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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(2): 172-183
LU: Neng-chao, ZHENG Meng-fan, HAO Wei, WU Chao-zhong, WU Hao-ran. Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 172-183. doi: 10.19818/j.cnki.1671-1637.2020.02.014
Citation: LU: Neng-chao, ZHENG Meng-fan, HAO Wei, WU Chao-zhong, WU Hao-ran. Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 172-183. doi: 10.19818/j.cnki.1671-1637.2020.02.014
shu

Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic

doi: 10.19818/j.cnki.1671-1637.2020.02.014
  • 1.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China

  • 2.

    Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan 430063, Hubei, China

  • 3.

    Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410205, Hunan, China

Funds:

National Natural Science Foundation of China 51775396

National Natural Science Foundation of China 51678460

Science and Technology Plan of Wuhan 2018010402011175

More Information
  • Author Bio:

    LYU Neng-chao(1982-), male, associateprofessor, PhD, E-mail: lnc@whut.edu.cn

  • Corresponding author: LYU Neng-chao(1982-), male, associate professor, PhD, E-mail: lnc@whut.edu.cn
  • Received Date: 2019-08-30
  • Publish Date: 2020-04-25
    Abstract
  • In order to improve the adaptability of advanced driver assistance system(ADAS) warning algorithm in complex driving environments, a comprehensive warning algorithm named the objective risk perception(ORP) algorithm based on the vehicle kinematics and risk perception characteristic was proposed. The analysis and derivation under typical risk conditions show that the proposed warning algorithm is a comprehensive mode of time headway(THW), time-to-collision(TTC) and safety margine(SM) based warning algorithms. In order to calibrate the parameter thresholds of the proposed warning algorithm, a total of 4 500 km natural driving experiments were carried out, and finally 409 valid near-crash events were extracted. The distribution characteristics of objective risk perception parameters when release accelerator and press brake were obtained. The risk warning algorithm parameters were calibrated based on the near-crash events and their parameter characteristics extracted from the natural driving data. The forward collision warning algorithm was developed under a simulated driving environment, and the verification experiments of the algorithm were carried out based on four risk scenarios. Research result shows that based on the parameter calibration of natural driving data, the two-level warning parameter thresholds of the ORP warning algorithm are 1.4 and 0.8 s, respectively. Based on the comparison of driving behavior under typical risk conditions, in terms of warning effectiveness, the ORP warning algorithm is slightly higher than the RP warning algorithm, and the effectiveness of secondary early warnings is significantly higher than that of the TTC warning algorithm. In terms of the average minimum time-to-collision of all driving segments under the warning algorithm, the ORP warning algorithm is 2.02 s, the RP warning algorithm is 1.90 s, and the TTC warning algorithm is 1.65 s, which shows that the ORP warning algorithm can adapt to the risk identification in complex risk environment. Based on a large number of natural driving test based parameter calibration and effect verification, the proposed warning algorithm can be used for the risk identification of advanced driver assistance systems.

     

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