  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.
