Research on Intelligent Merging Decision-making of Unmanned Vehicles Based on Reinforcement Learning

The decision-making model of merging behavior is one of the key technologies of unmanned vehicles. In order to solve the problem of unmanned vehicles' merging decision making, this paper presents a merging strategy based on Least squares Policy Iteration (LSPI) algorithm, and selects the basis function which includes reciprocal of TTC, relative distance and relative speed to represent state space and discretizes action space. This study synthetically takes consideration o safety, the success of the task, the merging efficiency and comfort in setting reward function, compares the Q-learning with LSPI algorithm, and verifies its adaptability by using NGSIM data. The algorithm can ultimately achieve a success rate of 86%. This research can provide theoretic support and technical basis for the merging decision-making of unmanned vehicles.