Safety guarantees in motion planning for autonomous driving typically involve certifying the trajectory to be collision-free under any motion of the uncontrollable participants in the environment, such as the human-driven vehicles on the road. As a result they usually employ a conservative bound on the behavior of such participants, such as reachability analysis. We point out that planning trajectories to rigorously avoid the entirety of the reachable regions is unnecessary and too restrictive, because observing the environment in the future will allow us to prune away most of them; disregarding this ability to react to future updates could prohibit solutions to scenarios that are easily navigated by human drivers. We propose to account for the autonomous vehicle's reactions to future environment changes by a novel safety framework, Comprehensive Reactive Safety. Validated in simulations in several urban driving scenarios such as unprotected left turns and lane merging, the resulting planning algorithm called Reactive ILQR demonstrates strong negotiation capabilities and better safety at the same time.
翻译:在自动驾驶的机动规划中,安全保障通常涉及核证轨道在无法控制的环境参与者的任何动作下是无碰撞的,例如公路上的人驾驶车辆,因此,他们通常采用保守的办法来约束这些参与者的行为,例如可达性分析。我们指出,为严格避免整个可达区域而规划的轨迹是不必要的,限制性过强,因为今后观测环境将使我们能够缩小大部分区域;无视这种对未来更新作出反应的能力,就可能禁止对人驾驶员容易导航的情景采取解决办法。我们提议用一个新的安全框架“全面反应安全”来说明自主车辆对未来环境变化的反应。在诸如无防护左转弯和车道合并等若干城市驾驶情景的模拟中加以验证,由此产生的规划算法“雷动性ILQR”显示强大的谈判能力和更好的安全性。