The application of Deep Reinforcement Learning(DRL)in intelligent driving decision-making is increasingly widespread,as it effectively enhances decision-making capabilities through continuous interaction with the environment.However,DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security.To address these issues,a Directed Acyclic Graph(DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization(D-IDSO)algorithm is proposed.First,a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing.Next,a DRL-based intelligent driving decision model is designed,incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety,comfort,and efficiency.Additionally,an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient(IPER-TD3)method is proposed to enhance training efficiency.Finally,braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles(CAVs).Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios,while ensuring data security and enhancing the safety,comfort,and efficiency of intelligent driving.
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