Publication: Deep Reinforcement Learning for Vechicle Platooning Optimization
| dc.contributor.author | Amani Chaimaa Sellam | |
| dc.date.accessioned | 2024-12-09T08:22:44Z | |
| dc.date.available | 2024-12-09T08:22:44Z | |
| dc.date.issued | 2024 | |
| dc.description | Automated platooning, a cooperative driving pattern where a group of vehicles moves at a consensual speed while maintaining a small and nearly constant distance between adjacent vehicles, has gained increasing attention due to its potential to improve transportation systems. We explore the optimization of platooning on highways through the application of reinforcement learning techniques. Automated vehicle platooning control and optimization have great potential to revolutionize optimal control through the application of reinforcement learning techniques. Automated vehicles can learn to negotiate crossings, merge onto highways, navigate dynamic settings, and optimize their platooning behavior to alleviate congestion and enhance traffic efficiency by utilizing deep reinforcement learning algorithms (Peng et al., 2021). Additionally, automated vehicles may coordinate their actions and communicate with one another through the use of reinforcement learning. This allows the vehicles to adjust their speed and make real-time decisions, preventing traffic jams at intersections and ensuring efficient and smooth traffic flow. Vehicles can achieve long-term optimization and short-term constraint satisfaction by integrating robust model-free reinforcement learning with model-based techniques, particularly in modeling techniques for self-driving cars | |
| dc.description.abstract | 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 | |
| dc.identifier.uri | https://dspace.estin.dz/handle/123456789/18 | |
| dc.language.iso | en | |
| dc.publisher | ESTIN | |
| dc.title | Deep Reinforcement Learning for Vechicle Platooning Optimization | |
| dc.type | Thesis | |
| dspace.entity.type | Publication |
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