Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data

Z. Gao, Y. Liu, J. Y. Zheng, R. Yu, X. Wang, P. Sun
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
As the raising of traffic accidents caused by commercial vehicle drivers, more regulations have been issued for improving their safety status. Driving record instruments are required to be installed on such vehicles in China. The obtained naturalistic driving data offer insight into the causal factors of hazardous events with the requirements to identify where hazardous events happen within large volumes of data. In this study, we develop a model based on a low-definition driving record
more » ... nt and the vehicle kinematic data for postaccident analysis by multi-modal deep learning method. With a higher camera position on commercial vehicles than cars that can observe further distance, motion profiles are extracted from driving video to capture the trajectory features of front vehicles at different depths. Then random forest is used to select significant kinematic variables which can reflect the potential crash. Finally, a multi-modal deep convolutional neural network (DCNN) combined both video and kinematic data is developed to identify potential collision risk in each 12-second vehicle trip. The analysis results indicate that the proposed multi-modal deep learning model can identify hazardous events within a large volumes of data at an AUC of 0.81, which outperforms the stateof-the-art random forest model and kinematic threshold method.
doi:10.1109/itsc.2018.8569659 dblp:conf/itsc/GaoLZYWS18 fatcat:amv4u5gtvnacrcr4vqjuowaehi