01631688

Component

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 modelling 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 the 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.

This paper was sponsored by TRB committee AHB30 Standing Committee on Vehicle-Highway Automation.

01618707

17-03960

English

22p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Automatic data collection systems; Driver support systems; Driving; Risk analysis

Driver characteristics; Driving safety; Road conditions; Vehicle state

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-03960

TRIS, TRB, ATRI

Dec 8 2016 11:31AM