Automated Safety Diagnosis Based on Unmanned Aerial Vehicle Video and Deep Learning Algorithm

This paper presents an automated traffic safety diagnostics solution named "Automated Roadway Conflict Identification System" (ARCIS) that uses deep learning techniques to process traffic videos collected by unmanned aerial vehicle (UAV). Mask region convolutional neural network (R-CNN) is employed to improve detection of vehicles in UAV videos. The detected vehicles are tracked by a channel and spatial reliability tracking algorithm, and vehicle trajectories are generated based on the tracking algorithm. Missing vehicles can be identified and tracked by identifying stationary vehicles and comparing intersect of union (IOU) between the detection results and the tracking results. Rotated bounding rectangles based on the pixel-to-pixel manner masks that are generated by mask R-CNN detection are introduced to obtain precise vehicle size and location data. Based on the vehicle trajectories, post-encroachment time (PET) is calculated for each conflict event at the pixel level. By comparing the PET values and the threshold, conflicts with the corresponding pixels in which the conflicts happened can be reported. Various conflict types: rear-end, head on, sideswipe, and angle, can also be determined. A case study at a typical signalized intersection is presented; the results indicate that the proposed framework could significantly improve the accuracy of the output data. Moreover, safety diagnostics for the studied intersection are conducted by calculating the PET values for each conflict event. It is expected that the proposed detection and tracking method with UAVs could help diagnose road safety problems efficiently and appropriate countermeasures could then be proposed.