An actor-critic based learning method for decision-making and planning of autonomous vehicles

In order to improve the agility and applicability of trajectory planning algorithm for autonomous vehicles, this paper proposes a novel actor-critic based learning method for decision-making and planning in multi-vehicle complex traffic. It is the coupling planning of vehicle’s path and speed thus to make the trajectory more flexible. First, generations from the decided action to the planned trajectory are described by the end-point of the trajectory. Then, the actor-critic based learning method is built to learn an optimal policy for the decision process. It can update the policy by the gradient of the current policy’s advantage. In this process, features of the real traffic are carefully extracted by time headway (TH) and speed distribution. Reward function is built by the safety, efficiency and driving comfort. Furthermore, to make the policy network have better convergency, the policy network is modularized in two parts: the lane-changing network and the lane-keeping network, which decide the optimal end-point of the path and speed candidates respectively. Finally, the curved overtaking scenario and the interaction process with human driver are conducted to illustrate the feasibility and superiority. The results show that the proposed method has better real-time performance and can make the planned coupling trajectory more continuous and smoother than the existing rule-based method.