Research on Decision-Making at Intersection Without Traffic Lights Based on Deep Reinforcement Learning
Left-turn intersections without signal lights are among the most dangerous scenes in autonomous driving,and achieving efficient and safe left-turn decision-making is highly challenging in autonomous driving.The Deep Reinforcement Learning(DRL)algorithm has broad prospects in autonomous driving decision-making.However,its sample efficiency is low and it cannot be used to easily design reward functions in autonomous driving.Therefore,a DRL algorithm based on expert priors,abbreviated as CBAM-BC SAC,is proposed to solve the aforementioned problems.First,a Scalable Multiagent RL Training School(SMARTS)simulation platform is used to obtain expert prior knowledge.Subsequently,a Convolutional Block Attention Module(CBAM)is used to improve Behavior Cloning(BC),which pretrains and imitates expert strategies based on the prior knowledge of experts.Finally,the learning process of the DRL algorithm is guided by an imitation expert strategy and verified in a left-turn decision-making at intersection without traffic lights.Experimental results indicate that the DRL algorithm based on expert prior is more advantageous than conventional DRL algorithms.It not only eliminates the workload of manually setting reward functions,but also significantly improves sample efficiency and achieves better performance.In left-turn scene at intersection without traffic lights,the CBAM-BC SAC algorithm improves the average traffic success rate by 14.2 and 2.2 percentage points,respectively,compared with the conventional DRL algorithm SAC and the DRL algorithm BC SAC based on classic BC.
Deep Reinforcement Learning(DRL)autonomous drivingimitation learningBehavioral Cloning(BC)driving decision-making
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