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Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps

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  • Xu, Ting
  • Zhang, Zhishun
  • Wu, Xingqi
  • Qi, Long
  • Han, Yi

Abstract

Crashes are occurred frequently at freeway off-ramps due to improper lane-changing (LC) behaviours. The LC behaviour is the main cause of freeway off-ramp crashes. It is important to warn the LC to reduce potential crashes. The uncertainty of LC behaviour increases the difficulties of predicting in advance. The off-ramps at Xi’an Raocheng freeway were chosen for investigation. The datasets were collected by the UAV. There was a total of 637 LC images extracted from the 200 minutes’ video stream. All LC behaviours were divided into twelve categories according to the changing direction and the influence of other vehicles in the target-lane or ego-lane. The machine learning technology is efficient in the image recognition. Thus, the vision technology was applied to devised a lane-changing recognition (LCR) model with the two-level convolutional neural network. A novel convolutional neural network based on the AlexNet was also proposed to compare with the LCR model. All samples were divided into a training dataset and a testing dataset for two models. The performance of two machines networks was compared. The training average accuracy was above 94.6% with the LCR model. The LCR model outperformed the model based on the AlexNet which was only 73.97% on average.

Suggested Citation

  • Xu, Ting & Zhang, Zhishun & Wu, Xingqi & Qi, Long & Han, Yi, 2021. "Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120309894
    DOI: 10.1016/j.physa.2020.125691
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    References listed on IDEAS

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    1. Danish Farooq & Janos Juhasz, 2019. "Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    2. Quantao Yang & Feng Lu & Jingsheng Wang & Dan Zhao & Lijie Yu, 2020. "Analysis of the Insertion Angle of Lane-Changing Vehicles in Nearly Saturated Fast Road Segments," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    3. Mingmin Guo & Zheng Wu & Huibing Zhu, 2018. "Empirical study of lane-changing behavior on three Chinese freeways," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
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    Citations

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    Cited by:

    1. Yuan, Renteng & Abdel-Aty, Mohamed & Gu, Xin & Zheng, Ou & Xiang, Qiaojun, 2023. "A unified modeling framework for lane change intention recognition and vehicle status prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    2. Bowen Gong & Zhipeng Xu & Ruixin Wei & Tao Wang & Ciyun Lin & Peng Gao, 2023. "Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    3. Li, Gen & Zhao, Le & Tang, Wenyun & Wu, Lan & Ren, Jiaolong, 2023. "Modeling and analysis of mandatory lane-changing behavior considering heterogeneity in means and variances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    4. 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).

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