Abstract

Abstract: The advent of powerful sensing technologies, especially video sensors and computer vision techniques, has allowed for the collection of large quantities of detailed traffic data. They allow us to further advance towards completely automated systems for road safety analysis. This paper presents a comprehensive probabilistic framework for automated road safety analysis. Building upon traffic conflict techniques and the concept of the safety hierarchy, it provides computational definitions of the probability of collision for road users involved in an interaction. It proposes new definitions for individual road users and aggregated measures over time. This allows the interpretation of traffic from a safety perspective, studying all interactions and their relationship to safety. New and more relevant exposure measures can be derived from this work, and traffic conflicts can be detected. A complete vision-based system is implemented to Road safety is characterized by the absence of accidents, i.e. collisions between road users. The safety is traditionally measured by the number of collisions, or rather its expected number at a given time. Traffic safety diagnosis has been traditionally undertaken using historical collision data. However, there are well-recognized problems of availability and quality

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