6. Automated Decision Making in Road Traffic Monitoring by On-board Unmanned Aerial Vehicle System

The study is dedicated to solving the target issues of the ground traffic monitoring aided by the Unmanned Aerial Vehicles (UAV) based on applying the on-board computer vision systems. The classification of the road situations using images obtained after Traffic Accident (TA) is based on the feature set, facts, and attributes specified directly and/or indirectly on a possible situation class. The hierarchical structure of description of a road situation observable after the TA event is developed. For decision making, the production model of knowledge representation and corresponding Knowledge Base (KB) is offered to use. The issues related to decision making for recognition of the occurring traffic situations have been considered. The analysis of the strategies have been carried out based on the principles of minimizing the overall losses, limiting the admissible UAV flight altitude, and ensuring the required class recognition reliability. The models describing the functional criteria of the losses, flight safety of the UAV, and reliability of class recognition have been proposed. It has been shown that applying the minimum loss criterion ensures considerable savings of resources under different ratio of the loss quotients. The example for classification of a road incident using the real images is given.