IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v640y2024ics037843712400181x.html
   My bibliography  Save this article

Expressway lane change strategy of autonomous driving based on prior knowledge and data-driven

Author

Listed:
  • Wang, Zhangu
  • Guan, Changming
  • Zhao, Ziliang
  • Zhao, Jun
  • Qi, Chen
  • Hui, Zilaing

Abstract

Automatic driving in expressways is generally considered to be the easiest for commercial landing, and vehicle behavior decision-making is the core of automatic driving technology, which directly affects the safety and comfort of vehicle driving. In this paper, an automatic lane change strategy based on prior knowledge and data-driven is proposed for expressway. Our method decouples autonomous driving decisions into two processes. Firstly, we propose an optimization model of lane change features based on prior knowledge, which not only provides a basis for the selection of vehicle features but also effectively reduces the interference of invalid features. The driving risk field is used to screen the vehicle targets that have potential influence on the lane change of the ego vehicle, and the maximal information coefficient evaluates the effectiveness of the features of the vehicle targets through the maximal information coefficient to optimize the lane change features. Then, a data-driven lane-changing model is proposed based on Attention-BiLSTM (Bi-directional Long Short-Term Memory), which transforms vehicle lane-changing into time series prediction, fully explores the relationship between lane-changing features in the context information, and effectively improves the anti-interference ability of the system against accidental errors. In addition, the attention mechanism can adaptively adjust the weight of lane-changing features and effectively capture the important information that needs attention in different lane-changing states, further improving the accuracy of the model. Finally, the validity of the model is verified by remote sensing data sets and real vehicle experiments. The test results show that the test accuracy of our method is 95.1% in data sets and 94.2% in real vehicle experiments, which fully meets the requirements of lane change decisions about automatic driving.

Suggested Citation

  • Wang, Zhangu & Guan, Changming & Zhao, Ziliang & Zhao, Jun & Qi, Chen & Hui, Zilaing, 2024. "Expressway lane change strategy of autonomous driving based on prior knowledge and data-driven," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    • Handle: RePEc:eee:phsmap:v:640:y:2024:i:c:s037843712400181x
    DOI: 10.1016/j.physa.2024.129672
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712400181X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129672?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kou, Yukang & Ma, Changxi, 2023. "Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    2. Zhu, Liling & Tang, Yandong & Yang, Da, 2021. "Cellular automata-based modeling and simulation of the mixed traffic flow of vehicle platoon and normal vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    3. Yu, Yuewen & Luo, Xia & Su, Qiming & Peng, Weikang, 2023. "A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    4. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    5. Ma, Yanli & Zhang, Peng & Hu, Baoyu, 2019. "Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Shi, Kunsong & Wu, Yuankai & Shi, Haotian & Zhou, Yang & Ran, Bin, 2022. "An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    7. Bray, Garrett & Cebon, David, 2022. "Operational speed strategy opportunities for autonomous trucking on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 75-94.
    8. Li, Dong & Ma, Changxi, 2022. "Research on lane change prediction model based on GBDT," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    9. Pei Gao & Ronggui Zhou & Qian Zhao & Jian Zhou & Xin Li & Weiyuan Peng, 2022. "Model and Approach of Solid Line Setting Based on Merge Safety and Merge Probability," Sustainability, MDPI, vol. 14(19), pages 1-14, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Yuning Wang & Shuocheng Yang & Jinhao Li & Shaobing Xu & Jianqiang Wang, 2023. "An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    4. Xu, Xinpeng & Yang, Chen & Wu, Weiguo, 2024. "Representation learning and Graph Convolutional Networks for short-term vehicle trajectory prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    5. Guo, Yinjia & Chen, Yanyan & Gu, Xin & Guo, Jifu & Zheng, Shuyan & Zhou, Yuntong, 2024. "Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    6. Sun, Baofeng & Ma, Guodong & Song, Jia & Cheng, Zeyang & Wang, Wei, 2023. "Driving safety field modeling focused on heterogeneous traffic flows and cooperative control strategy in highway merging zone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Ruru Hao & Tiancheng Ruan, 2024. "Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks," Sustainability, MDPI, vol. 16(7), pages 1-16, March.
    8. Hossein Samadi & Iman Aghayan & Khaled Shaaban & Farhad Hadadi, 2023. "Development of Performance Measurement Models for Two-Lane Roads under Vehicular Platooning Using Conjugate Bayesian Analysis," Sustainability, MDPI, vol. 15(5), pages 1-26, February.
    9. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    10. Ma, Yanli & Lv, Zhiliang & Zhang, Peng & Chan, Ching-Yao, 2021. "Impact of lane changing on adjacent vehicles considering multi-vehicle interaction in mixed traffic flow: A velocity estimating model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    11. Bray, Garrett & Cebon, David, 2022. "Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles: An assessment of potential for change," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    12. Wang, Tianshi & Lu, Huapu & Sun, Zhiyuan & Wang, Jianyu, 2023. "Towards higher efficiency and less consumption: Control Strategy and Simulation for CAV platooning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    13. Li, Linheng & Wang, Can & Zhang, Ying & Qu, Xu & Li, Rui & Chen, Zhijun & Ran, Bin, 2022. "Microscopic state evolution model of mixed traffic flow based on potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    14. Song Fang & Linghong Shen & Jianxiao Ma & Chubo Xu, 2022. "Study on the Design of Variable Lane Demarcation in Urban Tunnels," Sustainability, MDPI, vol. 14(9), pages 1-12, May.
    15. Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    16. Khelfa, Basma & Ba, Ibrahima & Tordeux, Antoine, 2023. "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    17. He, Z.C. & Kang, H. & Li, Eric & Zhou, E.L. & Cheng, H.T. & Huang, Y.Y., 2022. "Coordinated control of heterogeneous vehicle platoon stability and energy-saving control strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    18. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    19. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    20. Wang, Zhengwu & Chen, Tao & Wang, Yi & Li, Hao, 2024. "A cellular automaton model for mixed traffic flow considering the size of CAV platoon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:640:y:2024:i:c:s037843712400181x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.