Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones

Zhimian Chen, Yizeng Wang, Hao Hu (), Zhipeng Zhang, Chengwei Zhang and Shukun Zhou
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Zhimian Chen: School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Yizeng Wang: School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Hao Hu: State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Zhipeng Zhang: State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Chengwei Zhang: School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Shukun Zhou: Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250014, China

Mathematics, 2024, vol. 12, issue 23, 1-22

Abstract: The rapid development of autonomous driving technology is widely regarded as a potential solution to current traffic congestion challenges and the future evolution of intelligent vehicles. Effective driving strategies for autonomous vehicles should balance traffic efficiency with safety and comfort. However, the complex driving environment at highway entrance ramp merging areas presents a significant challenge. This study constructed a typical highway ramp merging scenario and utilized deep reinforcement learning (DRL) to develop and regulate autonomous vehicles with diverse driving strategies. The SUMO platform was employed as a simulation tool to conduct a series of simulations, evaluating the efficacy of various driving strategies and different autonomous vehicle penetration rates. The quantitative results and findings indicated that DRL-regulated autonomous vehicles maintain optimal speed stability during ramp merging, ensuring safe and comfortable driving. Additionally, DRL-controlled autonomous vehicles did not compromise speed during lane changes, effectively balancing efficiency, safety, and comfort. Ultimately, this study provides a comprehensive analysis of the potential applications of autonomous driving technology in highway ramp merging zones under complex traffic scenarios, offering valuable insights for addressing these challenges effectively.

Keywords: autonomous driving; ramp merging zones; mixed traffic flow; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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