Evaluating Ground Risk for Road Networks Induced by UAV Operations

Bertrand, S.; Raballand, N.; Viguier, F. · 2018 · OpenAlex-citations

DOI: 10.1109/icuas.2018.8453441

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Summary

This paper addresses the lack of existing methodologies for evaluating ground risk to road networks induced by Unmanned Aerial Vehicle (UAV) operations. While probabilistic risk assessment models exist for mid-air collisions and risks to people in inhabited areas, no specific models have been developed for assessing the risk of accidents involving vehicles and passengers on roads. The authors aim to fill this gap by proposing a method to quantitatively assess the risk of a fixed-wing UAV falling onto a road network, thereby enabling safer authorization processes for long-range civilian UAV missions. The study defines the hazardous event as an accident with damage caused by a direct impact between a UAV and a road user. The risk is decomposed into a sequence of four conditional probabilities: loss of UAV control, non-controlled impact on a road, collision with a vehicle, and resulting damage. To evaluate these terms, the authors developed specific computation models incorporating road properties (vocation, dimensions, speed limits) and traffic data. Traffic and vehicle speeds are modeled using Gaussian functions based on Annual Average Daily Traffic (AADT) and time of day. Vehicle geometry accounts for stopping distances, which depend on speed and friction coefficients. The probability of impact is calculated using a bivariate normal distribution of the UAV’s glide range after engine failure. The probability of collision is determined by multiplying vehicle density by a calculated collision surface, which accounts for UAV dimensions, gliding angle, and the relative azimuth angle between the UAV and vehicle flow. A conservative approach assumes a probability of damage equal to one. The paper presents simulation results illustrating the application of these models to a road network case study. The models successfully integrate variables such as road type, traffic volume, and time of day to compute risk probabilities. The analysis highlights that the collision surface varies significantly with the collision angle and road width, necessitating a saturation approach to avoid overestimating risk when the theoretical collision area exceeds the physical road width. The study demonstrates that the proposed framework can effectively evaluate risk across different road vocations, from motorways to local liaisons, by adapting traffic and speed parameters accordingly. The significance of this work lies in providing the first comprehensive set of models for evaluating ground risk to road networks during UAV operations. By offering a tractable method to assess these risks, the paper supports the integration of UAVs into civil airspace for long-range operations. The proposed approach allows for a more precise risk assessment than generic models, accounting for the specific dynamics of road traffic and vehicle behavior. This contributes to the broader goal of enabling fully operational UAV processes by facilitating easier and quicker flight authorizations based on quantified risk levels.

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