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Assessing handling qualities is crucial for ensuring the safety and operational efficiency of aircraft control characteristics. The growing interest in Urban Air Mobility (UAM) has increased the focus on electric Vertical Take-Off and Landing (eVTOL) aircraft; however, a comprehensive assessment of eVTOL handling qualities remains a challenge. This paper proposed a handling qualities framework to assess eVTOL handling qualities, integrating pilot compensation, task performance, and qualitative comments. An experiment was conducted, where eye-tracking data and subjective ratings from 23 participants as they performed various Mission Task Elements (MTEs) in an eVTOL simulator were recorded. The relationship between pilot compensation and task workload was investigated based on eye metrics. Data mining results revealed that pilots eye movement patterns and workload perception change when performing Mission Task Elements (MTEs) that involve aircraft deficiencies. Additionally, pupil size, pupil diameter, iris diameter, interpupillary distance, iris-to-pupil ratio, and gaze entropy are found to be correlated with both handling qualities and task workload. Furthermore, a handling qualities and pilot workload recognition model is developed based on Long-Short Term Memory (LSTM), which is subsequently trained and evaluated with experimental data, achieving an accuracy of 97%. A case study was conducted to validate the effectiveness of the proposed framework. Overall, the proposed framework addresses the limitations of the existing Handling Qualities Rating Method (HQRM), offering a more comprehensive approach to handling qualities assessment.
Li et al. (Fri,) studied this question.