面向复杂交通场景的自动驾驶运动规划模型OA北大核心CSTPCD

To address the problems that existing autonomous driving motion planning methods fail to effectively utilize the long-term continuous time features and the problem of low success rate in complex traffic scenarios,a Transformer-based autonomous driving motion planning model for complex traffic scenes is proposed.The method uses GPT-2 as the base model,and through temporal modeling of offline reinforcement learning,it can effectively characterize the dependen-cies of the state,action,and reward data of vehicles in the offline reinforcement learning model over a long period of time,allowing the model to effectively learn more from historical planning data and improve the accuracy and safety in complex traffic scenarios.The experiments are simulated and tested in the MetaDrive simulator,and the results show that the model has achieved a success rate of up to 93%in complex traffic scenarios such as merging into main roads and entering traffic circles,which are 20,19,and 13 percentage points higher than the success rates of the existing state-of-the-art method including behavior cloning,batch-constrained deep Q-learning(BCQ),and twin delayed deep deterministic policy gradient with behavioral cloning(TD3+BC),respectively.This indicates that the proposed method is more effec-tive to learn driving strategies from low quality datasets and with better generalization performance and robustness com-pared with other comparative methods.