  Decision and control are core functionalities of high-level automated
vehicles. Current mainstream methods, such as functionality decomposition and
end-to-end reinforcement learning (RL), either suffer high time complexity or
poor interpretability and adaptability on real-world autonomous driving tasks.
In this paper, we present an interpretable and computationally efficient
framework called integrated decision and control (IDC) for automated vehicles,
which decomposes the driving task into static path planning and dynamic optimal
tracking that are structured hierarchically. First, the static path planning
generates several candidate paths only considering static traffic elements.
Then, the dynamic optimal tracking is designed to track the optimal path while
considering the dynamic obstacles. To that end, we formulate a constrained
optimal control problem (OCP) for each candidate path, optimize them separately
and follow the one with the best tracking performance. To unload the heavy
online computation, we propose a model-based reinforcement learning (RL)
algorithm that can be served as an approximate constrained OCP solver.
Specifically, the OCPs for all paths are considered together to construct a
single complete RL problem and then solved offline in the form of value and
policy networks, for real-time online path selecting and tracking respectively.
We verify our framework in both simulations and the real world. Results show
that compared with baseline methods IDC has an order of magnitude higher online
computing efficiency, as well as better driving performance including traffic
efficiency and safety. In addition, it yields great interpretability and
adaptability among different driving tasks. The effectiveness of the proposed
method is also demonstrated in real road tests with complicated traffic
conditions.
