Real-time Risk Prediction at Signalized Intersections Using a Graph Neural Network


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Safe-D University Transportation Center


Intersection-related traffic crashes and fatalities are major concerns for road safety. This project aimed to understand the major causes of conflicts at intersections by studying the intricate interplay between roadway agents. The approach involved using the current traffic camera systems to automatically process traffic video data. As manual annotation of video datasets is a very labor-intensive and costly process, this research leveraged modern computer vision algorithms to automatically process these videos and retrieve kinematic behavior of the traffic actors. Results demonstrated how traffic actors and road segments can be modeled independently via graphs and how they can be integrated into a framework that can model traffic systems. The team used a graph neural network to model (a) the interaction of all the roadway agents at any given instance and (b) their role in road safety, both individually and as a composite system. The model reports a near-real-time risk score for a traffic scene. The study concludes with a presentation of a new drone-based trajectory dataset to accelerate research in intersection safety.



intersection safety, crash causation analysis, graph neural network, computer vision, traffic camera