The rise of 4K XR displays has increased demand for highresolution content, raising network and computational costs. To address these challenges while preserving perceptual quality, this study proposes binocular asymmetric quality degradation, leveraging findings that perceived quality is dominated by the higherfidelity view and acuity differences between dominant and nondominant eyes. A total of 41 participants viewed four types of 4 K content (video clip, game, UI, web) under two conditions: both eyes receiving the original video, or one eye receiving a lower-resolution or higher-compression version. Participants selected which appeared higher in quality. Using the Just Objectionable Difference (JOD) scale, results indicated participants generally failed to notice quality degradation at one-third of the original resolution and across all tested compression levels. Content-specific differences emerged: for game content, resolution degradation was unnoticed at any level, whereas compression artifacts became noticeable from Constant Rate Factor (CRF) 60 onward. Additionally, comparing JOD values for dominant versus non-dominant eye-based asymmetric quality degradation revealed that preserving original resolution in the dominant eye improved perceived quality for UI content, whereas maintaining original compression quality in the dominant eye was more critical for game content. Finally, to evaluate the JOD results in an objective context, we examined the alignment between objective quality metrics and subjective asymmetry tolerances. Video Multi-Method Assessment Fusion (VMAF) showed the strongest correspondence; participants did not notice asymmetric quality degradation at a VMAF score of approximately 46.76, confirmed by an additional validation test. The study supports content-adaptive asymmetric quality degradation in XR to efficiently reduce computational overhead while maintaining visual fidelity.

This study explores how human and AI visual attention differ in a short-term prediction task, particularly in the moments before an accident is about to happen. Since real-world studies of this kind would pose ethical and safety risks, we employed virtual reality (VR) to simulate an accident scenario. In the scenario, the driver approaches a fork in the road, knowing that one path would lead off a cliff crashing the car fatally-as the fork comes closer, the other, safe, path is suddenly blocked by trees, forcing the driver to make a split-second decision where to go. A total of $N=71$ drivers completed the task, and we asked another $N=30$ observers to watch short video clips leading up to the final event and to predict which way the driver would take. We then compared both prediction accuracy as well as attention patterns-how focus is distributed across objects-with AI systems, including vision language models (VLMs) and vision-only models. We found that overall, prediction performance increased as the accident time point approached; interestingly, humans fared better than AI systems overall except for the final time period just before the event. We also found that humans adapted their attention dynamically, shifting focus to important scene elements before an event, whereas AI attention remained static, overlooking key details of the scene. Importantly, as the accident time point approached, human-AI attentional alignment decreased, even though both types of models improved in prediction accuracy. Despite distinct temporal trajectories-vision-only models declining from an early advantage and VLMs peaking in the middle-both models achieved low to zero alignment with human attention. These findings highlight a critical dissociation: AI models make accurate predictions, but rely on visual strategies diverging from human processing, underscoring a gap between explainability and task performance.

This study investigates the effectiveness of augmented reality (AR) signals in camera monitor systems (CMS) for enhancing safety during lane changes. Seventy participants used seven side mirror conditions, including traditional side mirrors and six CMS conditions with and without AR signals. Results showed that CMS with AR signals significantly reduced the number of collisions and reaction time compared to CMS without AR signals.

Our work for the first time evaluates the effectiveness of visual and acoustic warning systems in an accident situation using a realistic, immersive driving simulation. In a first experiment, 70 participants were trained to complete a course at high speed. The course contained several forks where a wrong turn would lead to the car falling off a cliff and crashing – these forks were indicated either with a visual warning sign for a first, no-sound group or with a visual and auditory warning cue for a second, sound group. In a testing phase, right after the warning signals were given, trees suddenly fell on the road, leaving the (fatal) turn open. Importantly, in the no-sound group, 18 out of 35 people still chose this turn, whereas in the sound group only 5 out of 35 people did so – the added sound therefore had a large and significant increase in situational awareness. We found no other differences between the groups concerning age, physiological responses, or driving experience. In a second replication experiment, the setup was repeated with another 70 participants without emphasis on driving speed. Results fully confirmed the previous findings with 17 out of 35 people in the no-sound group versus only 6 out of 35 in the sound group choosing the turn to the cliff. With these two experiments using a one-shot design to avoid pre-meditation and testing naïve, rapid decision-making, we provide clear evidence for the advantage of visual-auditory in-vehicle warning systems for promoting situational awareness.

Emergency situations during car driving sometimes force the driver to make a sudden decision. Predicting these decisions will have important applications in updating risk analyses in insurance applications, but also can give insights for drafting autonomous vehicle guidelines. Studying such behavior in experimental settings, however, is limited by ethical issues as it would endanger peoples' lives. Here, we employed the potential of virtual reality (VR) to investigate decision-making in an extreme situation in which participants would have to sacrifice others in order to save themselves. In a VR driving simulation, participants first trained to complete a difficult course with multiple crossroads in which the wrong turn would lead the car to fall down a cliff. In the testing phase, obstacles suddenly appeared on the "safe" turn of a crossroad: for the control group, obstacles consisted of trees, whereas for the experimental group, they were pedestrians. In both groups, drivers had to decide between falling down the cliff or colliding with the obstacles. Results showed that differences in personality traits were able to predict this decision: in the experimental group, drivers who collided with the pedestrians had significantly higher psychopathy and impulsivity traits, whereas impulsivity alone was to some degree predictive in the control group. Other factors like heart rate differences, gender, video game expertise, and driving experience were not predictive of the emergency decision in either group. Our results show that self-interest related personality traits affect decision-making when choosing between preservation of self or others in extreme situations and showcase the potential of virtual reality in studying and modeling human decision-making.

Understanding how humans make decisions in challenging situations - such as trying to save peoples' lives even though this endangers one's own life - is crucial in optimizing rescue operations and dealing with natural disasters and other crises. The experimental study of these decisions, however, has often been done using text-based surveys, which is known to emphasize rational and reflective judgments. Here, we used virtual reality to investigate decision-making in a real-world context, in which a decision needs to be made intuitively under time pressure - for this we simulated an accident situation in an immersive virtual driving scenario. In the scenario, participants were told to race a course as fast as possible. After training, participants were confronted with the sudden appearance of pedestrians on the race course. We observed three different behaviors: group one ignored the pedestrians and/or hit the accelerator, group two hit the brake, and group three tried to steer the car to avoid pedestrians. We found that most Avoid-group participants had more real and virtual driving experience compared to the other two groups and they also felt more competent during the game as measured by subjective game experience questionnaires. Importantly, results from established personality questionnaires showed that participants who did not brake (therefore potentially harming the pedestrians) had significantly lower scores in perspective-taking and higher scores in psychopathy compared to participants who tried to avoid the accident situation. Our results demonstrate that personality differences to some degree are able to predict intuitive decision-making and that such processes can be studied in a controlled, immersive VR simulation.