论文标题
自动驾驶的基于目标的预测和计划
Interpretable Goal-based Prediction and Planning for Autonomous Driving
论文作者
论文摘要
我们提出了一种用于自动驾驶的综合预测和计划系统，该系统使用合理的反计划来识别其他车辆的目标。目标识别会告知蒙特卡洛树搜索（MCTS）算法，以计划自我车辆的最佳操作。逆计划和MCT使用共享的一组定义的操作和宏观动作来构建可以通过理性原则来解释的计划。在城市驾驶场景的模拟中进行的评估表明，该系统能够稳健地认识其他车辆的目标，从而使我们的车辆能够利用非平凡的机会来大大减少行驶时间。在每种情况下，我们为预测系统的决策合理的预测提取直观的解释。
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system's decisions.
