Interface design and pilot attention distribution whilst pursuing a dynamic target

Abstract

The purpose of this study is to analyse the impact of the cockpit interface design on pilots’ attention distribution during flight operations. Two different fighter jet simulators, Fighter-A and Fighter-B, with different interface designs were used in this research. Both Fighter-A and Fighter-B simulators are dynamic, high-fidelity trainers that replicate actual aircraft performance, navigation and weapon systems. Sixty-nine qualified mission-ready pilots (39 Fighter-A pilots, 30 Fighter-B pilots) participated in this research. Fighter-A pilots had: ages between 26 and 45 years old (M = 34, SD = 5); total flying hours between 372 and 3,200 h (M = 1294, SD = 753); and type flying hours between 89 and 2,270 h (M = 815, SD = 524). Fighter-B pilots had: ages between 26 and 51 years old (M = 30, SD = 6); total flying hours between 310 and 2,920 h (M = 845, SD = 720); and type flying hours between 63 and 2,000 h (M = 461, SD = 487). Eye movement data were collected by a head-mounted ASL (Applied Science Laboratory) Mobile Eye, which is 76 g in weight. Eye movements at five areas of interest (AOIs) were analyzed, since those AOIs provide pilots with the required flight information to accomplish the mission. The AOIs are: Head-up Display (HUD); Integrated Control Panel (ICP); Right Multiple Function Display (RMFD); Left Multiple Function Display (LMFD); and Outside of Cockpit (OC). The findings indicate that differences in interface design might impact pilots’ visual scanning patterns, which is associated closely with attention distribution. This research demonstrated that interface designs of HUD, ICP, RMFD and LMFD of Fighter-A attract a higher percentage of fixation and longer average fixation duration compared with Fighter-B. Furthermore, Fighter-A pilots’ perceived workloads were lower, but their situational awareness performance was better than Fighter-B pilots. The application of an eye-tracking device during flight operations is not only beneficial to understand the pilot’s attention distribution, but also to understand the interaction performance between the pilot and the interface. The findings of this research have potential benefits for improving interface design and the efficiency of aviation training.