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Classifying Traffic Accidents with Unmanned Aerial Vehicle


  • Moscow Aviation Institute, National Research University, Russian Federation


Background/Objectives: The study suggests a methodology for classifying traffic situations based on the images received on board UAV to improve monitoring efficiency and to eliminate the consequences of traffic accidents (TAs). Methods: The methodology for classifying traffic situations relies upon the images obtained after traffic accidents (TA), and it is based on a set of factors and attributes identifying directly and/or indirectly the potential class of the situation. A hierarchical structure has been developed to describe the situation that occurs after TA (“Description of Observed Scene”). The production model for knowledge representation and the relevant knowledge base (KB) have been suggested. Findings: Employing UAVs for the purposes of automated traffic monitoring can help improve the throughput capacity of the road, streamline the activities on eliminating the consequences of traffic accidents, reduce losses associated with accidents, etc. The study gives an example of classifying a situation based on the real image of the traffic accident. The novelty of the study is that it considers the case when UAV arrives at the region of interest, after the traffic accident. Applications/ Improvements: The obtained results prove that the suggested methodology can be applied to develop UAV on-board systems for automated traffic monitoring.


Road Traffic Monitoring, Situation Classification, Traffic Accident, Unmanned Aerial Vehicle.

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