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This paper presents a new method to evaluate the risk for road networks induced by UAV operations. The hazardous event considered for risk assessment is an accident with damages caused by an UAV falling on a road. Computation models are proposed to evaluate the probability of each event the sequence of which would lead to such an accident. Influences of the type of road as well as the road traffic are taken into account in the models. Simulation results are presented to illustrate risk evaluation by the proposed approach through a case study of a road network of several kilometres. Some metrics are also introduced to analyse the risk in a comprehensive way.

This paper investigates the generation of ground impact probability maps for UAVs in case of failure during the flight. Such maps are of a huge interest for risk assessment of UAV operations and can be used both for offline mission preparation or analysis and online decision making. Two approaches are proposed in this paper to generate such maps, taking into account a dynamical model a fixed-wing UAV and wind conditions. The first one relies on the generation of a complete database by Monte Carlo simulations. The second one is based on neural network surrogate models obtained by supervised learning using this database. Computation time required by the second approach is very small and compatible with online use. The two approaches are presented and discussed, and examples of ground impact probability maps generated are provided.