A Recognition Model of Driving Risk Based on Belief Rule-base Methodology

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modelling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of the proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB30 Standing Committee on Vehicle-Highway Automation.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Wu, Chaozhong
    • Sun, Chuan
    • Chu, Duanfeng
    • Lu, Zhenji
    • Shyrokau, Barys
    • Happee, Riender
  • Conference:
  • Date: 2017


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 22p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01631688
  • Record Type: Publication
  • Report/Paper Numbers: 17-03960
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 8 2016 11:31AM