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

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

Sun, Chuan (Huanggang Normal University)
Wu, Chaozhong (Wuhan University of Technology)
Chu, Duanfeng (Wuhan University of Technology)
Lu, Z. (TU Delft Intelligent Vehicles)
Tan, Jian (Huanggang Normal University)
Wang, Jianyu (Huanggang Normal University)

2018

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.

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

http://resolver.tudelft.nl/uuid:7005d49f-4c41-442c-bf93-4541cec18dcb

https://doi.org/10.1142/S0218001418500374

0218-0014

International Journal of Pattern Recognition and Artificial Intelligence, 32 (11)

Institutional Repository

journal article

© 2018 Chuan Sun, Chaozhong Wu, Duanfeng Chu, Z. Lu, Jian Tan, Jianyu Wang