Workload Assessment of Human-Machine Interface: A Simulator Study with Psychophysiological Measures

TL;DR

This study addresses the need for objective workload assessment in human–machine interfaces (HMI) for automated driving by evaluating EEG-based spectral power alongside NASA-TLX and TRASS in a simulator across three HMI designs with varying transparency (Fog, Trans, Trans-fog). The findings show that subjective workload and perceived transparency differ by design, with the Trans HMI achieving the lowest workload and highest transparency, while EEG results do not show significant differences—likely due to limited sample size and statistical power. The work provides a methodological foundation for real-time, objective HMI evaluation and highlights the importance of system transparency, not just interface clarity, in reducing mental workload. It sets the stage for future real-driving validation and the integration of additional psychophysiological measures to support standardized HMI assessment and safer automated driving.

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 enable a transparent HMI, we first need to know how to evaluate it. However, most of the assessment methods used for HMI designs are subjective and thus not efficient. To bridge the gap, an objective and standardized HMI assessment method is needed, and the first step is to find an objective method for workload measurement for this context. 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 simulator study. Three HMI designs were developed and used. Results showed that both workload measures were able to identify significant differences in objective mental workload when interacting with in-vehicle HMIs. As a first step toward a standardized assessment method, the results could be used as a firm ground for future studies. Marie Skłodowska-Curie Actions; Innovative Training Network (ITN); SHAPE-IT; Grant number 860410; Publication date: [29 Sep 2023]; DOI: [10.54941/ahfe1004172]

Figures (5)

• Figure 1: HMI designs under given circumstances.
• Figure 2: Illustration of participant with EEG set up in the driving simulator.
• Figure 3: Experimental procedure during each trial.
• Figure 4: Subjective evaluations with mean values and standard errors of means.
• Figure 5: EEG power spectral analysis with mean values and standard errors of means.