We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning pixel-based and statistic-information-theoretic (clutter), color, shape, and our two new object-based metrics (meaningful-color-count (MeC) and text-to-ink ratio (TiR)). Our results show that both low-level edges and high-level elements affect perceived VC in visualization images; the number of corners and distinct colors are robust metrics across visualizations. Second, feature congestion, a statistical information-theoretic metric capturing color and texture patterns, is the strongest predictor of perceived complexity in visualizations rich in the same continuous color/texture stimuli; edge density effectively explains VC in node-link diagrams. Additionally, we observe a bell-curve effect for texts: increasing TiR initially reduces complexity, reaching an optimal point, beyond which further text increases VC. Our quantification model is also interpretable-enabling metric-based explanations-grounded in the VisComplexity2K dataset, bridging computational metrics with human perceptual responses. The preregistration is available at osf.io/5xe8a. osf.io/bdet6 has the dataset and analysis code.

The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI community. To fill this gap, we conducted semi-structured interviews with 26 safety drivers. Our results present how safety drivers cope with defective algorithms and shape and calibrate their perceptions while working with AV. We found that, as front-line workers, safety drivers are forced to take risks accumulated from the AV industry upstream and are also confronting restricted self-development in working for AV development. We contribute the first empirical evidence of the lived experience of safety drivers, the first passengers in the development of AV, and also the grassroots workers for AV, which can shed light on future human-AI interaction research.

As autonomous driving systems prevail, it is becoming increasingly critical that the systems learn from databases containing fine-grained driving scenarios. Most databases currently available are human-annotated; they are expensive, time-consuming, and subject to behavioral biases. In this paper, we provide initial evidence supporting a novel technique utilizing drivers' electroencephalography (EEG) signals to implicitly label hazardous driving scenarios while passively viewing recordings of real-road driving, thus sparing the need for manual annotation and avoiding human annotators' behavioral biases during explicit report. We conducted an EEG experiment using real-life and animated recordings of driving scenarios and asked participants to report danger explicitly whenever necessary. Behavioral results showed the participants tended to report danger only when overt hazards (e.g., a vehicle or a pedestrian appearing unexpectedly from behind an occlusion) were in view. By contrast, their EEG signals were enhanced at the sight of both an overt hazard and a covert hazard (e.g., an occlusion signalling possible appearance of a vehicle or a pedestrian from behind). Thus, EEG signals were more sensitive to driving hazards than explicit reports. Further, the Time-Series AI (TSAI, [1]) successfully classified EEG signals corresponding to overt and covert hazards. We discuss future steps necessary to materialize the technique in real life.

Traditional wall-sized displays mostly only support side-by-side co-located collaboration, while transparent displays naturally support face-to-face interaction. Many previous works assume transparent displays support collaboration. Yet it is unknown how exactly its afforded face-to-face interaction can support loose or close collaboration, especially compared to the side-by-side configuration offered by traditional large displays. In this paper, we used an established experimental task that operationalizes different collaboration coupling and layout locality, to compare pairs of participants collaborating side-by-side versus face-to-face in each collaborative situation. We compared quantitative measures and collected interview and observation data to further illustrate and explain our observed user behavior patterns. The results showed that the unique face-to-face collaboration brought by transparent display can result in more efficient task performance, different territorial behavior, and both positive and negative collaborative factors. Our findings provided empirical understanding about the collaborative experience supported by wall-sized transparent displays and shed light on its future design.