Workload Assessment of Human-Machine Interface: A Simulator Study with Psychophysiological Measures
Authors: Yuan-cheng Liu, Nikol Figalová, Jürgen Pichen, Philipp Hock, Martin Baumann, Klaus Bengler
Abstract: Human-machine Interface (HMI) is critical for safety during automated driving, as it serves as the only media between the automated system and human users. To guarantee an understandable and transparent HMI, an evaluation method is urgently needed. However, there hasn't been a standardized and objective assessment method for HMI transparency. The methods used to evaluate HMI nowadays are primarily subjective and not efficient. To bridge the gap, an objective and standardized HMI assessment method was proposed in a previous study, but the adaptation to a simulator environment was not validated. Hence, the objective of this study is to first identify suitable objective workload measures in a driving context before incorporating them into the proposed transparency assessment method. In this study, two psychophysiological measures, electrocardiography (ECG) and electrodermal activity (EDA) were evaluated for their effectiveness in finding differences in mental workload among different HMI designs in a driving simulator. Three HMI designs with different transparency were developed and used as independent variables. Besides the root mean square of successive differences (RMSSD) between normal heartbeats from the ECG and the skin conductance response (SCR) from the EDA, self-reported NASA-TLX scores were also evaluated and considered as dependent variables. The study was conducted in a static driving simulator with a field of view of 120 degrees. A total of 24 participants were recruited, and each experienced 12 trials counterbalanced for HMI designs and driving scenarios. Participants were asked to monitor the HMI constantly and activate SAE Level 2 automated driving system whenever they felt comfortable. During the interaction, the eye tracker was applied to identify the time points when participants were gazing at the HMI designs. These time points were later used as references to calculate the corresponding RMSSD and SCR. Results showed that the RMSSD from ECG and the SCR from EDA were able to identify significant differences in objective mental workload when interacting with in-vehicle HMIs. Plus, the same correlations among HMI designs for two psychophysiological measures and the NASA-TLX were also identified. To the best of our knowledge, this study is the first to use psychophysiological measures to estimate the mental workload when interacting with HMI during automated driving. The results of this study could be used as a firm ground for future research. The findings not only help identify suitable objective workload measures for the interaction with HMI during simulator driving but also serve as the first step toward a standardized transparency assessment method.
Keywords: human-computer interaction, automated vehicles, human-machine interface, mental workload, psychphysiological measure, transparency
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