Realistically modeling interactions between road users — like those between drivers or between drivers and pedestrians — within experimental settings come with pragmatic challenges. Due to practical constraints, research typically focuses on a limited subset of potential scenarios, raising questions about the scalability and generalizability of findings about interactions to untested scenarios. Here, we aim to tackle this by laying the methodological groundwork for defining representative scenarios for dyadic (two-actor) interactions that can be analyzed individually. This paper introduces a conceptual guide for operationalizing controlled dyadic traffic interaction studies, developed through extensive interdisciplinary brainstorming to bridge theoretical models and practical experimental design. It elucidates critical trade-offs in scenario selection, interaction approaches, measurement strategies, and timing coordination, thereby enhancing reproducibility and clarity for future traffic interaction research and streamlining the design process. The methodologies and insights we provide aim to enhance the accessibility and quality of traffic interaction research, offering a guide that aids researchers in setting up studies and ensures clarity and reproducibility in reporting, bridging the gap between theoretical traffic interaction models and practical applications in controlled experiments, thereby contributing to advancements in human factors research on traffic management and safety.

This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases.

This workshop is designed to explore the potential synergies between established research on Social and Assistive Robots within HRI and the emerging field of Autonomous Vehicle and Other Road Users (AV-ORU) interactions. It examines the bidirectional applicability of principles, methodologies, and insights and seeks to initiate a detailed discussion on the distinctions and parallels between HRI and AV-ORU interactions to promote more meaningful, context-aware exchange and prevent the oversimplification of transferability. The goal is to encourage the integration of methodologies, broaden the ethical considerations in play, improve the focus on user-centric research in interactions and ultimately address shared challenges, innovative solutions towards sophisticated, ethically sound, and socially integrated autonomous systems.

Driving feedback is an important factor that can affect the perceptions of remote drivers of the surrounding environment during teleoperation. This paper focuses on investigating the influence of motion-cueing, sound and vibration feedback on driving behaviour and experience. A prototype teleoperation station is developed with feedback from audio, vibration actuators, and motion cues. Using this prototype, the experiment is carried out in two scenarios: a low-speed disturbance scenario with 30 participants and a dynamic driving scenario with 22 participants. Objective and subjective assessment methods are used to evaluate driving behaviour and experience separately. The results indicate that the combination of motion-cueing, sound and vibration feedback provides the most favourable driving experience for the participants. Specifically, sound and vibration feedback enhance drivers’ sense of speed, while motion-cueing feedback helps in road surface sensing, leading to increased throttle reversal rate in the low-speed disturbance scenario. However, it is noteworthy that motion-cueing feedback does not significantly improve driving performance in the dynamic driving scenario of this study.

External Human-Machine Interfaces (eHMIs) have been evaluated to facilitate interactions between Automated Vehicles (AVs) and pedestrians. Most eHMIs are, however, visual/ light-based solutions, and multi-modal eHMIs have received little attention to date. We ran an experimental video study (<Formula format="inline"><TexMath><?TeX $N~=~29$?></TexMath><AltText>Math 1</AltText><File name="chi24-142-inline1" type="svg"/></Formula>) to systematically understand the effect on pedestrian’s willingness to cross the road and user preferences of a light-based eHMI (light bar on the bumper) and two sound-based eHMIs (bell sound and droning sound), and combinations thereof. We found no objective change in pedestrians’ willingness to cross the road based on the nature of eHMI, although people expressed different subjective preferences for the different ways an eHMI may communicate, and sometimes even strong dislike for multi-modal eHMIs. This shows that the modality of the evaluated eHMI concepts had relatively little impact on their effectiveness. Consequently, this lays an important groundwork for accessibility considerations of future eHMIs, and points towards the insight that provisions can be made for taking user preferences into account without compromising effectiveness.

Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.

Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.

Emergency response system is a complex system of systems (SoS). The introduction of connected and autonomous vehicles (CAVs) introduces an extra dimension into the complexity. Future emergency response must be able to take into account of the autonomous vehicles with different automation levels and leverage the increasing connectivity and automation for efficient emergency response. Architecture frameworks have long been used for system engineering for large complex systems. The emerging unified architecture framework converges previous architecture frameworks for a unified one towards both military and civilian use. Based on the scenario of emergency response with CAVs on highways, this paper motivates an enterprise architecture for emergency response system of systems (ERSoS) with identification of the key challenges and opportunities in addition to a proposal of required capabilities. The work is a first iteration of an enterprise architecture for ERSoS with CAVs and forms part of the overall ERSoS architecture development process.

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.

External human-machine interfaces (eHMIs) support automated vehicles (AVs) in interacting with vulnerable road users such as pedestrians. eHMI research has mostly dealt with investigating the communication an AV’s yielding intent, but there is little insight into how (or if) an eHMI should communicate an AV’s non-yielding intent. We conducted a video-based study (N = 25) with two eHMI concepts that offer pedestrians information about the vehicle’s non-yielding intent either explicitly or implicitly, and compared it with a baseline of an AV without an eHMI. Results show that while both kinds of eHMIs are effective and perform better than the baseline, there is no evidence of significant difference in road-crossing decision performance between explicit and implicit eHMIs in ambiguous situations. However, subjective feedback shows a trend of preference for eHMIs that communicate an AV’s intent explicitly at all times, although with a need for a significant distinction between the yielding and non-yielding messages.

This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

“Prosocial Behavior‘‘ means cooperating and acting in a way to benefit others. Since more and more diverse road users (such as electronic bicycles, scooters, etc.) but also vehicles at different levels of automation are sharing the safety-critical road environment, acting prosocial will become increasingly important in the future for both human and automated traffic participants. A few papers so far have already begun to address this issue, but currently, there exist no systematic methodological approaches to research this area. In the proposed workshop, we plan to define more specifically what characterizes prosocial behavior in future traffic scenarios where automated and manual vehicles meet and interact with all kinds of vulnerable road users. We further want to identify important scenarios and discuss potential evaluation methods for researching prosocial behavior. Ultimately, these findings will be integrated into a research agenda actively pursued by cooperation initiated during this event.

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.