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Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety dataand knowledge; (3) offsetting costs of curating safety-critical data and incentivizing data sharing. 

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. 

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. 

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. 

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 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. 

Driving simulators are vital for human-centered automotive research, offering safe, replicable environments for studying human interaction with transportation technology interfaces and behaviors. However, traditional driving simulators are not well-suited to studying traffic interactions with various degrees of freedom in a way that allows for the capture of nuances in implicit and explicit interactions, e.g. gestures, body language, and movement. We developed a multi-participant virtual reality (VR) driving simulation platform to study these interactions. This portable system supports cross-cultural experiments by modeling diverse scenarios, generating analyzable data, and capturing human behaviors in traffic. Our interactive demo allows participants to experience roles as drivers or pedestrians in a shared virtual environment, with the goal of providing a hands-on experience with this open-source VR simulator and demonstrating its affordability and scalability for traffic interaction studies to researchers and practitioners. 

Most of today’s studies investigating the driver-vehicle interaction of automated driving systems are conducted in simulated environments like driving simulators or virtual reality. While this simulation-based experimental research can produce valuable and valid results, it is at the same time limited by the inherent lack of realism. Important insights into real-world driving experiences and repeated system usage are rarely collected due to the constraints imposed by time and financial resources. In a two-step research approach, we aim to connect the AutoUI research with real-world users. In the first step, we conducted qualitative interviews with 10 experienced, tech-savvy users of current automated driving systems (Waymo, Cruise, Tesla) and clustered the results into the most important topics from a human factor perspective. On this basis, the workshop now aims to bring these insights into the AutoUI research community to identify the most relevant and urgent issues that should be addressed in the coming years. 

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.