Deep Reinforcement learning for vehicule Platooning Optimization

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dc.contributor.authorAmani Chaimaa SELLAM
dc.date.accessioned2025-01-21T21:31:43Z
dc.date.available2025-01-21T21:31:43Z
dc.date.issued2025-01-21
dc.description.abstractAutomated 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
dc.identifier.urihttps://dspace.estin.dz/handle/123456789/34
dc.language.isoen
dc.publisherTassadit
dc.subjectAutomated vehicle platooning
dc.subjectDeep Q-Networks
dc.subjectDueling networks
dc.subjectPrioritized experience replay
dc.subjectReinforcement learning
dc.subjectTraffic efficiency consumption
dc.subjectRoad safety
dc.subjectMerging strategies
dc.subjectMixed traffic environments
dc.titleDeep Reinforcement learning for vehicule Platooning Optimization
dc.typeThesis

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