Human Visual Processes for Monitoring Multi-Vehicle Operations

Abstract

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Mature Urban Air Mobility (UAM) operations will require operators to manage multiple uncrewed and increasingly autonomous aircraft simultaneously. Operators will supervise automated operations rather than directly control individual aircraft, with monitoring as their primary task. Exploring this operational paradigm, we conducted a study to understand the effects of interruptions and set size (i.e., number of vehicles) on operators' situation awareness. In this study, participants performed a multi-vehicle monitoring task while eye movements were recorded. Eye tracking data were filtered and processed, and various metrics were computed to characterize and better understand eye gaze behavior of operators during multi-vehicle monitoring. The present analyses primarily tested set-size effects on eye tracking metrics but also explored the efficacy of using gaze entropy metrics to assess monitoring performance. Results indicated relationships between gaze entropy metrics and set size. Additionally, the findings suggest that the multi-vehicle monitoring task can be treated as a multiple identity tracking (MIT) task rather than a multiple object tracking (MOT) task. This exploratory research provides valuable insights for future research focused on developing passive performance metrics for multi-vehicle monitoring.