Although waiting during loading is a common online experience, the overall understanding of loading indicator designs and their impact on user experience (UX) remains limited. To address this, we conducted a systematic review of 38 articles, focusing on the following research questions: (1) What aspects of UX are affected by loading times? (2) What is the relationship between loading time and UX? (3) How can loading indicators be designed? (4) What is the impact of different loading indicator designs on UX? As a result, this study highlights the different dimensions of UX during the loading process, reveals how perceived loading time distortion affects UX, and identifies different designs of loading indicators, including those most effective in optimizing UX. This study contributes to the body of knowledge on the waiting experience, especially for those involved in optimizing indicator designs, and proposes future research directions for designing more diverse indicators.

Waiting for system loading is a common scenario that often diminishes user experience, leading to dissatisfaction. Well-established visual indicators like progress bars can not directly apply to the interactions with voice assistants (VAs) like Siri. As VAs continue to rise in popularity, this research aims to explore the design of auditory indicators, particularly human speech, for optimizing waiting experiences in Voice User Interfaces (VUIs). We first organized focus groups (N=35) to identify design considerations for speech indicators, uncovering design opportunities in integrating explanations and humor. Subsequently, we conducted an empirical study (N=30) to evaluate the effects of speech indicators with two levels of explanation and humor on the waiting experience, measured by attention, perceived time, pleasure, and overall satisfaction, during both short and long loading durations. Our findings suggest significant potential for incorporating explanations and humor into VUIs, offering actionable insights for designing effective speech indicators that improve waiting experiences.

The vibrotactile modality has great potential for presenting takeover requests (TORs) to get distracted drivers back into the control loop. However, few studies investigate the effectiveness of directional vibrotactile TORs. Whether TORs should be directed toward the direction of hazard (stimulus-response incompatibility) or the direction of avoidance action (stimulus-response compatibility) remains inconclusive. The present study explored the impact of directional vibrotactile TORs (toward-hazard, toward-action, and non-directional) on takeover performance. The influences of TORs lead time (3 s, 4 s, 6 s, and 8 s) and non-driving related tasks (NDRTs) (playing Tetris games and monitoring the road) on the effect of directional TORs were also probed. A total of 48 participants were recruited for our simulated driving study. Results showed that when drivers were engaged in NDRTs during automated driving, directional TORs were more effective than non-directional TORs. Specifically, at the lead times of 6 s and 8 s, both toward-hazard and toward-action TORs could shorten steering response times, compared with the non-directional TORs. At the lead times of 3 s and 4 s, toward-action TORs were more beneficial, as the maximum lateral acceleration was smaller than toward-hazard and non-directional TORs. However, when drivers monitored the road during automated driving, no obvious difference existed between directional and non-directional TORs, regardless of how long the lead time was. The findings in the present study shed light on the design and implementation of the tactile takeover system for automobile designers.

Effective communication between automated vehicles and human drivers in manual driving vehicles is of great importance for traffic safety during the transition phase of automated vehicles. Gestures, which were widely used in road users’ communication, were promising in conveying the intentions of automated vehicles naturally and intuitively without extra learning costs. However, the effect of gestures in conveying the automated vehicles’ intentions on human understanding remains unknown. This study proposed the idea of adopting mechanical arms to produce gestures. An experiment based on video recordings was conducted to explore the effect of arms type (slow-waved mechanical arm (80 beats/min) vs. fast waved mechanical arm (120 beats/min) vs. human arm) and gesture type (taking the road vs. giving the road) on the participants’ objective responses and subjective opinions. A total of 30 participants were recruited as human drivers in a manual driving vehicle, who received and responded to the gestures transferred by an encountering automated vehicle. Results indicated that regardless of the gesture type, the slow-waved mechanical arm led to a longer response time (mean ± SD: 4.871 ± 0.947 s) and lower response accuracy (88.3 ± 32.4%) when compared with the human arm (response time: 4.457 ± 0.727 s, response accuracy: 95.0 ± 22.0%). It was also rated less understandable and comfortable than the human arm. Nevertheless, the fast-waved mechanical arm not only exerted as fast (4.484 ± 0.818 s) and accurate responses (98.3 ± 12.9%) as the human arm but was also rated as understandable, polite, and comfortable as the human arm. This indicated the implication of conveying gestures by utilizing the fast-waved mechanical arm (120 beats/min) to facilitate effective communication from automated vehicles to human drivers in manual driving vehicles. The present study’s findings provided reference implications for manufacturers and designers to adopt this gesture-based communication method to develop safe and user-friendly automated vehicles.

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.