Imperfect Automation in Aviation Traffic Alerts: A Review of Conflict Detection Algorithms and Their Implications for Human Factors Research

Abstract

Automated warning and alert devices such as airborne collision avoidance systems (ACASs) represent a class of automation that is often found to be imperfect. The imperfections can be expressed as the number of false alarms or missed events. Most ACASs are constructed with a bias to prevent misses (which may have catastrophic consequences) and therefore, coupled with a low base-rate of conflict events, create high false alarm rates. In this paper, we review the adequacy of various CDTI warning algorithms that have been proposed and tested in addressing the false alarm issue, and the potential for multiple levels of alerting to mitigate the effects of false alarms on trust and reliance on the CDTI. We suggest new directions for future research, including evaluating the effects of false alarm rates on pilots' use of the CDTI, determining what strategies may enhance pilot tolerance of false alarms, and investigating the use of CDTI in conjunction with air traffic controllers.