In automated vehicles, beeps are widely used as alarms and feedback. However, as automation advances, there is a need to explore subtler, contextually sound-based notifications for non-urgent situations. While auditory interfaces for take-over requests have been studied, limited attention has been given to using soundscapes for such alerts. This paper designed and evaluated soundscapes using existing driving-related sounds – amplified road noise and/or dimmed background music – for scheduled take-over situations. A driving simulator study showed that these soundscapes enhanced reaction time, situation awareness, and acceptance without causing annoyance. Particularly, the combined condition (music dimming and road noise amplifying) supported higher driver awareness and responsiveness. These findings suggest that soundscapes can offer safer, more intuitive take-over alerts by embedding information into familiar audio cues. This study contributes to developing soundscapes as novel alert mechanisms that integrate seamlessly with the driving environment to enhance both safety and user experience in automated vehicles.

Abstract Day-night appearance shift degrades vision for driving and surveillance. Low illumination, mixed lighting, glare, and sensor noise weaken cues for detection, segmentation, localisation, and tracking. We survey illumination domain translation for images and video, focusing on day to night and night to day mapping that changes illumination while preserving geometry, semantics, and temporal coherence. We relate illumination modelling and colour transfer to learning- based methods, and develop an IDT-specific constraint centric taxonomy linking supervision, five domain gap factors, and five families of constraints and priors to typical failure modes. Using this taxonomy, we organise 30 representative methods and summarise 23 datasets. We also report an artefact availability audit of 34 published methods: 29 release code, 22 provide pretrained weights, 21 specify licences, and 19 provide reproducibility packages. Finally, we recommend evaluation spanning perceptual quality, semantic preservation, downstream utility, and temporal stability, and we synthesise the literature using an evidence-aligned P/S/D/T protocol that highlights recurring failure modes and evaluation gaps.

Figure 1: The 233 countries and territories with dashcam footage in CROWD dataset [2].The colouration is based on the logarithm of the total recorded time per country or territory, calculated as log 𝑒 (1 + time in seconds), to reduce the skew from outliers such as the United States (with 707.76 hours available).The black dots show the 2,495 cities in the dataset.The labels under images show the corresponding YouTube video ID.The frame on the bottom left shows an example of object detection using YOLOv11x with identified objects such as pedestrians, vehicles, and traffic signs; in this image, the labels 'id' refer to the unique ID of the detected object with the type mentioned later and end with the confidence of detection of the object.

Cyclists face significant risks from vehicles that overtake too closely.Through crowdsourcing (N = 200) and driving simulator (N = 20) experiments, this study examines driver behaviour in seven scenarios: laser projection, road sign, road marking, car projection, centre line and side line markings (baseline), cycle lane and no road markings.Crowdsourced participants consistently underestimated overtaking distances, particularly at wider gaps, despite feeling safer with greater distances.The simulation results showed that drivers maintained an average passing distance of 3.4 m when not constrained by traffic, exceeding the 1.5 m law of the European Union.However, interventions varied in effectiveness: while laser projection was preferred, it did not significantly increase passing distance.In contrast, a dedicated cycle lane and a solid centreline led to the greatest improvements.These findings highlight the discrepancies between perceived and actual safety and provide insight for policy interventions to enhance cyclist protection in the EU.

Drones will soon deliver packages to recipients in public spaces, where drones may encounter difficulties identifying safe drop-off locations. Such uncertainties can reduce trust and raise safety concerns. This augmented reality study investigates how recipients perceive being asked to guide the drone in uncertain situations using hand gestures, and to what extent they feel comfortable with different levels of involvement. Results show that participants preferred a basic level of involvement, which received higher trust and usability scores than either no or high involvement. We recommend involving recipients in the final stage of delivery to not only support drone operations but also improve recipient trust and clarity in uncertain conditions.

The use of drones is expected to increase for delivering groceries or medical equipment to individuals. Understanding how people perceive drone behavior, specifically in terms of approach trajectories and delivery methods, and identifying factors that induce feelings of uncertainty is crucial for perceived safety and trust. This virtual reality experiment investigated the impact of drone approach trajectories and delivery methods on feelings of uncertainty. Forty-five participants observed a drone approaching in an orthogonal or a curved path and either, delivering packages by landing or using a cable while hovering above eye level. We found that participants felt uncertain and unsafe, especially when looking up at drones approaching with orthogonal paths. Curved paths led to lower feelings of uncertainty, with comments such as being more natural, trustful, and safe. Feelings of uncertainty arose while landing on the ground due to altitude changes and potential collision concerns. Using a cable instead of actually landing for delivery reduced feelings of uncertainty and increased trust. The study recommends drones avoid hovering near humans, especially after landing. Furthermore, the study suggests exploring design solutions, including design aesthetics and human–machine interfaces, that clearly convey drone intentions to help reduce feelings of uncertainty.

We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating scientific progress and promoting an inclusive scientific community. However, there is little documentation of these aspects within the automotive user research community. To address this, we report two studies that identify (1) community perspectives on motivators and barriers to data sharing, and (2) how openness and transparency have changed in papers published at AutomotiveUI over the past 5 years. We show that while open science is valued by the community and openness and transparency have improved, overall compliance is low. The most common barriers are legal constraints and confidentiality concerns. Although research published at AutomotiveUI relies more on quantitative methods than research published at CHI, openness and transparency are not as well established. Based on our findings, we provide suggestions for improving openness and transparency, arguing that the motivators for open science must outweigh the barriers. All supporting materials are freely available at: https://osf.io/zdpek/

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver–pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.

Future automated vehicles may be equipped with external human-machine interfaces (eHMIs) capable of signaling to pedestrians whether or not they can cross the road. There is currently no consensus on the correct colors for eHMIs. Industry and academia have already proposed a variety of eHMI colors, including red and green, as well as colors that are said to be neutral, such as cyan. A confusion that can arise with red and green is whether the color refers to the pedestrian (egocentric perspective) or the automated vehicle (allocentric perspective). We conducted two crowdsourcing experiments (N = 2000 each) with images depicting an automated vehicle equipped with an eHMI in the form of a rectangular display on the front bumper. The eHMI had one out of 729 colors from the RGB spectrum. In Experiment 1, participants rated the intuitiveness of a random subset of 100 of these eHMIs for signaling `please cross the road', and in Experiment 2 for `please do NOT cross the road'. The results showed that for `please cross', colors close to pure green were considered the most intuitive. For `please do NOT cross', colors close to pure red were rated as the most intuitive, but with high standard deviations among participants. In addition, some participants rated green colors as intuitive for `please do NOT cross'. Results were consistent for men and women and for colorblind and non-colorblind persons. It is concluded that eHMIs should be green if the eHMI is intended to signal `please cross', but green and red should be avoided if the eHMI is intended to signal `please do NOT cross'. Various neutral colors can be used for that purpose, including cyan, yellow, and purple.

Head-mounted displays (HMDs) offer immersion and binocular disparity. This study investigated whether an HMD yields better object-alignment performance than a conventional monitor in virtual environments that are rich in pictorial depth cues. To determine the effects of immersion and disparity separately, three hardware setups were compared: 1) a conventional computer monitor, yielding low immersion, 2) an HMD with binocular-vision settings (HMD stereo), and 3) an HMD with the same image presented to both eyes (HMD mono). Two virtual environments were used: a street environment in which two cars had to be aligned (target distance of about 15 m) and an office environment in which two books had to be aligned (target distance of about 0.7 m, at which binocular depth cues were expected to be important). Twenty males (mean age = 21.2, SD age = 1.6) each completed 10 object-alignment trials for each of the six conditions. The results revealed no statistically significant differences in object-alignment performance between the three hardware setups. A self-report questionnaire showed that participants felt more involved in the virtual environment and experienced more oculomotor discomfort with the HMD than with the monitor.