Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data


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SAFE-D: Safety Through Disruption National University Transportation Center


The emerging connected vehicle and Automated Driving System (ADS), the widely available advanced in-vehicle telematics data collection/transmitting systems, as well as smartphone apps produce gigantic amount of high-frequency, high-resolution driving data. These telematics data provide comprehensive information on driving style, driving environment, road condition, and vehicle conditions. The high frequency telematics data has been used for several safety areas such as insurance pricing, teenage driving risk evaluation, and fleet safety management. This report study advances traffic safety analysis in the follow aspects: 1) characterize the high-frequency kinematic signatures for safety critical events compared to normal operations; and 2) develop models to distinguish and predict crashes from normal driving scenarios based on the high frequency data. Two deep learning models were developed. The first one combines the strength of convolutional neural network (CNN), gated recurrent unit (GRU) network and extreme gradient boosting (XGBoost). The second approach is based on a novel variational inference for extremes (VIE) to address the rarity of crashes. The models proposed in this project can benefit a variety of traffic research and applications including connected vehicles and ADS real-time safety monitoring, NDS data analysis, ride-hailing safety prediction, as well as fleet and driver safety management programs.



high frequency driving data, kinematics driving data, crash prediction, convolutional neural network, gated recurrent unit network, Variational Inference, connected vehicles