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Analysis & performance

Scientific publication

Near-Miss Accidents – Classification and Automatic Detection

Publication from Digital

Georg Thallinger, Florian Krebs, Eduard Kolla, Peter Vertal, Gustáv Kasanický, Helmut Neuschmied, Karl-Ernst Ambrosch

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineeringbook series (LNICST, volume 222) , 7/2018


In this work, we propose a system that automatically identifies hazardous traffic situations in order to gather comprehensive evidence, allowing timely mitigation of dangerous traffic areas. The system employs optical and acoustic sensors, stores the recorded sensor data to an incident store, and provides an assessment of the causes and consequences of the captured situation. Three main categories of features are used to assess the risk of a traffic situation: (1) key parameters of the traffic participants such as size, their distance, acceleration and motion trajectories; (2) the occurrence of acoustic events (shouting, tire squealing, honking sounds, etc.) which often co-occur with hazardous situations; (3) global parameters which describe the current traffic situation, such as traffic volume or density. An automated detection allows to monitor an intersection for an extensive time period. Compared to traditional manual methods, this facilitates generating significantly more data, which increases the informative value of such an assessment and therefore leads to a better understanding of the hazard potential of the spot. The outcome of such an investigation will finally serve as a basis for defining and prioritizing improvements.

Keywords: Near-miss accidents; Accident detection; Automatic detection; Sensor fusion;

Url: https://link.springer.com/chapter/10.1007/978-3-319-93710-6_16