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Risky Driving Behavior Recognition Based on Vehicle Trajectory

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  • Shengdi Chen

    (Shandong Provincial Key Laboratory of Highway Technology and Safety Assessment, Shandong 250357, China
    College of Transport & Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Qingwen Xue

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Xiaochen Zhao

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Yingying Xing

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Jian John Lu

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

Suggested Citation

  • Shengdi Chen & Qingwen Xue & Xiaochen Zhao & Yingying Xing & Jian John Lu, 2021. "Risky Driving Behavior Recognition Based on Vehicle Trajectory," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12373-:d:687228
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    References listed on IDEAS

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    1. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
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    Cited by:

    1. Wen, Jianghui & Zhan, Xiaomei & Wu, Chaozhong & Xiao, Xinping & Lyu, Nengchao, 2023. "Risky driving behavior propagation: A novel stochastic SIR model and two-stage risk quantification method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    2. Jianfeng Xi & Yunhe Zhao & Zhiqiang Li & Yizhou Jiang & Wenwen Feng & Tongqiang Ding, 2022. "A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec," IJERPH, MDPI, vol. 19(23), pages 1-13, November.
    3. Huacai Xian & Yujia Hou & Yu Wang & Shunzhong Dong & Junying Kou & Zewen Li, 2022. "Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety," Sustainability, MDPI, vol. 15(1), pages 1-15, December.

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