Deep Reinforcement Learning For Vechicle Platooning Optimization

Résumé: Automated vehicle platooning has emerged as a significant method for improving traffic efficiency, reducing fuel consumption, and enhancing road safety. This thesis investigates the optimization of vehicle platooning using reinforcement learning techniques, specifically focusing on Deep Q-Networks (DQN) integrated with dueling networks and prioritized experience replay (PER). A two-layered approach is employed, where the first layer identifies joinable platoons and evaluates the benefits of joining them, and the second layer uses reinforcement learning to optimize merging, lane-changing, and acceleration strategies. The results of the simulation demonstrate that the proposed method significantly reduces travel time, fuel consumption, and improves the overall safety of the platooning process. Future work includes extending the model to mixed traffic environments with both autonomous and human-driven vehicles, as well as integrating predictive traffic models. Keywords: Automated vehicle platooning, Deep Q-Networks, Dueling networks, Prioritized experience replay, Reinforcement learning, Traffic efficiency, Fuel

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