An external human-machine interface (eHMI) is a novel tool that facilitates efficient interactions between pedestrians and automated vehicles (AVs). However, as pedestrians become accustomed to communicating with AV through eHMI, there is a risk of exhibiting less cautious behavior, which can increase the likelihood of accidents. This study aimed to examine the effects of methods to mitigate the side effects of communication with AV through an eHMI. A total of 132 participants participated in a Virtual Reality experiment to investigate the existence of negative effects and to explore mitigation methods. We designed five experimental conditions to manipulate the tone of text messages (Allocentric: “After you,” Egocentric: “I’ll stop”) and test the effect of mitigation methods, such as hiding the text messages on the eHMI when the AV is stopped (No eHMI, eHMI-A, eHMI-E, HeHMI-A, HeHMI-E). The results revealed that pedestrians were more likely to exhibit less careful behavior in the eHMI condition than in the No eHMI condition. The disappearance of text messages on the eHMI prompted pedestrian attention allocation toward traffic situations, leading to a reduction in accident risk. In particular, the mitigation method was most effective for safe crossing when pedestrians were continuously exposed to the eHMI, presenting an egocentric message. Our findings contribute to the design of text-based eHMIs for pedestrian decision-making when crossing and enhancing traffic safety.

One of the challenges in introducing automated service vehicles (ASVs) to parking areas is guiding pedestrians to make quick and appropriate crossing decisions based on an understanding of what the ASV typically does. This study aims to find effective communication strategies for ASVs for pedestrian crossings in Japanese parking areas. Virtual Reality experiments were conducted with 40 participants facilitating vehicle behavior (Normal Deceleration, Early Deceleration, Early Stop) and text-based external human-machine interfaces (eHMIs; No eHMI, “After you,” “I’ll stop,” “In Automated Driving”) as tools of communication with two types of ASVs (bus and golf cart) in parking areas. The results indicated that vehicle behavior significantly influenced pedestrian crossing decisions, and pedestrians began to cross when the ASV decelerated early and stopped not too far from them (Early Deceleration). Self-reported measure analyses revealed that text messages containing the intent of the ASV's actions or instruction (After you, I’ll stop) with Early Deceleration would probably lead to pedestrians’ favorable attitudes toward automated buses. These findings contribute to communication designs based on implicit communication for pedestrian crossing decision-making in parking areas with a clear interpretation of ASV’s intentions.

The utilization of gaze behavior for control has been studied as one of the hands-free control methods. With the recent spread of Head Mounted Display devices, it has become a vital issue to establish hands-free control methods. Previously proposed approaches using the eye gaze behavior have several problems to be solved for natural manipulation during content browsing. Therefore, in this study, considering the case where the gaze point leaves the decision area for a short period, we propose a method that enables accurate control prediction based on gaze behavior during content browsing by counting the cumulative gaze dwell time. Through experimentation, we confirmed that the proposed method would be effective for the accurate prediction of user intention. The limitation of our method and further issues to be addressed were also clarified.

Due to the technical advances of automated vehicles (AVs), new uncertainties for human road users arise. To overcome these uncertainties, driving strategies of AVs might be aligned to human interaction styles. In vehicle-vehicle interactions, driving behavior is informed by remaining time gaps between vehicles. This video-based experiment investigated the influence of gap sizes and the measurement method on driving decisions. N=32 participants experienced a highly automated drive in which their AV approached narrow passages. The time to arrival (TTA) of the oncoming traffic was varied. Participants had to decide to drive first or second, indicate their decision certainty, and the situation’s criticality. The videos were presented in ascending, descending, and random order. Moreover, participants adjusted the TTA at which they would drive first and second. The results indicated a higher probability of driving first and lower criticality with increasing TTA. Decision certainty was lowest around the 50% threshold, while longer and shorter TTAs resulted in higher certainty. Results differed between the methods. The findings provide guidance for the design of automated systems to mimic human driving behavior.

Driving automation leads drivers to be disengaged by dynamic driving tasks, but the need for the driver to monitor traffic environment is existed in a partial automation. Monotonous road situations can produce drowsiness, resulting in poor driving performance. Conversation in automated vehicles is expected to mitigate fatigued driving. However, few studies have examined effects of conversation. This paper reports a driving simulator study describing impacts of passenger conversation on driver fatigue and driving performance regarding the relationship between a driver and a passenger. Participants were categorized into two groups: a passenger's friends (ConvR) and people who had not met the passenger before (ConvNR) to examine whether the acquaintance with the passenger affects driver behaviour, driving performance, fatigue and workload. The results showed that drivers in ConvNR felt lesser sleepy and found it easier to drive than the drivers in ConvR, but no difference of driver behaviour and driving performance was observed between two groups. In addition, regardless of the relationship, making casual conversation led decreased fatigue and workload in comparison with solo driving without conversation. The practical implication for further conversation methods to regard driver fatigue and driving performance in the automated vehicle is discussed.

Partially automated vehicles require human drivers to continuously monitor system status and external traffic environment. The `Out-of-The-Loop' phenomenon is a major issue with partially automated driving, and how to keep drivers remaining the driving loop and maintaining situation awareness for coping with system limits is an important question from perspective of human factors. In order to overcome fatigued driving caused by automation casual conversation with a passenger and a system was conducted, and to prevent the loss of situation awareness the information of traffic situation was presented to drivers. This study simulated two critical events: merging-car and curve road events to examine the impacts of conversation and the attention arousal. The statistical results found making conversation is significantly helpful in diminishing driver fatigue and boredom. Drivers' response to an impending collision on the merging-lane was quicker in driving during conversation with the system than with the passenger and solo driving, whereas no difference was observed in the curve event. The practical implication for further consideration with respect to the timing of attention arousal and the amount of information is discussed.

Visual field defects, which are partial losses of the visual field, may have an impact on driver behavior. They affect the driver's information processing and are a possible cause of traffic accidents. Some patients who suffer from optic diseases related to visual field defects try to compensate for their disability by more actively moving their head in order to look around more carefully. Compensation is a driving strategy used to maintain safe driving regardless of the driver impairment. Several studies have shown that compensation is effective at enhancing the driving performance. However, few studies have examined both the compensation of drivers who have advanced visual field defects, and a quantitative analysis of driver performance while driving under such compensation. For these reasons, by using a driving simulator and eyeglasses that can imitate advanced visual field defects, this study investigates driver compensatory head movements to better understand the extent to which they are effective in avoiding pedestrian collisions. The results indicate that active head movements are efficient at reducing the number of pedestrian collisions compared with driving without such compensation. However, they are insufficient in terms of collision avoidance when compared to driving without a visual impairment. In other words, the results imply that there are assorted unavoidable circumstances that occur even when the driver attempts to compensate their visual impairment as much as possible.

This study investigates limitations of driver's compensation under visual field contraction. Two types are distinguished for the compensation: (A) reducing the vehicle speed, and (B) looking around frequently. An experiment using a driving simulator is conducted to evaluate to what extent accidents can be avoided by compensation. The participants are healthy drivers wearing eyeglasses that contract the participant's field of view. The results show that even if drivers perform compensation under visual field contraction as much as possible, they do not react early like when they drove normally without visual field contraction because they delay in perception of traffic condition. The accident rate is also high under the condition of visual field contraction. These findings suggest the need to design an assistance system that enhances drivers' ability to perceive traffic condition for people with visual field contraction.