Volume 23 Issue 2
Apr.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(2): 287-300
XIE Ji-ming, XIA Yu-lan, QIAN Zheng-fu, LIU Bing, QIN Ya-qin. Lane-change risk warning in interweaving area considering information from intelligent connected near-neighboring vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 287-300. doi: 10.19818/j.cnki.1671-1637.2023.02.021
Citation: XIE Ji-ming, XIA Yu-lan, QIAN Zheng-fu, LIU Bing, QIN Ya-qin. Lane-change risk warning in interweaving area considering information from intelligent connected near-neighboring vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 287-300. doi: 10.19818/j.cnki.1671-1637.2023.02.021
shu

Lane-change risk warning in interweaving area considering information from intelligent connected near-neighboring vehicles

doi: 10.19818/j.cnki.1671-1637.2023.02.021
  • 1.

    Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China

  • 2.

    Yunnan Communications Investment and Construction Group Co., Ltd., Kunming 650103, Yunnan, China

Funds:

National Natural Science Foundation of China 71861016

More Information
  • Author Bio:

    XIE Ji-ming(1994-), male, doctoral student,xiejiming@kust.edu.cn

    QIN Ya-qin(1972-), female, professor, PhD, qinyaqin@kust.edu.cn.

  • Received Date: 2022-10-19
    Available Online: 2023-05-09
  • Publish Date: 2023-04-25
    Abstract
  • In view of the problems of large feature differences, unbalanced samples, and long parameter tuning time in predicting the lane-change risk of vehicles, the high-precision microscopic vehicle trajectory data were combined with the hyperparametric optimization machine learning method, and a method for identifying and warning of the lane-change risk in interweaving areas was proposed, which could be applied to intelligent connected vehicles (ICV). According to the unmanned aerial vehicle video, lane-change trajectories with time accuracy of 0.1 s and spatial accuracy of 0.1 m per pixel in the urban expressway interweaving area were extracted from a wide-area view, and the lane-change risk perception information, such as the vehicle spacing, vector velocity, acceleration, proximity rate, and velocity angle, was measured. A lane-change time-to-collision (TTC) model that took into account information from near-neighboring vehicles was introduced, and the urgent need for vehicles to merge into or leave arteries was reflected. In addition, the difference in the lane-change behaviors at different locations was described. The 15th quartile method and the interquartile range method were used, and the warning levels for lane-change risks were classified. With several evaluation metrics, such as the accuracy, true positive rate, and sensitivity, the linear classifier, support vector machine, K-nearest neighbor, and RUSBoost model were selected and compared in terms of the lane-change risk prediction results, and the optimal model for the real-time warning of lane-change risks in interweaving areas was selected. For the optimal model, the hyperparameters were optimized and validated. Research results show that the RUSBoost model is the optimal model. When the hyperparametric optimization machine learning method iterates 24th times, the minimum error and optimal point hyperparameter of RUSBoost appear, and the prediction accuracies of RUSBoost and optimized BRUSBoost models are 91.40% and 99.80%, respectively. The AUC values are 0.96 and 0.99, respectively, and the warning accuracy of the optimized BRUSBoost model for level Ⅰ and Ⅲ lane change risks improves by 50.9% and 41.2%, respectively. As a result, the defects of more complex and less predictable lane-change conditions of extreme risks improves effectively. The research results can help the ICV lane-change decisions and trajectory optimization and guide traffic management departments to develop ICV dynamic warning schemes.

     

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