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Numerous driving simulator systems are available and are continu- ing to be developed. However, we believe many simulator offerings are built around what is technically possible rather than what is useful to the researchers that might use such systems. This points to a critical need to understand what makes a driving simulator prac- tical and effective for automotive interface design researchers. To remedy this shortcoming, we conducted video interviews with 15 industry and academic researchers engaged in automotive interface design research. We transcribed and performed thematic analy- sis on the data collected to better understand the different ways that researchers are using driving simulators, and what challenges they still face. We identified needs across three broad dimensions including: (1) Participant Experience, (2) Research Needs, and (3) Operationalization Requirements. By categorizing these needs, we aim to inform the development of future simulation tools that are more accessible to researchers from diverse backgrounds. 

Social scientists have argued that autonomous vehicles (AVs) need to act as effective social agents; they have to respond implicitly to other drivers’ behaviors as human drivers would. In this paper, we investigate how contingent driving behavior in AVs influences human drivers’ experiences. We compared three algorithmic driving models: one trained on human driving data that responds to interactions (a familiar contingent behavior) and two artificial models that intend to either always-yield or never-yield regardless of how the interaction unfolds (non-contingent behaviors). Results show a statistically significant relationship between familiar contingent behavior and positive driver experiences, reducing stress while promoting the decisive interactions that mitigate driver hesitance. The direct relationship between familiar contingency and positive experience indicates that AVs should incorporate socially familiar driving patterns through contextually-adaptive algorithms to improve the chances of successful deployment and acceptance in mixed human-AV traffic environments. 

The transition to mixed-tra!c environments that involve auto- mated vehicles, manually operated vehicles, and vulnerable road users presents new challenges for human-centered automotive re- search. Despite this, most studies in the domain focus on single- agent interactions. This paper reports on a participatory workshop (N = 15) and a questionnaire (N = 19) conducted during the Automo- tiveUI ’24 conference to explore the state of multi-agent automotive research. The participants discussed methodological challenges and opportunities in real-world settings, simulations, and computational modeling. Key "ndings reveal that while the value of multi-agent approaches is widely recognized, practical and technical barriers hinder their implementation. The study highlights the need for in- terdisciplinary methods, better tools, and simulation environments that support scalable, realistic, and ethically informed multi-agent research. 

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. 

Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as tra#c participants. Human tra#c partici- pants 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, e$ec- tive interaction in mixed tra#c 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 tra#c situations or foster tra#c %ow. 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 tra#c scenarios. Through an online survey on N = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Tra#c Scale and assessed it psychometrically. 

Automated vehicles are expected to communicate with vulnerable road users. In two longitudinal studies, we investigated the impact of external Human-Machine Interfaces (eHMI) on pedestrian safety and behavior when interacting with automated vehicles. Utilizing LED strips for communication, these studies probed various factors, including mixed traffic scenarios, presence of eHMIs, and being from Germany or the USA. Our experimental approaches included a Virtual Reality study with 24 participants in Germany and an online study with 28 participants from the USA and Germany. Results revealed that repeated interactions with automated vehicles featuring eHMI significantly enhance pedestrian Trust, Understanding, and perceived safety, while simultaneously diminishing mental workload. Notably, the positive effects of eHMI were consistent across the two countries. US participants exhibited a tendency for higher risk-taking in crossing situations and reported lower mental workloads, underscoring the importance of considering cultural nuances in designing eHMI systems for mixed-traffic environments. 

In this workshop, we aim to explore how the design of technology can encourage sustainable mobility practices and facilitate interactions that promote environmentally friendly, prosocial transportation choices. We intend to identify real-world scenarios where these interactions can be implemented, discuss the challenges and opportunities they present, and develop actionable strategies for their application. We will use speculative design methods such as design fiction and anticipatory ethnography to envision alternative future mobility practices. This holistic approach aims to create a comprehensive understanding of how technology can shape sustainable and inclusive mobility ecosystems, and critique the current practices. By bringing together researchers, practitioners, and stakeholders from various disciplines, we hope to foster a collaborative network that will drive future advancements in sustainable mobility. Our goal is to address the urgent need to reduce ecological footprints and improve social experiences through innovative technological solutions. 

In this review, we analyze the current state of the art of compu- tational models for in-vehicle User Interface (UI) design. Driver distraction, often caused by drivers performing Non Driving Re- lated Tasks (NDRTs), is a major contributor to vehicle crashes. Accordingly, in-vehicle UIs must be evaluated for their distraction potential. Computational models are a promising solution to au- tomate this evaluation, but are not yet widely used, limiting their real-world impact. We systematically review the existing literature on computational models for NDRTs to analyze why current ap- proaches have not yet found their way into practice. We found that while many models are intended for UI evaluation, they focus on small and isolated phenomena that are disconnected from the needs of automotive UI designers. In addition, very few approaches make predictions detailed enough to inform current design pro- cesses. Our analysis of the state of the art, the identified research gaps, and the formulated research potentials can guide researchers and practitioners toward computational models that improve the automotive UI design process. 

In the future, roads will host a complex mix of automated and manually operated vehicles, along with vulnerable road users. However, most automotive user interfaces and human factors research focus on single-agent studies, where one human interacts with one vehicle. Only a few studies incorporate multi-agent setups. This workshop aims to (1) examine the current state of multi-agent research in the automotive domain, (2) serve as a platform for discussion toward more realistic multi-agent setups, and (3) discuss methods and practices to conduct such multi-agent research. The goal is to synthesize the insights from the AutoUI community, creating the foundation for advancing multi-agent traffic interaction research.