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首页> 外文期刊>The International journal of robotics research >On infusing reachability-based safety assurance within planning frameworks for human-robot vehicle interactions
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On infusing reachability-based safety assurance within planning frameworks for human-robot vehicle interactions

机译：在人体机器人车辆相互作用的规划框架内输注基于可达性的安全保证

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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 article 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 (100 Hz) 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.
机译：行动预期，意图预测和主动行为是交互式方案中自主驾驶策略的所有理想特征。然而，至关重要的是确保在路上的安全：这样做的关键挑战是在没有过度影响计划人员的情况下占人类驾驶员行动的不确定性。本文介绍了一种在自主车辆控制堆栈内运行的微小介入安全控制器，其作用是确保无静电相互作用，同时尊重诸如道路边界壁的静态障碍物。我们利用可达性分析来构建一个实时（100Hz）控制器，该控制器用于使用模型预测控制从高级计划算法跟踪输入轨迹的（Ⅰ），并通过维持可用性来确保安全性无论其他车所需的任何未来行动如何，无论是持续的约束，无论如何，无抵抗的逃生操纵。全尺寸的转向绕线平台用于进行交通编织实验，其中两辆车最初并排，必须在有限的时间和距离中换流车道，汇编符合高速公路/关闭的汽车。我们证明，通过我们的控制堆栈，即使其他汽车遵守规划师的期望，并且不经意地或者意图碰撞，也能够避免碰撞，避免碰撞，或者违背碰撞，并且否则从计划的轨迹最小地偏离维持安全所需的程度。

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