In:
International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 32, No. 11 ( 2018-11), p. 1850037-
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.
Type of Medium:
Online Resource
ISSN:
0218-0014
,
1793-6381
DOI:
10.1142/S0218001418500374
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2018
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