This study aims to contribute to guidelines for driver licensing organizations on assessing driver competence in using Level 3 Automated Lane Keeping Systems (ALKS), based on an on-road experiment with eight professional driving assessors (i.e., expert driving examiners who train examiner candidates; 6 males, 2 females, all driving more than 20,000 km per year) in a Wizard-of-Oz vehicle. Using a think-aloud protocol, we captured cognitive processes during system supervision and take-over requests (TORs) in real-world traffic jams. A large language model (LLM)-based thematic analysis of transcripts revealed five themes: (1) Requirement for immediate environmental assessment, (2) Requirement for causal understanding, (3) Requirement for proactive intervention to maintain traffic flow, (4) Requirement for continuous "supervisor" engagement, and (5) Physical ergonomics and mode awareness. These findings indicate that, at least during short-duration usage, drivers do not simply rely on the system to disengage from driving; instead, they maintain active monitoring, physical readiness, and anticipatory skills. These observations blur the distinction between Level 2 and Level 3 automation, as the expert participants in this study generally remained attentive rather than adopting the 'mind-off' state that Level 3 theoretically allows. In conclusion, assessing ALKS usage involves not only evaluating a driver's reaction to a TOR but also judging their performance as a systems manager responsible for anticipating conflicts and smoothly executing control transitions.

BackgroundImmersive virtual reality (IVR) is increasingly used for task training. Adjusting sensory and cognitive complexity to individual capacity is important for effective training, yet it remains unclear how varying visual, auditory, and cognitive demands influence behavioral responses, physiological activity, and task performance during goal-directed activities in IVR. ObjectiveThis study aimed to examine how 2 levels (low and high) of visual, auditory, and mental demands affect self-reported mental demand and effort (MDE), task performance, heart rate, gaze behavior, and head movements during a virtual grocery shopping task. MethodsA within-subjects study was conducted with 22 unimpaired university students (11 women, 11 men; aged 23-27 years) recruited via convenience sampling at TU Delft, the Netherlands. Participants completed a grocery shopping task (collecting products from shelves during 122-second trials) under 7 conditions: baseline, visual low/high (background characters), auditory low/high (background sounds), and mental low/high (0-back/2-back tasks), in random order. Outcome measures included MDE (0-100), task performance (products placed in the cart), mean heart rate (from electrocardiography), head-stillness duration, and gaze duration on areas of interest (via integrated eye-tracking). Two-tailed paired-samples t tests with Bonferroni correction (α=.008) were supplemented by linear mixed-effects models. ResultsThe mental high condition produced the largest effects: MDE increased relative to baseline (mean difference [Mdiff] 52.73, 95% CI 44.01-61.44; t21=12.58; P<.001), performance decreased (Mdiff –8.00 products, 95% CI –9.57 to –6.43; t21=–10.58; P<.001), head-stillness duration increased (Mdiff 13.54 s, 95% CI 9.94-17.14; t21=7.83; P<.001), and gaze on the shopping list increased (Mdiff 9.89 s, 95% CI 5.36-14.43; t21=4.54; P<.001). Auditory high demands increased MDE (Mdiff 17.05, 95% CI 9.15-24.94; t21=4.49; P<.001) without significantly affecting performance. Visual high demands redistributed gaze away from the main shelves (Mdiff –6.11 s, 95% CI –8.70 to –3.52; t21=–4.91; P<.001) without reducing performance. Heart rate increased under mental low demand (Mdiff 4.02 bpm, 95% CI 1.69-6.34; t21=3.59; P=.002); the mental high effect did not reach corrected significance in the paired-samples test (P=.02), possibly due to reduced physical activity in this condition. Under high cognitive load, participants spent more time checking the shopping list and showed greater head-stillness, consistent with task-serialization and memory-offloading strategies. ConclusionsCognitive demands strongly impacted workload, performance, and behavior, while sensory demands affected attention distribution and perceived workload, with no significant performance decrements at the Bonferroni-corrected threshold. By comparing 2 demand levels against a no-demand baseline and incorporating gaze and head-movement measures not previously examined, this study extends prior work in this domain. The findings suggest IVR environments can incorporate sensory richness without large performance reductions in unimpaired young adults, and that behavioral strategies under cognitive load may support real-world workload monitoring and adaptive task adjustment, pending clinical validation.

To obtain a driver's licence, one must successfully complete a practical driving test and a theory test. Although the theory test is widely regarded as an important element of driving competence, little is known about the predictors of theory test performance, and in particular the extent to which the acquired knowledge is retained over the years. All individuals who passed a car theory test in the Netherlands between November 2019 and October 2023 were invited to complete a questionnaire, which included a retention test (i.e., a representative retake test) consisting of 20 items not used before. The results based on 50,857 respondents revealed that those with a lower level of education exhibited lower performance on the retention test. Moreover, respondents who took a course with an instructor, an approach mostly used by those with a lower level of education, had a relatively high likelihood of passing the official car theory test on the first attempt. It was also found that the extent to which knowledge increased or decreased over the years was item-dependent, a pattern possibly explained by whether the test item measures functionally relevant driving experiences or if it primarily assesses isolated rules. The results of this study are relevant for training institutes and policymakers.

The advent of self-driving cars has sparked discussions about eye contact in traffic, particularly due to challenges that automated vehicles face in non-verbal communication with human road users. In his 1992 book, Turn Signals Are The Facial Expressions Of Automobiles, Don Norman describes how drivers in Mexico City deliberately avoid eye contact when entering a roundabout to create uncertainty in the minds of other drivers, leading the latter to yield right of way. Norman argued that such manipulative or aggressive behavior would not be tolerated in the United States. In the present study, we tested these claims through an online survey involving 3,857 respondents from 20 countries. The results confirmed that Mexican drivers reported a higher frequency of non-speeding ‘aggressive’ violations compared to those from most other countries. Regarding eye contact in the roundabout scenario presented in the survey, national differences were found not so much in the frequency of eye contact but in the reasons behind its use. Mexican drivers tended to avoid eye contact to reduce tension or avoid conflict with other drivers. However, they also frequently reported making eye contact to assert or subtly enforce their right of way. In higher-income countries like the United States, driver-driver eye contact is often deemed unnecessary. In conclusion, our findings partially correspond with Norman’s anecdote based on his experiences in 1950s Mexico City. These results may have implications for understanding the stability of traffic cultures and the challenges related to eye contact and non-verbal communication faced by developers of automated vehicles.

The emerging use of automated driving systems introduces novel situations that may affect the safety of vulnerable road users such as cyclists. In this paper, we explain and conceptualise the phenomenon of phantom braking – sudden and unexpected deceleration – in automated vehicles. We apply signal detection theory to interpret phantom braking as a by-product of automated decision-making, with the vehicle favouring the avoidance of accidents at the cost of potentially causing rear-end accidents. To illustrate phantom braking and its effects on cyclists, we used a newly developed cycling simulator. An exploratory measurement conducted with a single cyclist participant revealed a possible complacency effect of the cyclist, with the cyclist’s decision-making mirroring the automated vehicle’s decision-making. The findings provide a testament to using cycling simulators for further exploration of the effects of phantom braking on cyclists.

In future traffic, intelligent user interfaces may aid cyclists in interpreting the behaviour of automated vehicles. Cyclists can be equipped with obstacle-detecting sensors, and an interface could display relevant information or use audible alerts to warn or inform cyclists of other road users' intent and potential hazards. Researching intelligent user interfaces for cyclists is vital for understanding how users can efficiently and safely interact with automated vehicles. This work-in-progress paper presents two studies for developing and testing user interfaces for cyclists in future automated traffic. In the first study, we reanalysed interview data from 30 cyclists, resulting in two interface concepts: the app CycleSafe and an omnidirectional on-vehicle interface capable of communicating cyclist recognition. In the second study, we outline an envisioned experiment to test these two concepts in a naturalistic environment with cyclists and a vehicle emulating automation. We hypothesise that cyclists prefer receiving warning signals over no warnings, prefer early over late warnings, and that auditory signals and visual on-vehicle interfaces will perform better than visual on-bike interfaces.

Humans are embarking on a new era of space exploration with the plan of sending crewed spacecraft to the Moon, Mars, and beyond. Extravehicular activities (EVAs) will be an essential part of the scientific activities to be carried out in these missions, and they will involve extensive geological fieldwork. These EVAs entail many challenges as real-time support from ground control cannot be provided to astronauts. Hence, new human-machine interfaces are urgently needed to enhance mission autonomy for astronauts and reduce ground communication dependability for real-time operations. This study introduces an Augmented Reality (AR) Internet of Things tool for astronauts to carry out geological activities. It proposes a theoretically-informed user-centred design method supported by expert feedback and an evaluation method. The tool was assessed via questionnaires and semi-structured interviews with European Space Agency (ESA) astronauts and geological field activities experts. Content analysis of the interviews revealed that user satisfaction was the first most mentioned (32% of 139 quotes) usability aspect. Key design factors identified were: displaying solely important information in the field of view while adjusting it to the user's visual acuity, easy usage, extensibility, and simplicity. User interaction was the second most mentioned (24% of 139 quotes) usability aspect, with voice seen as the most intuitive input. Finally, this research highlights impor-tant factors determining the usability and operational feasibility of an AR tool for analogue training missions and provides a foundation for future design iterations and an eventual integration of AR into the spacesuit's visor.(c) 2022 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated vehicles (AVs) can perform low-level control tasks but are not always capable of proper decision-making. This paper presents a concept of eye-based maneuver control for AV-pedestrian interaction. Previously, it was unknown whether the AV should conduct a stopping maneuver when the driver looks at the pedestrian or looks away from the pedestrian. A two-agent experiment was conducted using two head-mounted displays with integrated eye-tracking. Seventeen pairs of participants (pedestrian and driver) each interacted in a road crossing scenario. The pedestrians' task was to hold a button when they felt safe to cross the road, and the drivers' task was to direct their gaze according to instructions. Participants completed three 16-trial blocks: (1) Baseline, in which the AV was pre-programmed to yield or not yield, (2) Look to Yield (LTY), in which the AV yielded when the driver looked at the pedestrian, and (3) Look Away to Yield (LATY), in which the AV yielded when the driver did not look at the pedestrian. The driver's eye movements in the LTY and LATY conditions were visualized using a virtual light beam. Crossing performance was assessed based on whether the pedestrian held the button when the AV yielded and released the button when the AV did not yield. Furthermore, the pedestrians' and drivers' acceptance of the mappings was measured through a questionnaire. The results showed that the LTY and LATY mappings yielded better crossing performance than Baseline. Furthermore, the LTY condition was best accepted by drivers and pedestrians. Eye-tracking analyses indicated that the LTY and LATY mappings attracted the pedestrian's attention, while pedestrians still distributed their attention between the AV and a second vehicle approaching from the other direction. In conclusion, LTY control may be a promising means of AV control at intersections before full automation is technologically feasible.

An important question in the development of automated vehicles (AVs) is which driving style AVs should adopt and how other road users perceive them. The current study aimed to determine which AV behaviours contribute to pedestrians' judgements as to whether the vehicle is driving manually or automatically as well as judgements of likeability. We tested five target trajectories of an AV in curves: playback manual driving, two stereotypical automated driving conditions (road centre tendency, lane centre tendency), and two stereotypical manual driving conditions, which slowed down for curves and cut curves. In addition, four braking patterns for approaching a zebra crossing were tested: manual braking, stereotypical automated driving (fixed deceleration), and two variations of stereotypical manual driving (sudden stop, crawling forward). The AV was observed by 24 participants standing on the curb of the road in groups. After each passing of the AV, participants rated whether the car was driven manually or automatically, and the degree to which they liked the AV's behaviour. Results showed that the playback manual trajectory was considered more manual than the other trajectory conditions. The stereotype automated 'road centre tendency' and 'lane centre tendency' trajectories received similar likeability ratings as the playback manual driving. An analysis of written comments showed that curve cutting was a reason to believe the car is driving manually, whereas driving at a constant speed or in the centre was associated with automated driving. The sudden stop was the least likeable way to decelerate, but there was no consensus on whether this behaviour was manual or automated. It is concluded that AVs do not have to drive like a human in order to be liked.

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.

Several papers by Eckhard Hess from the 1960s and 1970s report that the pupils dilate or constrict according to the interest value, arousing content, or mental demands of visual stimuli. However, Hess mostly used small sample sizes and undocumented luminance control. In a first experiment (N = 182) and a second preregistered experiment (N = 147), we replicated five studies of Hess using modern equipment. Our experiments (1) did not support the hypothesis of gender differences in pupil diameter change with respect to baseline (PC) when viewing stimuli of different interest value, (2) showed that solving more difficult multiplications yields a larger PC in the seconds before providing an answer and a larger maximum PC, but a smaller PC at a fixed time after the onset of the multiplication, (3) did not support the hypothesis that participants' PC mimics the pupil diameter in a pair of schematic eyes but not in single-eyed or three-eyed stimuli, (4) did not support the hypothesis of gender differences in PC when watching a video of a male trying to escape a mob, and (5) supported the hypothesis that arousing words yield a higher PC than non-arousing words. Although we did not observe consistent gender differences in PC, additional analyses showed gender differences in eye movements towards erogenous zones. Furthermore, PC strongly correlated with the luminance of the locations where participants looked. Overall, our replications confirm Hess's findings that pupils dilate in response to mental demands and stimuli of an arousing nature. Hess's hypotheses regarding pupil mimicry and gender differences in pupil dilation did not replicate.

Much psychological research uses pupil diameter measurements to investigate the cognitive and emotional effects of visual stimuli. A potential problem is that accommodating at a nearby point causes the pupil to constrict. This study examined to what extent accommodation is a confounder in pupillometry research. Participants solved multiplication problems at different distances (Experiment 1) and looked at line drawings with different monocular depth cues (Experiment 2) while their pupil diameter, refraction, and vergence angle were recorded using a photorefractor. Experiment 1 showed that the pupils dilated while performing the multiplications, for all presentation distances. Pupillary constriction due to accommodation was not strong enough to override pupil dilation due to cognitive load. Experiment 2 showed that monocular depth cues caused a small shift in refraction in the expected direction. We conclude that, for the young student sample we used, pupil diameter measurements are not substantially affected by accommodation.

External human-machine interfaces (eHMIs) may be useful for communicating the intention of an automated vehicle (AV) to a pedestrian, but it is unclear which eHMI design is most effective. In a crowdsourced experiment, we examined the effects of (1) colour (red, green, cyan), (2) position (roof, bumper, windshield), (3) message (WALK, DON'T WALK, WILL STOP, WON'T STOP, light bar), (4) activation distance (35 or 50 m from the pedestrian), and (5) the presence of visual distraction in the environment, on pedestrians' perceived safety of crossing the road in front of yielding and non-yielding AVs. Participants (N = 1434) had to press a key when they felt safe to cross while watching a random 40 out of 276 videos of an approaching AV with eHMI. Results showed that (1) green and cyan eHMIs led to higher perceived safety of crossing than red eHMIs; no significant difference was found between green and cyan, (2) eHMIs on the bumper and roof were more effective than eHMIs on the windshield, (3) for yielding AVs, perceived safety was higher for WALK compared to WILL STOP, followed by the light bar; for non-yielding AVs, a red bar yielded similar results to red text, (4) for yielding AVs, a red bar caused lower perceived safety when activated early compared to late, whereas green/cyan WALK led to higher perceived safety when activated late compared to early, and (5) distraction had no significant effect. We conclude that people adopt an egocentric perspective, that the windshield is an ineffective position, that the oftenrecommended colour cyan may have to be avoided, and that eHMI activation distance has intricate effects related to onset saliency.

Modern computerized vehicles offer the possibility of changing vehicle parameters with the aim of creating a novel driving experience, such as an increased feeling of sportiness. For example, electric vehicles can be designed to provide an artificial sound, and the throttle mapping can be adjusted to give drivers the illusion that they are driving a sports vehicle (i.e., without altering the vehicle's performance envelope). However, a fundamental safety-related question is how drivers perceive and respond to vehicle parameter adjustments. As of today, human-subject research on throttle mapping is unavailable, whereas research on sound enhancement is mostly conducted in listening rooms, which provides no insight into how drivers respond to the auditory cues. This study investigated how perceived sportiness and driving behavior are affected by adjustments in vehicle sound and throttle mapping. Through a within-subject simulator-based experiment, we investigated (1) Modified Throttle Mapping (MTM), (2) Artificial Engine Sound (AES) via a virtually elevated rpm, and (3) MTM and AES combined, relative to (4) a Baseline condition and (5) a Sports car that offered increased engine power. Results showed that, compared to Baseline, AES and MTM-AES increased perceived sportiness and yielded a lower speed variability in curves. Furthermore, MTM and MTM-AES caused higher vehicle acceleration than Baseline during the first second of driving away from a standstill. Mean speed and comfort ratings were unaffected by MTM and AES. The highest sportiness ratings and fastest driving speeds were obtained for the Sports car. In conclusion, the sound enhancement not only increased the perception of sportiness but also improved drivers' speed control performance, suggesting that sound is used by drivers as functional feedback. The fact that MTM did not affect the mean driving speed indicates that drivers adapted their "gain" to the new throttle mapping and were not susceptible to risk compensation.

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.

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about data-driven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behavior, and color-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were skeptical about higher levels of decision support, noting that forming an overall picture of the candidate's abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test.

Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher?s control. Here, we predicted fuel consumption using test track data, an approach that allowed for location specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Me?gane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption.

Various external human-machine interfaces (eHMIs) have been proposed that communicate the intent of automated vehicles (AVs) to vulnerable road users. However, there is no consensus on which eHMI concept is most suitable for intent communication. In nature, animals have evolved the ability to communicate intent via visual signals. Inspired by intent communication in nature, this paper investigated three novel and potentially intuitive eHMI designs that rely on posture, gesture, and colouration, respectively. In an online crowdsourcing study, 1141 participants viewed videos featuring a yielding or non-yielding AV with one of the three bio-inspired eHMIs, as well as a green/red lightbar eHMI, a walk/don't walk text-based eHMI, and a baseline condition (i.e., no eHMI). Participants were asked to press and hold a key when they felt safe to cross and to answer rating questions. Together, these measures were used to determine the intuitiveness of the tested eHMIs. Results showed that the lightbar eHMI and text-based eHMI were more intuitive than the three bio-inspired eHMIs, which, in turn, were more intuitive than the baseline condition. An exception was the bio-inspired colouration eHMI, which produced a performance score that was equivalent to the text-based eHMI when communicating 'non-yielding'. Further research is necessary to examine whether these observations hold in more complex traffic situations. Additionally, we recommend combining features from different eHMIs, such as the full-body communication of the bio-inspired colouration eHMI with the colours of the lightbar eHMI. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

It may be necessary to introduce new modes of communication between automated vehicles (AVs) and pedestrians. This research proposes using the AV’s lateral deviation within the lane to communicate if the AV will yield to the pedestrian. In an online experiment, animated video clips depicting an approaching AV were shown to participants. Each of 1104 participants viewed 28 videos twice in random order. The videos differed in deviation magnitude, deviation onset, turn indicator usage, and deviation-yielding mapping. Participants had to press and hold a key as long as they felt safe to cross, and report the perceived intuitiveness of the AV’s behaviour after each trial. The results showed that the AV moving towards the pedestrian to indicate yielding and away to indicate continuing driving was more effective than the opposite combination. Furthermore, the turn indicator was regarded as intuitive for signalling that the AV will yield. Practitioner Summary: Future automated vehicles (AVs) may have to communicate with vulnerable road users. Many researchers have explored explicit communication via text messages and led strips on the outside of the AV. The present study examines the viability of implicit communication via the lateral movement of the AV.

Gibson and Crooks (1938) argued that a ‘field of safe travel’ could qualitatively explain drivers' steering behavior on straights, curved roads, and while avoiding obstacles. This study aims to quantitatively explain driver behavior while avoiding obstacles on a straight road, and quantify the ‘Driver's Risk Field’ (DRF). In a fixed-based driving simulator, 77 (7 longitudinal and 11 lateral) positions of the obstacles were used to quantify the subjectively perceived and objectively (maximum absolute steering angle) measured DRF for eight participants. The subjective response was a numerical answer to the question “How much steering do you think you need at this moment in time?” The results show that the propagation of the width of the DRF, along the longitudinal distance, resembled an hourglass shape, and all participants responded to obstacles that were placed beyond the width of the car. This implies that the Driver's Risk Field is wider than the car width.