Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants. Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them. Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations. Prosocial behavior, such as yielding one’s right of way, will be needed to resolve unclear traffic situations or foster traffic flow. However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles but for all road users? Here, we describe a new scale to measure perceived social behavior in urban traffic scenarios. Through an online survey on N = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Traffic Scale and assessed it psychometrically.

Cyclists frequently face numerous hazards on the road. Often those hazards are posed by motorised vehicles. Advanced support systems that alert cyclists to potential dangers could enhance their safety. However, research in this area, particularly regarding hazard notifications for cyclists, remains sparse. This work assesses bi-modal early hazard notification concepts (combining visual cues with either auditory or tactile feedback) provided at head level (smart glasses with speakers, tactile headband). They are detailing the nature of the hazard, its direction relative to the cyclist, and the timing of exposure. This work investigates cyclists' preference and perception of the proposed concepts for two hazardous situations originating from interactions with vehicles: ‘dooring’, the hazard of a potential collision with an opening door of a parked vehicle (evaluated through a test track study, N = 32) and ‘being overtaken’ which poses the hazard of being cut off or hit by the overtaking vehicle (assessed in a bicycle simulator study, N = 21). The study involved comparisons of supported and unsupported rides, focusing on their impact on usability, intuitiveness, workload, and perceived safety. Our findings reveal varied preferences for the supporting feedback modality, with 56% favouring visual-auditory and 31% visual-tactile. The participants rated user experience, intuitiveness and perceived safety for the use of both concepts quite high. Further, the workload for assisted rides was rated as equally low as for unassisted rides.

The absence of physical accident risk in driving simulation, which allows for safely studying critical driving situations, also reduces the driver's risk perception, which may result in unrealistic driver behavior. Validation studies in this context are rare for ethical reasons, making it difficult to assess the extent of this issue at present. The present study addresses this gap by utilizing naturalistic driving data. Four critical cut-in situations on German highways were extracted from naturalistic data and replicated in a driving simulator study with N = 58 participants. Both in- situ self-ratings on subjective criticality and post-hoc video-based ratings (from the driver and objective observers), as well as presence ratings, were collected to supplement driver behavior. Although driver reactions in the simulator and the field were not equivalent in absolute terms, drivers in both the simulation and the real world exhibited accident-avoidance behavior through braking reactions, indicating relative validity. No clear mediating role of the sense of presence towards a more careful driver behavior was found. This work shows that drivers exhibit accident- avoiding behavior in the simulator and tend to react to hazards in the simulator similarly as they would in a real situation, while absolute numerical values should only be interpreted with caution.

Cycling plays an essential role in sustainable mobility, health, and socializing. This workshop aims to collect and discuss the lessons learned from Cycling Human-Computer Interaction (CyclingHCI). For this, we will gather researchers and experts in the field to discuss what we learned from designing, building, and evaluating CyclingHCI systems. We will start the workshop with three lessons learned from CyclingHCI defined by the organizers and their experience in the field, which include (1) a lack of theories, tools, and perspectives, (2) knowledge about designing for safety and inclusive cycling, and (3) evaluation methods and environments. Taken together, with this work, we aim to promote interactive technology to get more people cycling, profiting from the many associated benefits.

Being the premier forum for automotive user interface research and other vehicular technologies, AutomotiveUI concerns professionals, academics, researchers, and industry representatives from all around the world interested in innovation, research, and application of automotive user interface topics, embodying diversity at its core. This diversity is however not always reflected in the conference’s main program. In order expand the topic foci of the conference in the future, this workshop aims to identify the key factors that influence the main program creation, and create strategies that can help increase its diversity and accessibility, culturally and geographically. We aim to exchange ideas, experiences and start conversations that raise awareness about this topic, in order to inspire longer-term follow-up activities which will eventually result in increased diversity and accessibility not only at AutomotiveUI, but at international conferences in general.

Although automated driving is becoming a more widespread technology, there is still a lack of understanding about how to best communicate information to drivers in the specific situation of automated parking. This mixed-method user study aimed to address this gap by evaluating the preferences of users for information about the vehicle’s behavior in automated parking and the impact on user experience and situation awareness. An explainable concept displayed as augmented visualizations in the vehicle’s windshield was prototyped and evaluated in a driving simulation study and a qualitative interview. N = 25 participants provided insights into the development of more effective and user-centered interface designs for automated parking. As a result, the explainable concept was preferred by the participants and led to a higher user experience and explainability. This work contributes to the design and evaluation of future automated parking systems and provides a step towards more user-friendly automated driving experiences.

EDITORIAL article Front. Robot. AI, 16 June 2023Sec. Human-Robot Interaction Volume 10 - 2023 | https://doi.org/10.3389/frobt.2023.1228093

One way to improve road safety for cyclists is the development of hazard notification systems. Instead of in field experiments, such systems could be tested in safe and more controlled simulated environments; however, their validity needs verification. We evaluated the validity of mixed reality (MR) simulation for bicycle support systems notifying of dooring hazards. In a mixed-design study (N=43) with environment type(MR/test track) as within and hazard notifications (with/without) as between factor, comparing subjective and objective measures across environments. In conclusion, MR simulation is absolutely valid for user experience and perceived safety and relatively valid for workload, standard deviation of lateral position, and speed. However, MR simulation was not valid for lateral distance, as participants cycled more in the center of the street than on the test track, perhaps to avoid simulator sickness. Thus, we conclude that MR simulation is valuable for studying bicycle safety.

The Science Gallery in Ingolstadt’s city center serves as a platform for public engagement and discussion on important future topics concerning urban development. The exhibition aims to make science tangible and accessible to all, fostering dialogue and promoting social acceptance of automated and networked mobility solutions. The Science Gallery offers the possibility to engage via interactive elements, including a driving simulator and virtual reality experiences of an autonomous shuttle bus interior. As the exhibition will be closed by September 2023, we will provide a 360 ° video demonstration at the conference, allowing participants to experience the Science Gallery virtually. We reflect on and discuss about lessons learned when realizing a physical space for knowledge transfer and feedback encouragement from the local community.

The development of automated vehicles (AVs) brings new challenges to human-vehicle communication, primarily in urban environments. One way to facilitate communication is seen in the use of external human-machine interfaces (eHMIs). These indicate the current status as well as the intentions of AVs to other road users. This paper focuses on designing and evaluating eHMIs for communication between multiple AVs and pedestrians at multi-lane intersections. For this purpose, we analyzed the current situation with traffic signals and future scenarios with eHMIs. We designed an eHMI concept suitable for scenarios with many vehicles and different intersection situations. In an online survey (N=104), we used videos from four test scenarios for a comparative evaluation (baseline: traffic signals). The results show that the eHMI concept received higher ratings for user experience and emotional state in some cases, but also highlight the need for consistent behavior across different vehicles, which would be the default with traffic lights.

The fact that automated vehicles will be part of road traffic raises the question of how human road users, like bicyclists or pedestrians, would safely interact with them. Research has proposed external human-machine interfaces (eHMIs) for automated vehicles as a potential solution. Concept prototypes and evaluations so far have mainly focused on young, healthy adults and people without disabilities, such as visual impairments. For a “one-for-all” holistic, inclusive solution, however, further target groups like children, seniors, or people with (other) special needs will have to be considered. In this workshop, we bring together researchers, experts, and practitioners working on eHMIs to broaden our perspective on inclusiveness. We aim to identify aspects of inclusive eHMI design that can be universal and tailored to any culture and will focus on discussing methods, tools, and scenarios for inclusive communication.

Autonomous shared ride vehicles may be prone to similar social issues and non-ideal passenger behaviors as today’s public transit. Such issues may include passengers littering, harassing others, and creating an environment that is generally unpleasant for riders. Transportation user experience designers should preemptively consider such scenarios early in their design work to help develop possible interfaces to manage social order and maintain good rider experience. Through a short video prototype, we present three possible non-ideal scenarios that may occur on shared autonomous shuttles and provide three potential solutions to begin a discussion around how to design for such non-ideal situations.

Shared automated vehicles (SAV) are expected to benefit society and the environment as vehicles and rides are shared among passengers. However, this requires acceptance by different types of people. Recent research confirms that women and older people are particularly concerned about this mobility form due to security reasons. These concerns must be considered to assure the adoption of SAVs from women and senior citizens, too. Accordingly, we conducted a qualitative user study (N=21) using participatory design methods. Our work contributes insights into women's security needs by taking a holistic view of a ride with an SAV from booking to arrival from the perspective of women of different age groups. From our results, we derived general design implications and propose three concrete concepts for high levels of security. Lastly, we present a research agenda for further investigation on security concepts in SAVs.

(1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work. (2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance. (3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance. (4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance.

Abstract The inappropriate use of automation as a result of trust issues is a major barrier for a broad market penetration of automated vehicles. Studies so far have shown that providing information about the vehicle’s actions and intentions can be used to calibrate trust and promote user acceptance. However, how such feedback could be designed optimally is still an open question. This article presents the results of two user studies. In the first study, we investigated subjective trust and user experience of (N=21) participants driving in a fully automated vehicle, which interacts with other traffic participants in virtual reality. The analysis of questionnaires and semi-structured interviews shows that participants request feedback about the vehicle’s status and intentions and prefer visual feedback over other modalities. Consequently, we conducted a second study to derive concrete requirements for future feedback systems. We showed (N=56) participants various videos of an automated vehicle from the ego perspective and asked them to select elements in the environment they want feedback about so that they would feel safe, trust the vehicle, and understand its actions. The results confirm a correlation between subjective user trust and feedback needs and highlight essential requirements for automatic feedback generation. The results of both experiments provide a scientific basis for designing more adaptive and personalized in-vehicle interfaces for automated driving.

Automated vehicles are expected to become a part of the road traffic in the near future. This upcoming change raises concerns on how human road users, e.g., cyclists or pedestrians, would interact with them to ensure safe communication on the road. Previous work focused primarily on the scenario in which a young adult without impairments crosses a street in front of an automated vehicle. Several road user groups, such as children, seniors, or people with special needs, in roles of pedestrians and cyclists, are not considered in this scenario. On top of this, cultural differences are rarely considered. To ensure that future traffic is safe and accessible for all citizens, we aim to address inclusive communication between automated vehicles and vulnerable road users. In this workshop, we will discuss and exchange methods, tools, and scenarios applicable for inclusive communication, identify the most relevant research gaps, and connect people for future collaborations.

Testing and validation of automated driving functions represent major challenges for automobile manufacturers and other stakeholders. Simulation of automated driving functions in a virtual world has the potential to accelerate testing and improve the quality of the optimization process. Among the many challenges in the urban context, a critical problem cluster involves the safety impacts of automated driving functions on vulnerable road users (VRU). Virtual assessment of safety impacts requires validated models of VRU behavior, particularly behavior related to “failure modes” and reactions in critical situations. Among VRU, there is an especially urgent need for data and models to describe the dynamics and behavior of e-scooter riders. A recent study performed by our group under laboratory conditions has provided data with implications for e-scooter stability, in particular the impact of hand signals and rear blind spot checks. It turns out that even novice e-scooter riders can successfully learn to maintain stability while performing these tasks. To understand the details of maintaining stability, a more profound understanding of the dynamical modes of the e-scooter, including the control and guidance process performed by the rider, would be of great utility, particularly to address the problem of realistic e-scooter models for simulations. To this end, more comprehensive e-scooter testing environments with enhanced sensor technology should be developed.

Exploiting the potential of automated vehicles and future traffic concepts like platooning or dynamic intersections requires the integration of human traffic participants. Recent research investigating how automated vehicles can communicate with other road users has focused mainly on pedestrians. We argue that cyclists are another important group of vulnerable road users that must be considered, as cycling is a vital transportation modality for a more sustainable future. Within this paper, we discuss the needs of cyclists and claim that their integration will demand to think of other concepts, which support moving communication partners. We further sketch potential approaches for augmented reality applications based on related work and present results of a pilot study aiming to evaluate and improve those. Initial findings show that people are open towards concepts that increase cyclist safety. However, it is key to present information clearly and unambiguously to produce a benefit.

There is a growing body of research in the field of interaction between automated vehicles and other road users in their vicinity. To facilitate such interactions, researchers and designers have explored designs, and this line of work has yielded several concepts of external Human-Machine Interfaces (eHMI) for vehicles. Literature and media review reveals that the description of interfaces is often lacking in fidelity or details of their functionalities in specific situations, which makes it challenging to understand the originating concepts. There is also a lack of a universal understanding of the various dimensions of a communication interface, which has impeded a consistent and coherent addressal of the different aspects of the functionalities of such interface concepts. In this paper, we present a unified taxonomy that allows a systematic comparison of the eHMI across 18 dimensions, covering their physical characteristics and communication aspects from the perspective of human factors and human-machine interaction. We analyzed and coded 70 eHMI concepts according to this taxonomy to portray the state of the art and highlight the relative maturity of different contributions. The results point to a number of unexplored research areas that could inspire future work. Additionally, we believe that our proposed taxonomy can serve as a checklist for user interface designers and researchers when developing their interfaces.