Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions limit the vehicle's perception ability, reasoning about potential hazards beyond the field of view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer's state is initially unknown to the evader and may later be discovered by the vehicle's sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework for autonomous driving that provides worst-case safety guarantees while minimizing conservativeness by accounting for the vehicle's ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator.
翻译:与其他交通参与者互动的自治车辆在很大程度上依赖对其它代理人行为的认识和预测来规划安全轨迹。然而,由于隔离限制了车辆的感知能力,因此将潜在危险推入视野之外是最具有挑战性的问题之一。本文介绍了一种新的分析方法,在隔离下将安全轨迹规划纳入安全轨迹规划,作为自主车辆(蒸发)和最初隐藏的交通参与者(Pursuer)之间的混合零和动态游戏。由于隔离,追赶者的状况最初为躲避者所不知,后来可能由车辆传感器发现。分析为两个玩家制定了最佳战略,并为保障自动车辆避免碰撞的最初条件组合提供了最佳战略。我们利用这一理论结果为自主驾驶制定新的轨迹规划框架,提供最坏的安全保障,同时通过计算车辆在未来观测中能够积极避免其他道路使用者时的保守性来尽量减少。我们的框架对驾驶环境具有怀疑性,并适合各种运动规划者。我们用具有挑战性的城市和汽车的动力演算系统,我们用具有挑战性的城市和汽车演算法。