A Summary of Unmanned Aircraft Accident/Incident Data: Human Factors Implications


Available reports regarding unmanned aircraft (UA) reliability have noted that the accident rate for UA is, in general, much higher than that of manned aircraft (DoD, 2001; Schaefer, 2003; Tvaryanas, 2004). An understanding of the causal factors associated with these accidents is important if the goal is to improve the reliability of these aircraft to a level comparable to manned aircraft.

Human factors are consistently cited as a major cause of manned aircraft accidents. Estimates of the percentage of accidents that implicate human error range from 70% to 80% (Wiegmann & Shappell, 2003). In addition, over the past 40 years, the percentage of accidents attributable to human error has increased relative to those attributable to equipment failures (Shappell & Weigmann, 2000).

The review and analysis of UA accident data can assist researchers in the identification of important human factors issues related to their use. The most reliable source for UA accident data currently is the military. The military has a relatively long history of UA use and is diligent in accurately recording information pertaining to accidents/incidents. The purpose of this report is to review all currently available information on military UA accidents to determine to what extent human error has contributed to those accidents and to identify specific human factors involved in the accidents.


Designations for unmanned aircraft are almost as varied as the aircraft themselves. The most common term for these aircraft is Unmanned Aerial Vehicle (UAV). They have also been called Uninhabited Aerial Vehicles (also UAVs), Remotely Operated Vehicles (ROVs), and Remotely Piloted Vehicles (RPVs). In addition, the military has some special categories of unmanned aircraft that require additional nomenclature. These categories include Tactical UAVs (TUAV), Combat UAVs (UCAV), Unmanned Combat Armed Rotorcraft (UCAR), and “drones.” Some agencies have problems with the use of the term “vehicle” for aircraft. So there also exist designations like Remotely Operated Aircraft (ROA), Robotic Aircraft (RA), Remotely Piloted Aircraft (RPA), and Unmanned Aircraft (UA). The term “Unmanned Aircraft” (UA) will be used in this paper to designate the large population of remotely piloted, operated, and/or monitored aircraft.

Military Accident Classification System

Military accidents are classified based on monetary damage and/or severity of injury to personnel. All military branches have similar accident classification schemes. The most severe accident classification is Class A. Table 1 shows the accident classes for the Army.

The Air Force and Navy definitions of Class A, B, and C accidents are very similar to the Army definitions. However, both the Navy and Air Force classify their mishaps/accidents into only the three categories of A, B, and C. They do not have a category D.

Data Sources

To collect UA accident data, personnel from the Safety Centers of the Army, Navy, and Air Force were contacted. Requests were made for all data related to UA accidents, mishaps, and incidents from each of the Safety Centers. Personnel from military research laboratories were also contacted for information and reports summarizing accident data. In addition, an Internet search was conducted to identify and download accident information.

Army Data

Two primary sources of accident information were collected from the Army. The first source was a report entitled “The Role of Human Causal Factors in U.S. Army Unmanned Aerial Vehicle Accidents” (Manning, Rash, LeDuc, Noback, & McKeon, 2004). The report was produced by the U.S. Army Aeromedical Research Laboratory and is a summary of 56 UA accidents that occurred between January 1995 and February 2003. The accident data were obtained from the U.S. Army Risk Management Information System (RMIS), maintained by the U.S. Army Safety Center (USASC), Fort Rucker, Alabama. The accidents were summarized using two taxonomies, a modified version of the Human Factors Analysis and Classification System (HFACS; Shappell & Weigmann, 2000), and the Army accident investigation and reporting taxonomy, DA PAM 385-40 (Department of the Army, 1994b).

The second source of information was a direct query of the RMIS system. The query examined all UA accidents contained in the RMIS database that occurred between January 1980 and June 2004. A total of 74 accidents were identified, the earliest of which occurred on March 2, 1989, and the latest on April 30, 2004.

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