Vehicle field test can be conducted smoothly because of the automobile-mounted monitoring system and abundant diving data have been collected. Driving data mining is in an urgent need of new thoughts introduced to break through the original technical bottleneck. This paper presented a novel method of symbolic representation in diving data mining and applied the idea of time series symbolization to traffic engineering. The sample data is processed by normalization, dimensionality reduction, discretization, and symbolization based on the three steps of symbolic aggregate approximation (SAX) with driving data characteristics taken into adequate consideration. The results showed that the high-dimensionality miscellaneous driving time series data was rationally converted into highly readable, easy to search and locate symbolic series after semantic encoding, and the main characteristics of time series data was preserved after a substantial reduction of data dimensionality. Finally, the paper demonstrated the positive effects of this method on the analysis of actual vehicle driving safety based on case study, and it explored the application of SAX to speed and acceleration data from driving data set.

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.