Early Afternoon Concurrent Sessions: Critical Issues: Presentation: System Safety Modeling of Alternative Geofencing Configurations for Small UAS
Notice bibliographique
Résumé
As is well known, the integration of small Unmanned Aircraft Systems (sUAS) or “drones” into the National Airspace System (NAS) has captured significant industry, academic, regulatory and media attention. For sUAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied from a system safety perspective to identify possible hazards and to assess mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. Using inductive logic, the ASRM may be used to evaluate underlying causal factors linked to the air vehicle and/or to the systems and procedures that lead to the unsafe state and the probabilistic interactions among these factors that contribute to the safety risk. The ASRM can also assess the projected impact of mitigations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides an analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a notional scenario that involves a sUAS being used for aerial surveillance in the siting of a wind turbine farm near the Yukon River in Alaska. It is conjectured that the sUAS interacts with a general aviation aircraft flying in the nearby vicinity from a local airport. The sUAS being used is a fixed wing-type where there is a failure of the separation assurance function since the UAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. In the modeling approach, the time-dependent effects of wind velocity, wind sensor faults, and wind sensor accuracy are also included. In particular, the system safety study focuses on alternative geofencing mitigations proposed by Atkins (2014) such as using a single onboard processor that integrates the datalink, autopilot and geofencing functions vs. the use of a separate processor solely for the geofencing function. The alternative geofencing configurations are modeled as separate “objects” or sub-nets in a fault tree-type analysis. The geofencing fault tree analyses can then be iteratively linked with the top-level network to comparatively assess the efficacies of the alternative geofencing configurations on reducing the likelihood of the sUAS leaving its’ AO. The ASRM safety risk results for the notional scenario are presented and interpreted. It is suggested that the safety risk model may also be used to strategically assess alternative “assured containment” concepts as posited by Hayhurst et al. (2015). References: Atkins, E.M. (2014), “Autonomy as an enabler of economically-viable, beyond-line-of-sight, low-altitude UAS application with acceptable risk,” AUVSI Unmanned Systems, Orlando, FL, May 12-15, pp. 200-211. Hayhurst, K.J., N.A. Neogi, and H.A. Verstynen (2015), “A Case Study for Assured Containment,” International Conference on Unmanned Aircraft Systems (ICUAS), Denver Marriott Tech Center, Denver, CO, June 9-2, pp. 260-268.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».