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.

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.