Automated Road Safety Analysis Using Video Data

Traffic safety analysis has often been undertaken using historical collision data. However, there are well-recognized availability and quality problems associated with collision data. In addition, the use of collision records for safety analysis is reactive: a significant number of collisions has to be recorded before action is taken. Therefore, the observation of traffic conflicts has been advocated as a complementary approach to analyze traffic safety. However, incomplete conceptualization and the cost of training observers and collecting conflict data have been factors inhibiting extensive application of the technique. The goal of this research is to develop a method for automated road safety analysis using video sensors in order to address the problem of a dependency on the deteriorating collision data. The method will automate the extraction of traffic conflicts from video sensors data. This should address the main shortcomings of the traffic conflict technique. This paper describes a comprehensive system for traffic conflict detection in video data. The system is composed of a feature-based vehicle tracking algorithm adapted for intersections and a traffic conflict detection method based on the clustering of vehicle trajectories. The clustering uses a K-means approach with Hidden Markov Models and a simple heuristic to find the number of clusters automatically. Traffic conflicts can then be detected by identifying and adapting pairs of models of conflicting trajectories. The technique is demonstrated on real world video sequences of traffic conflicts. TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal. INTRODUCTION Given the magnitude of the traffic safety problem, road authorities around the world are working hard to improve road safety in an effort to reduce the economic and societal costs associated with traffic collisions. Traffic safety analysis has been traditionally undertaken using historical collision data. However, there are well-recognized availability and quality problems associated with collision data. In many jurisdictions, the quantity and quality of collision data has been degrading for several years. In addition, the use of collision records for safety analysis is a reactive approach: a significant number of collisions has to be recorded before action is taken. Because of these problems, the observation of traffic conflicts has been advocated as an alternative or complementary approach to analyze traffic safety from a broader perspective than collision statistics alone (1,2,3,4). The Traffic Conflict Technique involves observing and evaluating the frequency and severity of traffic conflicts at an intersection by a team of trained observers. Traffic conflicts are more frequent than collisions, and their study can give detailed information about safety. The technique therefore provides a means for the analysts to immediately observe and evaluate unsafe driving maneuvers at an intersection. However, incomplete conceptualization and the cost of training observers and collecting conflict data have been factors inhibiting extensive application of the technique. Therefore, the successful automation of extracting conflicts from video sensors data using computer vision techniques appears to have practical benefits for traffic safety analysis. This research aims at implementing a complete system to interpret vehicle interactions and detect traffic conflicts in real world video data (provided by one stationary camera). This system should be generic, robust and low-cost, for regular use by traffic safety practitioners. The emphasis is on intersections which are crucial parts of the road networks for safety. This paper presents an approach to build such a traffic conflict detection system, and demonstrates its feasibility using experimental data.