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A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . Nov2018, Vol. 32 Issue 11, pN.PAG-N.PAG. 23p. - Publication Year :
- 2018
-
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]
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 32
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 130870851
- Full Text :
- https://doi.org/10.1142/S0218001418500374