  Learning-based driving solution, a new branch for autonomous driving, is
expected to simplify the modeling of driving by learning the underlying
mechanisms from data. To improve the tactical decision-making for
learning-based driving solution, we introduce hierarchical behavior and motion
planning (HBMP) to explicitly model the behavior in learning-based solution.
Due to the coupled action space of behavior and motion, it is challenging to
solve HBMP problem using reinforcement learning (RL) for long-horizon driving
tasks. We transform HBMP problem by integrating a classical sampling-based
motion planner, of which the optimal cost is regarded as the rewards for
high-level behavior learning. As a result, this formulation reduces action
space and diversifies the rewards without losing the optimality of HBMP. In
addition, we propose a sharable representation for input sensory data across
simulation platforms and real-world environment, so that models trained in a
fast event-based simulator, SUMO, can be used to initialize and accelerate the
RL training in a dynamics based simulator, CARLA. Experimental results
demonstrate the effectiveness of the method. Besides, the model is successfully
transferred to the real-world, validating the generalization capability.
