Background

Research investigating the interactions between cyclists and automated vehicles (AVs) is very scarce. So far, only two photo-based studies (Hagenzieker et al., 2019; Rodríguez Palmeiro, van der Kint, Hagenzieker, van Schagen, & de Winter, 2018) and one study using animated videos (Vlakveld & Kint, 2019) have been performed and have generally found conservative dispositions of cyclists towards AVs.

Method

The aim of this study was to determine the main factors influencing cyclists’ crossing intentions when interacting with an AV as compared to a conventional vehicle (CV). A 360 video smartphone-based virtual reality experiment was performed and included 16 different scenarios resulting from four factors with two-levels each: vehicle type, gap size, vehicle speed, right of way. Additional factors considered in the study were trust in AVs, cyclists’ self-reported behaviour, Perceived Behavioural Control (PBC), and Perceived Risk (PR). Forty-seven individuals participated in the VR experiment. A multinomial logistic mixed regression model was developed and estimated.

Results

The primary factors influencing cyclists’ crossing intentions are the distance gap between the cyclist and the vehicle approaching the intersection and the right of way. Neither speed of the approaching vehicle, vehicle appearance, vehicle automation or Trust in AVs had a significant effect on the crossing intentions. Interestingly, participants’ statements whether they trusted AVs as compared to CVs was found to be a better predictor of the crossing intentions compared to their score on the Trust in AVs questionnaire. A positive relation was found between cycling slower or faster and PBC and a negative for PR.

Conclusions

The results of this study concur with previous studies. Cyclists are still cautious towards automated vehicles and do not adapt their behavior when interacting with them, at this point in time. Therefore, vehicle type and appearance did not have an effect on crossing intentions. However, future exposure to AVs may elicit behavioral adaptation from cyclists. Thus, the long term effects of AVs on cyclists should be studied. The relative trust cyclists have in AVs compared to CVs is more important than the absolute trust.

Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This study aims to determine the main factors influencing cyclists’ crossing intentions when interacting with an automated vehicle as compared to a conventional vehicle (CV) using a 360^◦ video-based virtual reality (VR) method. The considered factors in this study included vehicle type, gap size between cyclist and vehicle, vehicle speed, and right of way. Each factor had two levels. In addition, cyclist’s self-reported behavior and trust in automated vehicles were also measured. Forty-seven participants experienced 16 different crossing scenarios in a repeated measures study using VR. These scenarios are the result of combinations of the studied factors at different levels. In total, the experiment lasted 60 min. The results show that the gap size and the right of way were the primary factors affecting the crossing intentions of the individuals. The vehicle type and vehicle speed did not have a significant effect on the crossing intentions. Finally, the 360^◦ video-based VR method scored relatively high as a research method and comparable with the results of a previous study investigating pedestrians’ crossing intentions confirming its suitability as a research methodology to study cyclists’ crossing intentions.

This dissertation aims to understand the behavior of pedestrians and cyclists when interacting with automated vehicles (AVs). The role of AVs’ characteristics such as their physical appearance, whether a driver is present, the presence of external communication interfaces, and factors pertaining to the behavior of the vehicle were investigated using virtual reality road crossing experiments. In addition, psychological factors that could be affected by the presence of AVs were included.

Automated bus systems are a promising means of future first- and last mile public transport solutions, and can even possibly become a regular part of the public transport network. Therefore, many projects appear throughout Europe to pilot the feasibility of automated bus system implementation on various locations. Keeping up with the rapidly increasing pace in which these pilots appear, this report aimed to provide an overview of past, currently on-going, and concretely planned pilots with automated bus systems in Europe. Via extensive internet searches, exhausting personal networks, and gathering information from other sources, a detailed overview was developed. In the first version, established March 2020, 118 pilots were found which were characterized by vehicles with predominantly low speeds, low capacities, and short operation routes. In this final version, established February 2021, aside from additional information on known pilots, another 13 were found, making a total of 131 pilots throughout Europe. The search in itself proved to be difficult due to the often lacking detailed information of pilots, which was argued to be due to most scientific pilots being of recent years, and therefore often still on-going, and consequentially not having published any information yet on their research. Another difficulty arose due to the rapid increase of occurring pilots with automated buses, which leads to the report already being out-of-date as this report is being written. Therefore, this report was updated early 2021. Meanwhile, the Covid-19 pandemic situation appears a major issue for automated bus systems pilots during the year 2020. The results show that currently the vast majority of automated bus system pilots occur with the presence of a steward on board, due to legislation, technological challenges, as well as passengers requesting them, raising concerns regarding (e.g., economic) efficiency. Although there are a few automated bus systems that actively show efficient operation without on board stewards, this still appears to be a future development.

Automated bus systems are a promising means of future first- and last mile public transport solutions, and can even possibly become a regular part of the public transport network. Therefore, many projects appear throughout Europe to pilot the feasibility of automated bus system implementation on various locations. Keeping up with the rapidly increasing pace in which these pilots appear, this report aimed to provide an overview of past, currently on-going, and concretely planned pilots with automated bus systems in Europe. Via extensive internet searches, exhausting personal networks, and gathering information from other sources, a detailed overview was developed. 118 pilots were found which were characterized by vehicles with predominantly low speeds, low capacities, and short operation routes. The search in itself proved to be difficult due to the often lacking detailed information of pilots, which was argued to be due to most scientific pilots are of recent years, and therefore often still on-going, and have consequentially not published any information yet on their research. Another difficulty arose due to the rapid increase of occurring pilots with automated buses, which leads to the report already being out-of-date as this report is being written. Therefore, this report will be updated early 2021. Currently, the vast majority of automated bus system pilots occur with the presence of a steward on board, due to legislation, technological challenges, as well as passengers requesting them, raising concerns regarding (e.g., economic) efficiency. Although there are a few automated bus systems that actively show efficient operation without on-board stewards, this still appears to be a future development.

The impact of automated vehicles (AV) on pedestrians’ crossing behavior has been the topic of some recent studies, but findings are still scarce and inconclusive. The aim of this study is to determine whether the drivers’ presence and apparent attentiveness in a vehicle influences pedestrians’ crossing behavior, perceived behavioral control, and perceived risk, in a controlled environment, using a Head-mounted Display in an immersive Virtual Reality study. Twenty participants took part in a road-crossing experiment. The VR environment consisted of a single lane one-way road with car traffic approaching from the right-hand side of the participant which travelled at 30 kmph. Participants were asked to cross the road if they felt safe to do so. The effect of three driver conditions on pedestrians’ crossing behavior were studied: Attentive driver, distracted driver, and no driver present. Two vehicles were employed with a fixed time gap (3.5 s and 5.5 s) between them to study the effects of time gaps on pedestrians’ crossing behavior. The manipulated vehicle yielded to the pedestrians in half of the trials, stopping completely before reaching the pedestrian's position. The crossing decision, time to initiate the crossing, crossing duration, and safety margin were measured. The main findings show that the vehicle's motion cues (i.e. the gap between the vehicles, and the yielding behavior of the vehicle) were the most important factors affecting pedestrians’ crossing behavior. Therefore, future research should focus more on investigating how AVs should behave while interacting with pedestrians. Distracted driver condition leads to shorter crossing initiation time but the effect was small. No driver condition leads to smaller safety margin. Findings also showed that perceived behavioral control was higher and perceived risk was significantly lower when the driver appeared attentive. Given that drivers will be allowed to do other tasks while AVs are operating in the future, whether explicit communication will be needed in this situation should be further investigated.

Automated driving systems promise a tremendous amount of benefits. Especially when applied in the domain of public transport, economic and passenger advantages are thought to be manifold. As technology rapidly advances, and projects involving automated buses appear throughout the world, investigating how its users and surrounding road traffic interact with these novel technologies need to advance with a similar pace. However, up to now, a reliable and up-to-date overview of performed, running, and planned projects is lacking. Moreover, little is known about human interaction with automated bus systems, and what is known is not always reported. By means of a systematic review, an overview of the current state-of-the-art knowledge on the interaction between automated bus systems and its interactors is presented. Results of these studies are described and discussed, and implications are being made regarding future policies to be applied in this domain to safeguard safe interaction with automated bus systems.

Objective: In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians’ crossing intentions. Background: Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians’ interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI. Methods: Twenty-eight participants experienced an urban road environment from a pedestrian’s perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and “feel-safe” percentages were calculated. Results: For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs. Conclusion: An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous. Application: This research supports the development of automated vehicles that communicate with other road users.

Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. However, other road users, such as pedestrians and cyclists will still be present and would need to interact with these automated vehicles. Therefore, external communication interfaces could be added to the vehicle to communicate with pedestrians and other non-automated road users. The first aim of this study is to investigate how the physical appearance of the AV and a mounted external human-machine interface (eHMI) affect pedestrians’ crossing intention. The second aim is to assess the perceived realism of Virtual reality based on 360° videos for pedestrian crossing behavior for research purposes. The speed, time gap, and an eHMIs were included in the study as independent factors. Fifty-five individuals participated in our experiment. Their crossing intentions were recorded, as well as their trust in automation and perceived behavioral control. A mixed binomial logistic regression model was applied on the data for analysis. The results show that the presence of a zebra crossing and larger gap size between the pedestrian and the vehicle increase the pedestrian's intention to cross. In contrast to our expectations, participants intended to cross less often when the speed of the vehicle was lower. Despite that the vehicle type affected the perceived risk of the participants, no significant difference was found in crossing intention. Participants who recognized the vehicle as an AV had, overall, lower intentions to cross. A strong positive relationship was found between crossing intentions and perceived behavioral control. A difference in trust was found between participants who recognized the vehicle as automated, but this did not lead to a difference in crossing intentions. We assessed the research methodology using the presence questionnaire, the simulation sickness survey, and by comparing the results with previous literature. The method scored highly on the presence questionnaire and only 2 out of 55 participants stopped prematurely. Thus, the research methodology is useful for crossing behavior experiments.

Automated vehicles could have many impacts on society [1]. Taking the control of vehicles from human drivers, who by their nature make mistakes, and giving it to automated vehicles (AVs), which are believed to be accurate and reliable, could, in theory, increase safety. However, how non-automated road users will react and interact with driverless AVs is unknown. In particular, cyclists and pedestrians, the vulnerable road users (VRUs), will not be able to rely on eye contact. In addition, they are vulnerable because of a lack of a metal shield to protect them, their low mass, and their many degrees of freedom in movement makes them hard to predict. At this moment, it is unclear how the interactions between AVs and VRUs will be.

Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. Most fatal accidents of vulnerable road users (VRUs), pedestrians, cyclists and mopeds, involve a motorized vehicle. In addition, most of the accidents involving VRUs and motorized vehicles happen at road crossings. By replacing human-driven vehicles with automated vehicles, the human role will be altered and reduced which could lead to an increase in traffic safety. However, drivers are not the only ones who will have to adapt to automated vehicles, other road users, such as pedestrians and cyclists, will have to interact with vehicles with various levels of automation, too. Pedestrians and cyclists will still be humans and might behave in an unpredictable manner which could lead to unsafe behaviors. The main goal of this paper is to propose a theoretical framework which describes the interactions between automated vehicles and road user behavior under different road design conditions. This is a prerequisite to understand how to design safe urban environments where VRUs and automated vehicles can interact safely. A synthesis of the existing literature about the interactions between automated vehicles and VRUs, and the main factors that could influence VRUs’ behavior is presented. The results of the synthesis and the identified knowledge gaps are discussed. Based on this, a theoretical framework for the interactions between VRUs and automated vehicles is developed and discussed.