  Decision and control are two of the core functionalities of high-level
automated vehicles. Current mainstream methods, such as functionality
decomposition or end-to-end reinforcement learning (RL), either suffer high
time complexity or poor interpretability and limited safety performance in
real-world complex autonomous driving tasks. In this paper, we present an
interpretable and efficient decision and control framework for automated
vehicles, which decomposes the driving task into multi-path planning and
optimal tracking that are structured hierarchically. First, the multi-path
planning is to generate several paths only considering static constraints.
Then, the optimal tracking is designed to track the optimal path while
considering the dynamic obstacles. To that end, in theory, we formulate a
constrained optimal control problem (OCP) for each candidate path, optimize
them separately and choose the one with the best tracking performance to
follow. More importantly, we propose a model-based reinforcement learning (RL)
algorithm, which is served as an approximate constrained OCP solver, to unload
the heavy computation by the paradigm of offline training and online
application. Specifically, the OCPs for all paths are considered together to
construct a multi-task RL problem and then solved offline by our algorithm into
value and policy networks, for real-time online path selecting and tracking
respectively. We verify our framework in both simulation and the real world.
Results show that our method has better online computing efficiency and driving
performance including traffic efficiency and safety compared with baseline
methods. In addition, it yields great interpretability and adaptability among
different driving tasks. The real road test also suggests that it is applicable
in complicated traffic scenarios without even tuning.
