Abstract It is currently unknown how automated vehicle platoons will be perceived by other road users in their vicinity. This study explores how drivers of manually operated passenger cars interact with automated passenger car platoons while merging onto a highway, and how different inter-vehicular gaps between the platooning vehicles affect their experience and safety. The study was conducted in a driving simulator and involved 16 drivers of manually operated cars. Our results show that the drivers found the interactions mentally demanding, unsafe, and uncomfortable. They commonly expected that the platoon would adapt its behavior to accommodate a smooth merge. They also expressed a need for additional information about the platoon to easier anticipate its behavior and avoid cutting-in. This was, however, affected by the gap size; larger gaps (30 and 42.5 m) yielded better experience, more frequent cut-ins, and less crashes than the shorter gaps (15 and 22.5 m). A conclusion is that a short gap as well as external human–machine interfaces (eHMI) might be used to communicate the platoon’s intent to “stay together”, which in turn might prevent drivers from cutting-in. On the contrary, if the goal is to facilitate frequent, safe, and pleasant cut-ins, gaps larger than 22.5 m may be suitable. To thoroughly inform such design trade-offs, we urge for more research on this topic.

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver (n=8n=8) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

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.

Abstract 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.

In this paper, we report user preferences regarding color and animation patterns to support the interaction between Automated Vehicles (AVs) and pedestrians through an external Human-Machine-Interface (eHMI). Existing concepts of eHMI differ -- among other things -- in their use of colors or animations to express an AV's yielding intention. In the absence of empirical research, there is a knowledge gap regarding which color and animation leads to highest usability and preferences in traffic negotiation situations. We conducted an online survey (N=400) to investigate the comprehensibility of a light band eHMI with a combination of 5 color and 3 animation patterns for a yielding AV. Results show that cyan is considered a neutral color for communicating a yielding intention. Additionally, a uniformly flashing or pulsing animation is preferred compared to any pattern that animates sideways. These insights can contribute in the future design and standardization of eHMIs.

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities.

Radar sensors have been used for active safety in cars for many years. An ongoing research project explores how radar sensors and technology common in automotive vehicles can be transferred for use on bicycles. Workshops have been used to generate ideas. A bicycle simulator is planned to be used for test and evaluation. Tests on a test track has been used to simulate high-risk scenarios. This paper describes the design process of this project, with focus on the user interface. High-risk scenarios and requirements are identified, followed by identified design challenges and design activities, including evaluation. Ideas for a dual HMI approach, directed towards the bicyclist and towards surrounding traffic are presented.

Interactions between autonomous vehicles (AV) and pedestrians remain an ongoing area of research within the AutoUI community and beyond. Given the challenge of conducting studies to understand and prototype these interactions, we propose a combined full-day workshop and tutorial on how to conduct field experiments and controlled experiments using Wizard-of-Oz (WoZ) protocols. We will discuss strengths and weaknesses of these approaches based on practical experiences and describe challenges we have faced. After diving into the intricacies of different experiment designs, we will encourage participants to engage in hands-on exercises that will explore new ways to answer future research questions.

Methods and metrics for studying interactions between automated vehicles and other road users in their vicinity, such as pedestrians, cyclists and non-automated vehicles, are not established yet. This workshop focuses on identifying the strengths and weaknesses of various methodologies that could potentially be used to study such interactions. The objective lies in determining the proper experimental design, sensitivity of metrics for measuring user behavior, ecological validity, generalizability of findings, extraction of insights regarding how findings can be translated into actionable requirements, and the alternatives for conducting longitudinal field studies. It will be of an interactive nature and involve hands-on activities. The workshop will consolidate existing knowledge, identify recurring issues, and explore the path towards resolving these issues. The outcome will be compiled into a paper to share this valuable knowledge with a broader research community.

While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of “who will go first,” are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers’ behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles’ mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians’ emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians’ perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs – something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.

This paper presents a large-scale simulator study on driver adherence to recommendationsgiven by driver support systems, specifically eco-driving support and navigation support.123 participants too ...