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A model of lane-changing intention induced by deceleration frequency in an automatic driving environment

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  • Wang, Lichao
  • Yang, Min
  • Li, Ye
  • Hou, Yiqi

Abstract

Achieving high-level, anthropomorphic lane-changing control for autonomous vehicles is an important method for promoting the safe and efficient operation of autonomous vehicles. According to existing lane-changing models, the important phenomenon that frequent acceleration and deceleration cause a poor riding experience and thus induce the intention of lane-changing needs to be given more attention. In this study, a discretionary lane-changing control method for autonomous vehicles is designed based on anti-interference ability (AIA, where AIA is the number of decelerations per unit time of an autonomous vehicle) to extract the traffic flow variation characteristics of autonomous vehicles with AIA and to determine methods to improve the driving efficiency and stability of autonomous vehicles. AIA is introduced to the lane-changing decision-making process of autonomous vehicles to simulate the phenomenon of lane-changing intention caused by poor riding experience. A control model that can evaluate the lane-changing conditions is then proposed. NetLogo software was selected to construct a one-way, two-lane scenario for autonomous vehicle operation, and the impacts of the proposed decision-making method in each scenario were tested. The experimental results show that autonomous vehicles are more likely to produce frequent deceleration behaviors under large-traffic and small-space operating conditions. A traffic flow composed of autonomous vehicles that use AIA to induce lane-changing intentions has a more obvious phase transition process. An autonomous vehicle whose AIA induces lane-changing intention can improve the speed and stability for a certain traffic volume (a saturation of less than approximately 0.45). Although lane-changing behavior breaks the stable state of two-lane traffic flow, it will also successfully suppress the aggravation of traffic congestion. Lane-changing has an impact on the local oscillation of traffic flow, and a stable traffic flow operation state and reduction in the AIA clearing frequency are mutually reinforcing.

Suggested Citation

  • Wang, Lichao & Yang, Min & Li, Ye & Hou, Yiqi, 2022. "A model of lane-changing intention induced by deceleration frequency in an automatic driving environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005799
    DOI: 10.1016/j.physa.2022.127905
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    1. Shinji Kukida & Jun Tanimoto & Aya Hagishima, 2011. "Analysis Of The Influence Of Lane Changing On Traffic-Flow Dynamics Based On The Cellular Automaton Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 271-281.
    2. Lyu, Zelin & Hu, Xiaojian & Zhang, Fang & Liu, Tenghui & Cui, Zhiwei, 2022. "Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    3. Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    4. Vranken, Tim & Schreckenberg, Michael, 2022. "Modelling multi-lane heterogeneous traffic flow with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    5. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    6. Keller, Hartmut, 1994. "The German part in European research programmes PROMETHEUS and DRIVE/ATT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 28(6), pages 483-493, November.
    7. Oh, Simon & Yeo, Hwasoo, 2015. "Impact of stop-and-go waves and lane changes on discharge rate in recovery flow," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 88-102.
    8. Zeng, Jun-Wei & Qian, Yong-Sheng & Yu, Sen-Bin & Wei, Xu-Ting, 2019. "Research on critical characteristics of highway traffic flow based on three phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 530(C).
    9. Xiong, Bang-Kai & Jiang, Rui & Tian, Jun-Fang, 2019. "Improving two-dimensional intelligent driver models to overcome overly high deceleration in car-following," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    10. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    11. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish & Haque, Md. Mazharul, 2019. "Modelling car-following behaviour of connected vehicles with a focus on driver compliance," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 256-279.
    12. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    13. Xi-Qun Chen & Wei-Jun Xie & Jing Shi & Qi-Xin Shi, 2010. "Perturbation And Stability Analysis Of The Multi-Anticipative Intelligent Driver Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 647-668.
    14. Chauhan, Prajwal & Kanagaraj, Venkatesan & Asaithambi, Gowri, 2022. "Understanding the mechanism of lane changing process and dynamics using microscopic traffic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    15. Li, Xiang & Sun, Jian-Qiao, 2017. "Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 41-58.
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