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Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhimian Chen & Yizeng Wang & Hao Hu & Zhipeng Zhang & Chengwei Zhang & Shukun Zhou, 2024. "Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones," Mathematics, MDPI, vol. 12(23), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3859-:d:1539108
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    References listed on IDEAS

    as
    1. Wei Jiang & Zhishun Huang & Zonghao Wu & Huiyuan Su & Xiangping Zhou, 2022. "Quantitative Study on Human Error in Emergency Activities of Road Transportation Leakage Accidents of Hazardous Chemicals," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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