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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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    1. Li, Tienan & Chen, Danjue & Zhou, Hao & Laval, Jorge & Xie, Yuanchang, 2021. "Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 67-91.
    2. Guo, Lantian & Zhao, Xiangmo & Yu, Shaowei & Li, Xiuhai & Shi, Zhongke, 2017. "An improved car-following model with multiple preceding cars’ velocity fluctuation feedback," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 436-444.
    3. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2018. "An extended car-following model under V2V communication environment and its delayed-feedback control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 349-358.
    4. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2018. "Stability analysis methods and their applicability to car-following models in conventional and connected environments," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 212-237.
    5. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
    6. Zhang, Jing & Xu, Keyu & Li, Guangyao & Li, Shubin & Wang, Tao, 2021. "Dynamics of traffic flow affected by the future motion of multiple preceding vehicles under vehicle-connected environment: Modeling and stabilization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
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    2. 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.
    3. 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.

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