天文
物理
生物
理论计算机科学
农学
作者
Karen Leung,Edward Szczerbicki,Mo Chen,John M. Talbot,J. Christian Gerdes,Marco Pavone
出处
期刊:Springer proceedings in advanced robotics
日期:2020-01-01
卷期号:: 561-574
被引量:11
标识
DOI:10.1007/978-3-030-33950-0_48
摘要
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.
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