Criticality dimension-based probabilistic framework to detect near crashes in a roundabout

Abstract

Background: Preventing fatal traffic accidents towards Vision Zero is a challenge for the society. The collection of critical events from video recorded traffic data is of essential value for a better understanding on how and under what circumstances critical situations evolve. Identified behavioral patterns and derived infrastructural measures cannot only help to make driving safer, but also help to mature automated driving functions (ADFs) to make automated vehicles drive and interact more like humans especially in challenging situations. One flaw when developing ADFs is the dependency on synthetic simulated traffic scenarios. Method: In this paper, a novel probability-based framework is proposed allowing to measure the degree of criticality C(d) based on two dimensions explaining risk: severity (delta-v) and proximity (distance). Results: This metric is applied on real data of a roundabout. An initial evaluation of it was conducted using both a novel proposed method that takes the reaction of the second vehicle merged into account, and a practical application that shows a potential correlation between the traffic expert’s perceived risk and the metric. Conclusion: Quantifying risk on each of the collected real traffic scenarios makes testing ADFs possible in further more reliable and significant scenarios like near-crashes.