Probabilistic Collision Prediction for Vision-Based Automated Road Safety Analysis

This work aims at addressing the many problems that have hindered the development of vision-based systems for automated road safety analysis. The approach relies on traffic conflicts used as surrogates for collision data. Traffic conflicts are identified by computing the collision probability for any two road users in an interaction. A complete system is implemented to process traffic video data, detect and track road users, and analyze their interactions. Motion patterns are needed to predict road users' movements and determine their probability of being involved in a collision. An original incremental algorithm for the learning of prototype trajectories as motion patterns is presented. The system is tested on real world traffic data, including a few traffic conflict instances. Traffic patterns are successfully learnt on two datasets, and used for collision probability computation and traffic conflict detection.