With the rapid development of urbanization, the safety of road intersections has been widely concerned. This paper presents an automated vision-based road user conflict detection system, which can provide more effective data for traffic safety diagnosis. The system can achieve the high-precision detection, classification and tracking of the road users by using the state-of-art deep convolution neural network and the MOT technology, and finally the potential traffic conflict events are identified by LSTM based trajectory prediction techniques and TTC indicator. The system was experimented on a typical intersection of Nanjing, where the conflict between vehicles and non-motor vehicles (PTW and bicycles) was detected and their safety conditions at the intersections were evaluated. The results showed that the method based on deep learning can better adapt to the conflict detection of complex intersection, and the safety of the intersection can be effectively analyzed by this method. (Author/publisher)
Abstract