A virtual procedure for real-time monitoring of intervisibility between conflicting agents at intersections using point cloud and trajectory data

Abstract

A new procedure is developed for effectively monitoring intervisibility between conflicting agents in real-time at intersections. Dense light detection and ranging (Lidar) point clouds and time-stamped trajectory data are used to model static intersection environment and emulate traffic participants’ dynamic motion states, respectively. The proposed procedure reads trajectory data in sequence according to their timestamps. An agent-based approach that enables the application of multi-core parallel computing is applied to estimate conflict points and identify conflicting agents in pairs. Then, a linear elevation array is created from the Lidar point cloud data, based on which the elevation of each trajectory point is obtained in a real-time manner. Meanwhile, three-dimensional bounding cuboids are generated at each path point to represent digital twins of agents. Once a pair of conflicting agents are identified, a hybrid approach is triggered to examine whether the agents’ line-of-sights are occluded by either static (e.g., tree trunk) or dynamic obstacles (e.g., cars). Accordingly, virtual warning signals can be generated. The effectiveness of the procedure is demonstrated through controlled experiments. The procedure was also tested in two virtual scenarios. The mean processing time at each frame is less than 0.1 s as achieved with limited computational power. With implementation of parallel computing technique, processing time is not sensitive to number of agents within the intersection. In addition, by enabling the outputs of virtual warning signals, spatial distribution of conflict points, and individual conflict-related time series data, the procedure shall help provide substantial insights into intersection safety.