With the increase in development of self-driving cars, research has been conducted to retain humanized interaction between cars and other road users, such as pedestrians. One way to retain this type of interaction is through the use of external Human-Machine Interfaces (eHMIs). This project aims to contribute to this field of research by exploring the case of eHMI-equipped, self-driving cars yielding for a crossing pedestrian. From literature it is known that light- and text-based signals are both promising ways of utilizing an eHMI. Due to the often simplified nature of these models, the goal of this project was to investigate if their findings hold up when increasing the cognitive load of the pedestrian. An eye-tracking experiment was conducted where two promising eHMI signal types (i.e., flashing lights and message) were tested in a realistic scenario, where the participant took the role of pedestrian. In the experiment, the cognitive load of the participants was varied, by applying different sizes of gaze contingent windows to the stimuli.

A range of 63 different trials of 10s each were shown to 23 participants. This set of trials included 7 different presets, aimed to test the effect of the signal type and the gaze window size independently. The participants' task during the experiment was to indicate when they deemed it safe to cross the road, by pressing the space bar. Their gaze was measured with an eye-tracker, after which it was transformed into three different metrics: saccade count, saccade amplitude and fixation duration. Additionally, a novel metric, the dispersion of the grouped gaze data, was introduced and explored. Finally, a questionnaire was conducted, investigating the self-reported clarity of the different signal types. Together with the reaction time, these metrics aimed to answer the following research question: What influence does raising the cognitive load for crossing pedestrians have on the effectiveness of text-based eHMIs as opposed to light-based eHMIs and no eHMI?

The results show that, as has been previously established, light- and text-based signals showed very similar response times and feature similar gaze characteristics when compared to each other. Both outperformed the condition where no signal was shown. Raising the cognitive load shows a decrease in saccade count and saccade amplitude, paired with a higher fixation duration. However, no proof could be found that raising the cognitive load has an influence on the effectiveness on any of the different signal types, meaning that either, the cognitive load was not raised by enough, or there is no actual effect. The dispersion showed that for the light-based signal, the focus on the stopping car is lost the quickest after the signal was shown.

The results of this study may help in explaining how pedestrians base their traffic decision on the type of signals being shown. This may also serve as a basis to further explore the possible effects of cognitive load on the effectiveness of these types of signals.