IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v617y2023ics0378437123002200.html
   My bibliography  Save this article

Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization

Author

Listed:
  • Kou, Yukang
  • Ma, Changxi

Abstract

With the diversification and deepening of intelligent technology research, intelligent vehicles are gradually involved in entering the transportation life. When intelligent vehicles enter the actual road driving, the success of changing lanes directly determines the rate of safety problems occurring when driving. Therefore, combining with the current background of advocating low-carbon travel, the relevant technology of intelligent vehicles is improved and supplemented in terms of trajectory planning and the degree of impact on the environment, and a trajectory optimization method is proposed that considers the traffic impact and low-carbon of the vehicle when changing lanes. The method combines a vehicle dynamics model with intelligent vehicle operating conditions, the corresponding positional relationship is used to analyze and establish the corresponding safety domain. At the same time, the quintic polynomial model in the ideal state is improved, and the hexagonal polynomial model in the longitudinal direction is established. For the lane change is difficult to get the completion time and end position of the lane change in the PSO-BP neural network to solve the problem, to get the intelligent vehicle lane change trajectory cluster a dual-objective performance evaluation function is established, optimization of evaluation results using the properties of genetic algorithms. Taking car No. 689 in the NGSIM data as an example, the established trajectory planning model and algorithm are used to solve the problem, and the final lane-changing trajectory equation is obtained. After the simulation of the obtained data, a more suitable trajectory curve is obtained when the vehicle is running in the lane change, which shows that the lane change trajectory optimization method can solve the lane change problem well. Then the study is mainly applied to the planning of trajectories required by intelligent vehicles when lane changing occurs on real roads. After knowing the parameters of the lane-changing vehicle before and after changing lanes, which can plan a safe and efficient lane change route for vehicles in real time. The two comprehensive indicators of sexuality reached a better value.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:617:y:2023:i:c:s0378437123002200
    DOI: 10.1016/j.physa.2023.128665
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123002200
    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.2023.128665?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. 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.
    2. Dailisan, Damian N. & Lim, May T., 2019. "Vehicular traffic modeling with greedy lane-changing and inordinate waiting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 715-723.
    3. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
    4. 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).
    5. 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.
    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. 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).
    2. 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).
    3. Feng, Shumin & Li, Jinyang & Ding, Ning & Nie, Cen, 2015. "Traffic paradox on a road segment based on a cellular automaton: Impact of lane-changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 90-102.
    4. Dhari Ali Mahmood & Gábor Horváth, 2020. "Analysis of the Message Propagation Speed in VANET with Disconnected RSUs," Mathematics, MDPI, vol. 8(5), pages 1-21, May.
    5. 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).
    6. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    7. Ronan Keane & H. Oliver Gao, 2021. "Fast Calibration of Car-Following Models to Trajectory Data Using the Adjoint Method," Transportation Science, INFORMS, vol. 55(3), pages 592-615, May.
    8. Weihan Chen & Gang Ren & Qi Cao & Jianhua Song & Yikun Liu & Changyin Dong, 2023. "A Game-Theory-Based Approach to Modeling Lane-Changing Interactions on Highway On-Ramps: Considering the Bounded Rationality of Drivers," Mathematics, MDPI, vol. 11(2), pages 1-16, January.
    9. Huang, Jian & Hu, Mao-Bin & Jiang, Rui & Li, Ming, 2018. "Effect of pre-signals in a Manhattan-like urban traffic network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 71-85.
    10. 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.
    11. Kong, Dewen & Sun, Lishan & Li, Jia & Xu, Yan, 2021. "Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    12. Ke Wang & Qingwen Xue & Yingying Xing & Chongyi Li, 2020. "Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    13. Dailisan, Damian N. & Lim, May T., 2020. "Crossover transitions in a bus–car mixed-traffic cellular automata model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    14. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.
    15. Yao, Handong & Li, Qianwen & Li, Xiaopeng, 2020. "A study of relationships in traffic oscillation features based on field experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 339-355.
    16. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "A cellular automaton model for ship traffic flow in waterways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 705-717.
    17. Hamedi, Hamidreza & Shad, Rouzbeh & Ziaee, Seyed Ali, 2022. "A comparative study on measurement of lane-changing trajectory similarities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    18. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    19. He, Jia & Huang, Hai-Jun & Yang, Hai & Tang, Tie-Qiao, 2017. "An electric vehicle driving behavior model in the traffic system with a wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 119-126.
    20. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2020. "The relationship between car following string instability and traffic oscillations in finite-sized platoons and its use in easing congestion via connected and automated vehicles with IDM based control," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 58-83.

    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:617:y:2023:i:c:s0378437123002200. 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.