1. A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology.
- Author
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Sun, Chuan, Wu, Chaozhong, Chu, Duanfeng, Lu, Zhenji, Tan, Jian, and Wang, Jianyu
- Subjects
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AUTOMOBILE driving , *DRIVER assistance systems , *TRAFFIC safety , *AUTOMOBILE driving simulators , *ARTIFICIAL intelligence - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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