A fuzzy-inference-based reinforcement learning method of overtaking decision making for automated vehicles

Intelligent decision control is one key issue of automated vehicles. Complex dynamic traffic flow and multi-requirement of passengers including vehicle safety, comfort, vehicle efficiency bring about tremendous challenges to vehicle decision making. Overtaking maneuver is a demanding task due to its large potential of traffic collision. Therefore, this paper proposes a fuzzy-inference-based reinforcement learning (FIRL) approach of autonomous overtaking decision making. Firstly, the problem of overtaking is formulated as a multi-objective Markov decision process (MDP) considering vehicle safety, driving comfort, and vehicle efficiency. Secondly, a temporal difference learning based on dynamic fuzzy (DF-TDL) is presented to learn optimized policies for autonomous overtaking decision making. Fuzzy inference is introduced to deal with continuous states and boost learning process. The RL algorithm decides whether to overtake or not based on the learned policies. Then, the automated vehicle executes local path planning and tracking. Furthermore, a simulation platform based on simulation of urban mobility (SUMO) is established to generate the random training data, that is, various traffic flows for algorithm iterative learning and validate the proposed method, extensive test results demonstrate the effectiveness of the overtaking decision-making method.

Automated summary

This paper proposes a fuzzy-inference-based reinforcement learning approach for autonomous overtaking decision making in automated vehicles, considering various factors such as vehicle safety, driving comfort, and vehicle efficiency, and validating the effectiveness of the method on a simulation platform.