Findings from interviews with pilots on spatial disorientation: training, temporal dynamics and countermeasures

Spatial disorientation (SD) remains a prevalent issue in aviation, accounting for a disproportionate amount of Class A mishaps and fatalities. The current literature lacks specificity necessary to capture aspects of SD that may be essential in developing methods for detection and mitigation. This investigation focuses on the temporal progression of SD events in flight, elements of SD training facilitating recognition, recovery or avoidance of SD events, and considerations for SD countermeasures.

We developed a mixed-methods research study involving a questionnaire followed by a semi-structured interview to evaluate perception of spatial disorientation within a pilot population. Thirteen pilots participated with a mean age was 60 years (range: 35-76), and a median of 6,000 (range: 317-19050 h) flight hours.

Pilots reported common chronological stages of recognizing and responding to SD events defined as a habitual reaction to direct attention to instrument displays, an analytical investigation of the true aircraft state, followed by a calculated control response if warranted. Pilots estimated that the typical duration an SD event may go unrecognized is on the order of 1-15 s. Estimates for the durations of the various stages of response suggest events last on the order of 1's to 10's of seconds. The psychological and physiological recovery associated with experiencing an SD event often persists for 10's of seconds to minutes following recognition and recovery of the aircraft. SD-specific training was said to have positive impacts on understanding conditions where SD is likely to occur, and may reduce the duration it goes unrecognized. However, it does not appear to have much direct influence on recovery during an SD event.

Pilots suggest that real-time detection and pilot aiding systems would be acceptable additions to the flight deck, with caveats of signal detection performance, maintaining anonymity, and intended use of data collected. Through rich interview reports, we built a model of the temporal dynamics of SD events. This model can serve as a tool for improving specific aspects of SD training, developing computational SD detection methods, and designing targeted countermeasure systems for avoiding and/or mitigating the impacts of SD on pilot safety and performance.