We present a novel hybrid learning method, named 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 of the car. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the determined safe trajectories also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.
HyLEAR: Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving
04.06.2023
3771999 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving
ArXiv | 2022
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