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SONG Xiang, LI Xu, ZHANG Wei-gong, CAI Feng-tian, WU Ming-ming. Joint estimation method of key parameters for automotive active safety[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 65-74.
Citation: SONG Xiang, LI Xu, ZHANG Wei-gong, CAI Feng-tian, WU Ming-ming. Joint estimation method of key parameters for automotive active safety[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 65-74.
shu

Joint estimation method of key parameters for automotive active safety

  • 1.

    School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China

  • 2.

    Research Institute of Highway of Ministry of Transport, Beijing 100088, China

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  • Author Bio:

    SONG Xiang(1984-), male, doctoral student, +86-25-83794156, sx2190105@163.com

    ZHANG Wei-gong(1959-), male, professor, PhD, +86-25-83794157, zhangwg@seu.edu.cn

  • Received Date: 2013-08-18
  • Publish Date: 2014-02-25
    Abstract
  • According to the requirements of automotive active safety system, ajoint estimation method of key parameters for automotive active safety including automotive longitudinal velocity, lateral velocity and road friction coefficient was proposed. Based on automotive dynamics model with 3 degrees of freedom and brush tire model, the extended Kalman filter models under different road friction coefficient conditions were established. Automotive longitudinal velocity and lateral velocity were adaptively estimated by using the interacting multiple model, and the road friction coefficient could be real-timely estimated based on the calculated model probabilities. Calculation result shows that the method can accurately estimate automotive longitudinal and lateral velocities under different road friction coefficient conditions, the estimation error rates are less than 1% and 5% respectively. Compared with extended Kalman filter method, the estimation error of automotive velocity estimated by using the method reduces by more than 50%. When road condition mutates, the road friction coefficient can be real-timely estimated, the estimation error is less than 0.1, and the response time is less than 2 s.

     

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