LAUSR

With the development of AI, the intelligence level of vehicles is increasing. Structured roads, as common and important traffic scenes, are the most typical application scenarios for realizing autonomous driving. The driving behavior decision-making of intelligent vehicles has always been a controversial and difficult research topic. Currently, the mainstream decision-making methods, which are mainly based on rules, lack adaptability and generalization to the environment. Aimed at the particularity of intelligent vehicle behavior decisions and the complexity of the environment, this thesis proposes an intelligent vehicle driving behavior decision method based on DQN generative adversarial imitation learning (DGAIL) in the structured road traffic environment, in which the DQN algorithm is utilized as a GAIL generator. The results show that the DGAIL method can preserve the design of the reward value function, ensure the effectiveness of training, and achieve safe and efficient driving on structured roads. The experimental results show that, compared with A3C, DQN and GAIL, the model based on DGAIL spends less average training time to achieve a 95% success rate in the straight road scene and merging road scene, respectively. Apparently, this algorithm can effectively accelerate the selection of actions, reduce the randomness of actions during the exploration, and improve the effect of the decision-making model.