We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.
翻译:我们提出了一种新的混合学习方法,即HyLEAR,用于解决POMDPs汽车自行驾驶汽车的无碰撞导航问题。 HyLEAR的杠杆作用是将混合计划者的知识嵌入深层强化学习者,以更快地确定安全和舒适的驾驶政策。 特别是,混合计划者将行人路径预测和风险意识路径规划与驾驶行为规则推理结合起来,这样,驾驶政策也尽可能考虑到乘车的舒适度和一套特定驾驶行为规则。 我们对CARLA-CTS1关键交通情况基准的实验性业绩分析显示,HyLEAR在安全和驾驶舒适方面可以大大超过选定的基线。