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.

提出了一种名为 HyLEAR 的新型混合学习方法，用于在 POMDP 中解决自动驾驶汽车的无碰撞导航问题。HyLEAR 利用中介学习将混合规划器的知识嵌入到深度强化学习器中，以更快速地确定安全舒适的驾驶策略。实验结果表明，HyLEAR 在关键交通情景下的安全性和舒适性方面显著优于其他基准算法。