Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehicle-pedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the high-resolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies.