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To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive. [ABSTRACT FROM AUTHOR]

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]