A recognition model of driving risk based on Belief Rule-Base methodology

Chuan Sun*, Chaozhong Wu, Duanfeng Chu, Zhenji Lu, Jian Tan, Jianyu Wang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
20 Downloads (Pure)


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 modeling 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 our 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 and belongs to research on artificial intelligence (AI) in the automotive field.

Original languageEnglish
Article number1850037
Number of pages23
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number11
Publication statusPublished - 2018


  • ADAS
  • belief rule-base
  • data-driven
  • Driving data
  • vehicle driving risk


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