A Comprehensive Probabilistic Assessment Method of UAS Ground Collision Risk

Théo Serru (1) , Kevin Delmas (2)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 DTIS, ONERA, Université de Toulouse [Toulouse]

Unmanned Aircraft Systems (UAS) are widely experienced in domains such as transportation, delivery or infrastructure surveillance. However using these systems for missions near to populated areas presents new safety challenges. To address these challenges, the European Aviation Safety Agency has published safety assessment guidelines for unmanned operations. This document requires to assess, for a given operational profile, the likelihood of on-ground collision with critical infrastructure or people. Despite these regulatory requirements, the probabilistic assessment of on-ground collision is only partially addressed by existing works. On one hand, various works promote the Model Based Safety Assessment to identify the failure contributing to the crash. One the other hand some works provide probabilistic estimation methods of an on-ground collision knowing that the drone is unable to ensure flight continuation. Moreover in these methods, the assessment is performed thanks to Monte Carlo simulation. However, with the growing complexity of UAS, the computational effort to estimate the probability of rare events with standard Monte Carlo method becomes intractable for modern UAS. The contribution of this paper is thus to provide a comprehensive tooled method to estimate the on-ground collision probability by considering the contribution of on-board failures, tolerance mechanisms and operational specificity. To tackle Monte Carlo limitations, variance reduction methods and more specifically importance sampling is used to obtain quicker and tighter estimation of the probability than standard Monte Carlo method.The paper provides a detailed presentation of the method, and a demonstration of importance sampling benefits over Monte Carlo through a comparative study on a UAS case study. The experiments are based on a safety model formalized with the Open AltaRica platform and on a custom simulator to perform both Monte Carlo and important sampling simulations.

Mots clés: SURETE FONCTIONNEMENT; DRONE; ECHANTILLONNAGE

Domaines: Sciences de l'ingénieur [physics]; Statistiques [stat]

Théo Serru, Kevin Delmas. A Comprehensive Probabilistic Assessment Method of UAS Ground Collision Risk. 31st European Safety and Reliability Conference (ESREL), Sep 2021, Angers, France. pp.38-45, ⟨10.3850/978-981-18-2016-8_027-cd⟩. ⟨hal-03359325⟩