- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YANG Da, FENG Tingwei, ZHONG Jiayue, ZHENG Bin, YANG Guo. Lane Selection of Automated Vehicle Groups Approaching Intersections Based on Vehicle–Infrastructure Cooperation[J]. Journal of Southwest Jiaotong University, 2025, 60(5): 1250-1258, 1314. doi: 10.3969/j.issn.0258-2724.20230216
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In front of signalized intersections, frequent lane-changing and turning maneuvers often lead to conflict and reduced traffic efficiency. To address this issue, a shared deep Q-network (DQN)-based reinforcement learning framework was developed for vehicle group control, aiming to optimize lane selection. Firstly, real-time state information on surrounding vehicles and intersection signal lights was obtained using sensing and connected devices. Lane selection was then carried out based on the shared DQN model, and the vehicle’s next position, speed, and steering angle were calculated accordingly. A reward function incorporating efficiency and safety indicators was then constructed to evaluate lane selection decisions. The state, decision, and reward evaluation information were integrated into experience and stored in a shared experience pool to iteratively update the parameters of the shared DQN model. Finally, simulation of urban mobility (SUMO) and Python were used to simulate different traffic scenarios to verify the trained model. Experimental results show that, compared with the lane selection model in SUMO, the proposed shared DQN-based lane selection model for vehicle groups approaching signalized intersections improves average speeds in low, medium, and high traffic scenarios, while reducing queue lengths before intersections by 9.6%, 22.5%, and 24.8%, respectively. The model can effectively reduce the queue length at signal intersections, increase average speeds on road sections before signalized intersections, and improve the efficiency of vehicles arriving at the intersection from upstream, providing a theoretical reference and technical support for future application of vehicle–infrastructure cooperation.
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