HyLEAR: Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving.
2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV(2023)
Abstract
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.
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Key words
car,collision-free navigation problem,comfortable driving policies,deep reinforcement learner,determined safe trajectories,driving-behavior rule-based reasoning,driving-behavior rules,embed knowledge,hybrid deep reinforcement learning,hybrid planner,HyLEAR leverages,named HyLEAR,novel hybrid learning method,pedestrian path prediction,ride comfort,risk-aware path planning,safe driving policies
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