论文标题
使用对抗性道路模型的自动驾驶的安全运动计划
Safe Motion Planning for Autonomous Driving using an Adversarial Road Model
论文作者
论文摘要
本文介绍了一种游戏理论遵循的公式，对手是对手的道路模型。这种公式使我们能够使用可行性理论中的工具来计算安全集，这些工具可以用作基于优化的运动计划者中的终端约束。基于对手道路模型，我们首先得出一个分析区分域，甚至可以在考虑转向速率约束时保证安全性。其次，我们计算区分内核，并表明基于网格的算法的输出可以通过完全连接的神经网络准确地近似，这可以再次用作终端约束。最后，我们表明，通过使用我们提出的安全集合，基于优化的运动计划者可以成功地在预测范围的城市和乡村道路上行驶，无法完成其他基线，无法完成任务。
This paper presents a game-theoretic path-following formulation where the opponent is an adversary road model. This formulation allows us to compute safe sets using tools from viability theory, that can be used as terminal constraints in an optimization-based motion planner. Based on the adversary road model, we first derive an analytical discriminating domain, which even allows guaranteeing safety in the case when steering rate constraints are considered. Second, we compute the discriminating kernel and show that the output of the gridding based algorithm can be accurately approximated by a fully connected neural network, which can again be used as a terminal constraint. Finally, we show that by using our proposed safe sets, an optimization-based motion planner can successfully drive on city and country roads with prediction horizons too short for other baselines to complete the task.
