Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 4 January 2021

DOI: 10.1016/j.trip.2020.100293

We review the theoretical foundation for the need for human factors science. Over the past 2.8 million years, humans and tools have co-evolved. However, in the last century, technology is introduced at a rate that exceeds human evolution. The proliferation of computers and, more recently, robots, introduces new cognitive demands, as the human is required to be a monitor rather than a direct controller. The usage of robots and artificial intelligence is only expected to increase, and the present COVID-19 pandemic may prove to be catalytic in this regard. One way to improve overall system performance is to ‘adapt the human to the machine’ via task procedures, operator training, operator selection, a Procrustean mandate. Using classic research examples, we demonstrate that Procrustean methods can improve performance only to a limited extent. For a viable future, therefore, technology must adapt to the human, which underwrites the necessity of human factors science. Practitioner Summary: Various research articles have reported that the science of Human Factors is of vital importance in improving human-machine systems. However, what is lacking is a fundamental historical outline of why Human Factors is important. This article provides such a foundation, using arguments ranging from pre-history to post-COVID.

Objective: We review the sampling models described in John Senders’s doctoral thesis on “visual sampling processes” via a ready and accessible exposition. Background: John Senders left a significant imprint on human factors/ergonomics (HF/E). Here, we focus on one preeminent aspect of his career, namely visual attention. Methods: We present, clarify, and expand the models in his thesis through computer simulation and associated visual illustrations. Results: One of the key findings of Senders’s work on visual sampling concerns the linear relationship between signal bandwidth and visual sampling rate. The models that are used to describe this relationship are the periodic sampling model (PSM), the random constrained sampling model (RCM), and the conditional sampling model (CSM). A recent replication study that used results from modern eye-tracking equipment showed that Senders’s original findings are manifestly replicable. Conclusions: Senders’s insights and findings withstand the test of time and his models continue to be both relevant and useful to the present and promise continued impact in the future. Application: The present paper is directed to stimulate a broad spectrum of researchers and practitioners in HF/E and beyond to use these important and insightful models.

The topic of situation awareness has received continuing interest over the last decades. Freeze-probe methods, such as the Situation Awareness Global Assessment Technique (SAGAT), are commonly employed for measuring situation awareness. The aim of this paper was to review validity issues of the SAGAT and examine whether eye movements are a promising alternative for measuring situation awareness. First, we outlined six problems of freeze-probe methods, such as the fact that freeze-probe methods rely on what the operator has been able to remember and then explicitly recall. We propose an operationalization of situation awareness based on the eye movements of the person in relation to their task environment to circumvent shortfalls of memory mediation and task interruption. Next, we analyzed experimental data in which participants (N = 86) were tasked to observe a display of six dials for about 10 min, and press the space bar if a dial pointer crossed a threshold value. Every 90 s, the screen was blanked and participants had to report the state of the dials on a paper sheet. We assessed correlations of participants’ task performance (% of threshold crossing detected) with visual sampling scores (% of dials glanced at during threshold crossings) and freeze-probe scores. Results showed that the visual-sampling score correlated with task performance at the threshold-crossing level (r = 0.31) and at the individual level (r = 0.78). Freeze-probe scores were low and showed weak associations with task performance. We conclude that the outlined limitations of the SAGAT impede measurement of situation awareness, which can be computed more effectively from eye movement measurements in relation to the state of the task environment. The present findings have practical value, as advances in eye-tracking cameras and ubiquitous computing lessen the need for interruptive tests such as SAGAT. Eye-based situation awareness is a predictor of performance, with the advantage that it is applicable through real-time feedback technologies.