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

作者：Sun, Chuan^*; Wu, Chaozhong; Chu, Duanfeng; Lu, Zhenji; Tan, Jian; Wang, Jianyu

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(11): 1850037.

DOI：10.1142/S0218001418500374

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

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.

• 出版日期2018-11
• 单位武汉理工大学; 黄冈师范学院