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Driving strategy of connected and autonomous vehicles based on multiple preceding vehicles state estimation in mixed vehicular traffic

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  • Ding, Heng
  • Pan, Hao
  • Bai, Haijian
  • Zheng, Xiaoyan
  • Chen, Jin
  • Zhang, Weihua

Abstract

In the near future, connected and autonomous vehicles (CAVs) will share road space with human-driven vehicles (HVs). In this mixed vehicular traffic, effective following cooperation among multiple vehicles is an important basis for improving traffic efficiency and safety. However, CAVs are unable to communicate with HVs to acquire information. Therefore, how to obtain HV information and realize cooperative car-following has become an urgent problem for CAVs. This paper proposes a CAV driving strategy that considers multiple preceding vehicles, including HVs. The strategy first uses a large amount of real car-following data to build an upgraded Elman neural network (ENN) model optimized with the sparrow search algorithm (SSA), which is utilized to obtain HV information. Then, we combine the SSA-ENN with the classical car-following model and use a time-varying weighting model to analyze the impact of the different states of multiple preceding cars at various moments on the host car, so as to achieve car-following driving control. Numerical simulations are carried out, and the results show that the driving strategy can improve road capacity and suppress traffic oscillations. With the increase in CAV penetration, traffic efficiency, safety, and driving comfort are improved accordingly.

Suggested Citation

  • Ding, Heng & Pan, Hao & Bai, Haijian & Zheng, Xiaoyan & Chen, Jin & Zhang, Weihua, 2022. "Driving strategy of connected and autonomous vehicles based on multiple preceding vehicles state estimation in mixed vehicular traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001686
    DOI: 10.1016/j.physa.2022.127154
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    References listed on IDEAS

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    Cited by:

    1. Qi, Weiwei & Ma, Siwei & Fu, Chuanyun, 2023. "An improved car-following model considering the influence of multiple preceding vehicles in the same and two adjacent lanes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    2. Li, Yun & Zhang, Wenshan & Zhang, Shengrui & Pan, Yingjiu & Zhou, Bei & Jiao, Shuaiyang & Wang, Jianpo, 2024. "An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    3. Li, Jing & Liu, Di & Baldi, Simone, 2024. "Modular nudging models: Formulation and identification from real-world traffic data sets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    4. Ding, Heng & Zhang, Lang & Chen, Jin & Zheng, Xiaoyan & Pan, Hao & Zhang, Weihua, 2023. "MPC-based dynamic speed control of CAVs in multiple sections upstream of the bottleneck area within a mixed vehicular environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    5. Andrea Gemma & Tina Onorato & Stefano Carrese, 2023. "Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
    6. Matija Kovačić & Maja Mutavdžija & Krešimir Buntak, 2022. "New Paradigm of Sustainable Urban Mobility: Electric and Autonomous Vehicles—A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-23, August.

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