基于混合专家模型的智能网联汽车换道决策方法

The problem of lane-changing decision-making on highways，characterized by complex scenari⁃ os，strong uncertainty，and high real-time requirements，is a research hotspot and challenge in the field of autono⁃ mous driving both domestically and internationally. Deep Reinforcement Learning（DRL）exhibits excellent real-time decision-making capabilities and adaptability to complex scenarios. However，under the constraints of limited training samples and cost，its learning effectiveness remains limited，making it difficult to ensure optimal driving effi⁃ ciency and complete driving safety. In this paper，a DRL-Mixture of Expert（DRL-MOE）lane-changing decision-making method based on the improved DRL model is proposed. Firstly，the upper-level classifier dynamically deter⁃ mines the activation status of the lower-level DRL expert or heuristic expert based on the input state features. Then，to enhance the learning effectiveness of the DRL expert，the method utilizes Behavior Cloning（BC）for initializing the neural network parameters to make improvements on the traditional Deep Deterministic Policy Gradient （DDPG）algorithm. Finally，the Intelligent Driver Model（IDM）and the strategy of Minimizing Overall Braking In⁃ duced by Lane changes（MOBIL）are designed as heuristic experts to ensure driving safety. The simulation results show that compared to non-mixed expert DRL methods，the proposed DRL-MOE model improves driving efficiency by 15.04%，ensuring zero collisions and zero departures，demonstrating higher robustness and superior performance.