3D Representation of UAV-obstacle Collision Risk Under Off-nominal conditions

Banerjee, Portia; Gorospe, George; Ancel, Ersin

doi:10.1109/aero50100.2021.9438182

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…𝑓 nofly (𝑥) = 1, if cell 𝑐 within no-fly zone 𝐴 nofly 0, otherwise (16) where 𝐴 nofly represents the volume that is picked as a no-fly zone and 𝑐 depicts the individual cell in the 3D grid map. Figure 11b shows an example of a no-fly zone.…”

Section: E No-fly Zonesmentioning

confidence: 99%

“…Mathematical basis for risk assessment through a simple probabilistic formalization is introduced and Bayesian network implementation is discussed and compared with SORA in [12] and Bayesian framework is introduced for air risks in [13]. Also, there are risk assessment related studies that are conducted under NASA's System-wide Safety project as in [14][15][16]. The UTM Risk Assessment Framework (URAF) is designed in [14], by evaluating the risk in real time via Bayesian Belief Networks using vehicle system status, population density, and environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The UTM Risk Assessment Framework (URAF) is designed in [14], by evaluating the risk in real time via Bayesian Belief Networks using vehicle system status, population density, and environmental parameters. Moreover, determination of the non-participant casualty risk level during flights through population density information and onboard sources, is presented in [15] and determination of the obstacle collision risk level by considering off-nominal conditions, is studied in [16]. In [17], probability of fatality is quantified along a flight path by using vehicle specifications, population density, wind, and parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Risk Assessment and Utilization for Contingency Management of Future AAM System

Altun

Tsourdos

et al. 2023

AIAA AVIATION 2023 Forum

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This paper presents a risk assessment methodology to be used in the future Advanced Air Mobility (AAM) systems especially for supporting the planning phase and onboard contingency management solutions. Two types of dynamic risk maps are introduced as Contingency Risk Map that includes the probability of observing a contingency onboard and Risk Severity Map which covers various sources of data such as population density, a dense air traffic, obstacles, terrain, no-fly zones, and so forth. Contingency Risk Map is to quantify the probability of having a contingency and decide if the quantified probability is above the threshold. If the contingency risk probability is at unacceptable limit, Risk Severity Map assists to select a pre-defined secure emergency landing zone or non-secure emergency landing zone defined onboard. The developed risk assessment structure is tested through two different use cases. First one is about defining locations as vertiport alternatives based on the generated map, in case of a contingency ending up with an AAM vehicle to do emergency landing. Second case considers minimum risk onboard rerouting of an AAM vehicle to a secure/non-secure emergency landing zone under contingency management process. The main objective of this work is to build a system-wide contingency management concept for the AAM system by supporting with UTM services such as risk analysis assistance.

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Section: E No-fly Zonesmentioning

confidence: 99%