Deep Reinforcement Learning For Vehicule Platooning Optimization

Résumé: Automated vehicle platooning has emerged as a significant method for improvi traffic efficiency, reducing fuel consumption, and enhancing road safety. This investigates the optimization of vehicle platooning using reinforcement learning techniques, specifically focusing on Deep Q-Networks (DQN) integrated with dueli networks and prioritized experience replay (PER). A two-layered approach is employed, where the first layer identifies joinable platoons and evaluates the benef 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, a improves the overall safety of the platooning process. Future work includes extending the model to mixed traffic environments with both autonomous and human-dri vehicles, as well as integrating predictive traffic models

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