Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such ...as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (
N
= 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.
Automated trucks for long-distance journeys seem within reach. With such automation, no human driver could be available. However, the last mile of the delivery is likely to involve humans. Therefore, ...either a human driver should still be present, or construction site workers must interact with the automated truck. While automated trucks capable of dealing with various construction sites could be feasible, the development could be costly and time-consuming. To define cooperative solutions for automated deliveries incorporating interaction between automated trucks and humans, a workshop with truck drivers (
N
= 7) was conducted. Based on this workshop, a model of the delivery process, including communication needs, is proposed. Requirements addressing the issues for highly automated delivery are derived from this process.
•Implementation of a simulation including visualizations.•Online study with N = 59 participants.•Results indicate that mode distinction and the conspicuous sensor attached to the automated vehicle ...showed positive effects regarding mode confusion.•A tintable windshield was negatively assessed.
Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle’s communication. To evaluate potential solutions to this problem, we conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tintable windshield. Our results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tintable windshield, however, was negatively assessed. Thus, our work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion.
User acceptance is essential for successfully introducing automated vehicles (AVs). Understanding the technology is necessary to overcome skepticism and achieve acceptance. This could be achieved by ...visualizing (uncertainties of) AV's internal processes, including situation perception, prediction, and trajectory planning. At the same time, relevant scenarios for communicating the functionalities are unclear. Therefore, we developed EduLicitto concurrently elicit relevant scenarios and evaluate the effects of visualizing AV's internal processes. A website capable of showing annotated videos enabled this methodology. With it, we replicated the results of a previous online study (N=76) using pre-recorded real-world videos. Additionally, in a second online study (N=22), participants uploaded scenarios they deemed challenging for AVs using our website. Most scenarios included large intersections and/or multiple vulnerable road users. Our work helps assess scenarios perceived as challenging for AVs by the public and, simultaneously, can help educate the public about visualizations of the functionalities of current AVs.
•EduLicit–a method to elicit user-chosen driving scenarios and educate on automated vehicle functionalities and challenges.•Applying neural networks to scenario videos to visualize vehicles' detection, prediction, and trajectory planning functionalities.•Website implementation of EduLicit, where users upload in-the-wild driving videos for automated visualization and education.•User-chosen driving scenarios primarily include large intersections and/or multiple vulnerable road users.•Users perceive vulnerable road users as more unreliable, unpredictable, and harder to detect by sensors than vehicles.
Minor violations of traffic regulations are common today and partially socially accepted. Automated vehicles (AVs), however, will be obliged to keep to the letter of the law. This can lead to ...situations where user requests cause the AV to reach its legal boundaries, creating novel user-vehicle conflicts. To investigate whether traffic-violating driver interests are transferred to the automated context, we conducted an online survey with three conflict-prone scenarios (N=49). The results indicate that legally compliant AV behavior is desired but that users would intervene in the vehicle’s behavior to enforce interests. In a subsequent Virtual Reality study (N=30), we evaluated the effect of legal boundary-handling strategies (Responsibility and Control Shift, Responsibility Shift, No Shift) and other traffic participants’ violating traffic regulations on behavior, conflict, and trust in a legally conflict-prone parking scenario. Results show that conflict is perceived significantly higher in all strategies compared to the manual baseline, while situational trust in the vehicle is higher in the automated conditions but independent of the handling strategy.
•Online study (N = 49) unveils legal conflict scenarios between drivers and AVs.•VR study (N = 30) evaluates AV legal boundary-handling strategies.•Higher conflict noted in all AV strategies compared to manual driving.•Users may override AVs at legal boundaries to assert interests.•Handling strategy does not affect perceived conflict or trust.
•Development of functional prototype enabling external communication of (simulated) autonomous vehicles via a mounted display.•Online study with N = 59 participants.•Results indicate high reliance on ...factors independent of the external communication when users of such vehicles perform potentially confusing actions despite clear introduction.•Implications on System, User, and Societal level are discussed.
Automated vehicles are expected to require some form of communication (e.g., via LED strip or display) with vulnerable road users such as pedestrians. However, the passenger inside the automated vehicle could perform gestures or motions which could potentially be interpreted by the pedestrian as contradictory to the outside communication of the car. To explore this conflict, we conducted an online experiment (N = 59) with different message types (no message, intention, command), gestures (no gesture, wave, stop), and user positions (driver, co-driver) and measured the pedestrian’s confidence in crossing. Our results show that certain combinations (e.g., car indicates cross while the user in the driver seat gestures stop) confused the pedestrian, resulting in significantly lower confidence to cross. We further show that designing intention-based external communication led to less confusion and a significantly higher intention to cross.
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