Augmented Reality Head-Up Display (AR-HUD) is a promising solution to the current warning system distraction problem. However, how to effectively convey warnings through AR graphics is still unclear. This study examines the effectiveness of the contact-analog graphic compared to the bounding box graphic in various collision types and traffic densities. Forty-eight participants watched AR-augmented driving videos and were instructed to respond to critical events. Reaction time, response rate, and subjective evaluations were compared for rear-end and pedestrian collisions in different traffic densities under different warnings. Both bounding box and contact-analog warnings improved driving performance compared to the non-warning group. The contact-analog warning performed better for rear-end collisions, while the bounding box warning had a lower reaction time for pedestrian collisions, regardless of traffic density.

Technically, Augmented Reality Head-Up Display (AR-HUD) technology can augment multiple targets in the scene. However, existing literature predominantly focuses on scenarios involving single-target augmentation. The understanding of multi-target augmentation and studies exploring effective presentation methods under such conditions are notably scarce. This study evaluates the efficacy of integrating color-based warning priority design across multi-target scenarios. 45 Participants in different warning modes (Equivalent, Hierarchical, and Baseline) view AR-augmented driving videos and respond to risky targets. Their behavioral performance and eye-tracking data are compared. Findings indicate that the equivalent warning mode, lacking in priority design, adversely affects driver performance, prolongs reaction times, and elevates saccade counts, and gaze entropy. Conversely, the hierarchical warning mode significantly ameliorates driver reaction times and the time to first fixation, while also reducing saccade counts and gaze entropy, demonstrating the efficacy of the warning priority design. The findings provide insight into the design of AR-HUD with multi-target augmentation.

Awareness of behavioral consequences significantly influences focus, priorities, and emotions. This study investigates the impact of informing drivers about potential takeover consequences on human-machine collaboration in conditional autonomous driving. We recruited 32 licensed drivers and randomly assigned them to groups with and without informed consequences. Each group completed 8 distinct takeover tasks, each with varying consequences of not taking over. We assessed takeover performance, subjective evaluations (situational awareness, workload), and physiological stress responses (electrocardiogram, electromyogram) to provide a comprehensive evaluation of takeover safety. Our findings indicate that drivers informed of the consequences demonstrated superior takeover performance, evidenced by increased time-to-collision, reduced maximum lateral acceleration, and decreased trajectory deviation. Additionally, disclosing consequences increased drivers’ perceived attentional demands, elevating workload while maintaining stable stress levels during takeover. Future research should explore how to balance workload with driving performance when informing drivers of the consequences of not taking over.

With the development of augmented reality (AR) technology, AR navigation tools have gradually received increasing attention. Few previous studies have comprehensively discussed the advantages and disadvantages of AR and 2D maps from a spatial cognition perspective. Through three experiments, this article investigated the effects of different AR navigation display methods on wayfinding performance and spatial knowledge acquisition. First, we compared participants’ performances in using AR and 2D map navigation aids for wayfinding and spatial knowledge tasks. Second, we developed a combined map and compared the usability of this map with that of an AR map. Then, we applied the focus + context technique to improve the combined map’s minimap and verified the improved map’s effectiveness by laboratory simulation. The AR map is superior to the 2D map in acquiring spatial knowledge. AR displays are crucial for helping individuals acquire landmark and route knowledge. The improved map expands the range of exocentric spatial information and highlights the landmark locations to provide comprehensive information about the environment; thus, the improved map can further assist in acquiring spatial knowledge.

The popularity of science videos is critical for the dissemination of knowledge, and predicting the popularity of a video is a hot topic among researchers. The existing research is mainly based on the videos’ content (e.g., theme) or video-related external information (e.g., comments). However, videos with different popularity can bring different learning states and viewing experiences (emotional awakening, flow experiences). In this paper, we utilize participants’ learning states when they watch videos to predict the popularity of the videos, depending on two modal indicators, i.e., contact physiological indicators recorded by a Biopac MP150 polygraph and noncontact gesture indices recorded by a Kinect V2 body camera which can obtain data of head position. We propose two classification prediction models with each modality of indicator and filter out the indicators that make sense for modeling. Results show that the meaningful indicators in the physiological modality through stepwise logistic regression are the standard deviation of normal to normal R-R intervals (SDNN) and high-frequency heart rate variability (HF). We find that participants had higher SDNN and lower HF when watching science video with high popularity (compared with low popularity one), and the accuracy rate of classification model is 81.6%. In a same way, the selected meaningful indicators in the gesture aspect are the maximum, the standard deviation (SD) of distance between the participants’ head and Kinect. We find that the maximum and SD of head distance are smaller when participants studied highly popular science videos (contrary to the less popular videos) and the accuracy rate is 73.7%. Combining the four indicators for modeling by the direct input method, the accuracy rate is 78.9%, and the SD of head distance is probably the most important indicator for predicting popularity of videos. These results indicate that it is feasible to predict video popularity by learning states.

Augmented reality head-up display (AR HUD) is a new technology in assisted driving, which can add extra information to the driving environment in real-time to help the driver better perceive road situation. AR HUD can enhance driving safety but may also encourage inattentional blindness. Hence, this study aims to examine whether AR HUD-induces inattentional blindness and determine whether workload intensifies their relationship. In experiment 1, 60 participants were randomly assigned to three groups and watched three types of augmented reality (AR)-augmented driving videos, respectively. They were instructed to respond to any critical events, but only their responses to road-crossing pedestrians were recorded. Results show that AR HUD reduces inattentional blindness when pedestrians are augmented but encourages inattentional blindness when pedestrians are not augmented. In experiment 2, 20 participants viewed AR-augmented driving videos of high and low workloads. Pedestrians were not augmented in all videos. Result reveals that a high workload induces more inattentional blindness than low workload. The finding confirms that AR HUD induces inattentional blindness, and a high workload will intensify this relationship. The future design of the AR HUD assisted-driving system should consider the risk of inattentional blindness and come up with corresponding countermeasures.

Warning systems play a crucial role in the takeover of conditional automated driving. However, the widely used single-stage warning systems in takeover had inevitable and critical issues in situation awareness (SA), driving stress, and takeover performance. As such, two-stage warning systems might be an optimal solution to alleviate these problems. On this basis, this study investigated the effect of warning types (single-stage vs two-stage warning systems) and non-driving related tasks (NDRTs) (playing Tetris game vs monitoring automated systems) on takeover. A total of 32 participants were recruited to join our driving-simulated study. These participants responded to different types of takeover warning systems upon receipt while engaging in NDRTs. Simultaneously, the SA, physiology stress, takeover performance, and acceptance data of the participants were recorded. Results showed that the drivers exhibited higher SA, lower physiology stress, better takeover performance, and higher acceptance ratings in the two-stage warning systems than in the single-stage warning systems. In conclusion, two-stage warning systems are promising in improving takeover safety based on connected vehicle technologies in the future. These findings can provide some guidelines for designers and engineers when applying the warning systems in automated driving.

With the development of cutting-edge technology in the area of driving performance, driver warning systems based on head up displays (HUD) are considered to have the potential to improve driving safety in the future. The location of HUD warning graphics is a vital component to ensure that drivers obtain information the first time and avoid cognitive tunneling when coming across hazards; however, few studies have critically examined this. The present study investigated the advantages of HUD in presenting warning graphics in comparison with traditional head down display (HDD) in vehicles, and further explored the effect of HUD location based on comprehensive indicators, including behavior performance, eye movement data, and subjective assessment. The results revealed that compared with HDD, presenting warning graphics to drivers on HUD could significantly improve driving performance and eye movement patterns, and HUD was the preferential mode for drivers. Results also demonstrated that presenting HUD warning graphics at a location of 8°below the sight line was associated with the worst results in driving performance, eye movement patterns and subjective assessment. Other locations of HUD presentation were not associated with any significant differences for most indicators. These findings have some reference implications for automobile designers as they construct and implement HUD warning systems.

This study aims to investigate the usability of a head-up display (HUD) in presenting warning messages during driving and create a new and effective vehicle early warning system for drivers. Two experiments were conducted. In Experiment 1, 36 drivers were randomly assigned to a group using HUD and a control group. The simulated driving performance of the two groups was compared to determine if the HUD graphic early warning system facilitates driving safety. Results revealed that the HUD-using group demonstrated better driving performance than the control group in terms of collision, mean deceleration, accelerator release reaction time, brake reaction time, reduced velocity, reduced energy, steering reaction time, mean reaction time, and minimum reaction time. We investigated the influence of the presentation mode of warning messages on simulated driving performance in Experiment 2. Forty-eight drivers were randomly assigned to an HUD warning group, an audio warning group, and an audiovisual group that integrated HUD and audio warning. The drivers in the HUD warning group performed better than those in the two other groups in terms of mean deceleration. The audiovisual group that integrated HUD and audio warning showed an advantage in reduced velocity. The findings indicated that HUD technology has the potential to promote safe driving by improving the early warning system.