A Risk Based Paradigm and Model for Unmanned Aerial Systems in the National Airspace

Lum, Christopher W.; Waggoner, Blake

doi:10.2514/6.2011-1424

Cited by 65 publications

(53 citation statements)

References 8 publications

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“…As such it is widely assumed that the probability of an accident given the occurrence of the MAC State is one (i.e., P Accident|MAC = 1). This same assumption has been made when modelling the risk of MAC for UAS (Lum and Waggoner, 2011;Weibel and Hansman, 2004). For many types of UA this is an overly conservative assumption to make as there are a number of potential controls that can be implemented to reduce the probability that an accident results given the occurrence of a MAC.…”

Section: Mitigative Mid-air Collision Controlsmentioning

confidence: 91%

“…A safety case provides the ''documented body of evidence'' (CAA-UK, 2010) necessary to demonstrate that the risk of a MAC accident has been managed to an acceptable level (i.e., meets the specified ''safety-target''). To date, research has focussed on the modelling of the risk (e.g., references Dalamagkidis et al (2009), FAA (2009), Kochenderfer et al (2010), Lum and Waggoner (2011), Melnyk (2013), NATO (2008) and Weibel and Hansman (2004)) and on the analysis of specific risk controls (e.g., DAA (EUROCONTROL, 2010b;NATO, 2008;Prats et al, 2012)) at the expense of the more foundational problem of how a safety case for UA operations in non-segregated airspace should be structured and evaluated. Haddon and Whittaker (2002) identify a number of challenges in a regulation by ''safety-target'' approach.…”

Section: Introductionmentioning

confidence: 99%

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Structuring the safety case for unmanned aircraft system operations in non-segregated airspace

2015

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Section: Mitigative Mid-air Collision Controlsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Structuring the safety case for unmanned aircraft system operations in non-segregated airspace

2015

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“…However Lum and Waggoner (2011) suggest that these rates are overly pessimistic as they account for more than just crashes, so they suggest 1.0 per 1,000 FH (0.001 accidents/FH) is a more reasonable assumption and use this number in their safety risk model. The accident rate is not the MAC rate.…”

Section: Model Calibrationmentioning

confidence: 99%

System Safety Modeling of Alternative Geofencing Configurations for small UAS

Luxhøj¹

2016

IJAAA

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“…Military organizations around the world have been using them increasingly for the last 10 years (Lum & Waggoner, 2011). In the United States, from 2005 to 2011, the increase in UAV usage is approximately six-fold; about 100,000 flight hours to 600,000 in the Department of Defense (DOD).…”

Section: A Introductionmentioning

confidence: 99%

An Analysis of Human Causal Factors in Unmanned Aerial Vehicle (UAV) Accidents

Öncü¹,

Yıldız²

2014

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACT (maximum 200 words)Human error has been identified as the major contributor in many severe aviation mishaps, even for accidents involving Unmanned Aircraft (UA) systems. The Department of Defense (DOD) has used the Human Factors Analysis and Classification System (HFACS) taxonomy successfully for ten years to discover the human error in UA mishaps. It is important not to ignore the indisputable human presence in UA and the possible human-related causal factors in UA mishaps so we might be better able to reduce and prevent possible incidents. HFACS with its four main and 19 subcategories is a useful framework for identifying which factors have arisen historically, and which of them should have priority.The results of this study reveals that among 287 causal factors attributed to 68 accidents, 65 percent of the factors were associated with humans. Moreover, this study also discloses that the rater who categorizes the factors can differently observe, understand, and interpret the findings of mishap investigation; thus, human error may even impact the categorization phase due to the rater's perception. The research concluded that even though HFACS carried out its functionality well, further study is needed to conduct intense statistical analysis with unlimited data and to validate HFACS with more case studies and various raters. SUBJECT TERMShuman factors analysis and classification system (HFACS), U.S., Turkey, mishaps, human factors, unmanned aircraft systems, accident investigation reports, Navy Safety Center data. NUMBER OF PAGES 109 PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT AN ANALYSIS OF HUMAN CAUSAL FACTORS IN UNMANNED AERIAL VEHICLE (UAV) ACCIDENTS ABSTRACTHuman error has been identified as the major contributor in many severe aviation mishaps, even for accidents involving unmanned aircraft systems (UAS). TheDepartment of Defense (DOD) has used the Human Factors Analysis and Classification System (HFACS) taxonomy successfully for ten years to discover the human error in UA mishaps. It is important not to ignore the indisputable human presence in UA and the possible human-related causal factors in UA mishaps so we might be better able to reduce and prevent possible incidents. HFACS with its four main and 19 subcategories is a useful framework for identifying which factors have arisen historically, and which of them should have priority.The results of this study reveals that among 287 causal factors attributed to 68 accidents, 65 perce...

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A Risk Based Paradigm and Model for Unmanned Aerial Systems in the National Airspace

Cited by 65 publications

References 8 publications

Structuring the safety case for unmanned aircraft system operations in non-segregated airspace

Structuring the safety case for unmanned aircraft system operations in non-segregated airspace

System Safety Modeling of Alternative Geofencing Configurations for small UAS

An Analysis of Human Causal Factors in Unmanned Aerial Vehicle (UAV) Accidents

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