  Autonomous vehicles need to handle various traffic conditions and make safe
and efficient decisions and maneuvers. However, on the one hand, a single
optimization/sampling-based motion planner cannot efficiently generate safe
trajectories in real time, particularly when there are many interactive
vehicles near by. On the other hand, end-to-end learning methods cannot assure
the safety of the outcomes. To address this challenge, we propose a
hierarchical behavior planning framework with a set of low-level safe
controllers and a high-level reinforcement learning algorithm (H-CtRL) as a
coordinator for the low-level controllers. Safety is guaranteed by the
low-level optimization/sampling-based controllers, while the high-level
reinforcement learning algorithm makes H-CtRL an adaptive and efficient
behavior planner. To train and test our proposed algorithm, we built a
simulator that can reproduce traffic scenes using real-world datasets. The
proposed H-CtRL is proved to be effective in various realistic simulation
scenarios, with satisfying performance in terms of both safety and efficiency.
