As high-level, automated vehicles (AVs) become more present on our roads, resolving ethical, legal, and social implication (ELSI) conflicts in decision-making is a complex challenge. To possibly find solutions to such challenges, this workshop explores how Explainable AI(XAI), and Human-Machine Interfaces (HMIs) can improve transparency and increase trust, particularly in ambiguous situations. We propose a scenario-based workshop that invites participants to reflect on decision-making, expectations for explanations, and possible communication through HMI. Outcomes of this workshop will be the first step to meaningfully add XAI to ensure human-centered decisions of AVs.

Research into automated vehicles and Advanced Driver Assistance Systems (ADAS) is developing rapidly. On-road evaluations of state-of-the-art systems are still rare, as novel concepts are mainly investigated in simulations or as theoretical scenarios. Therefore, this study conducts empirical field tests using two representative models (BMW i5 and a Mercedes-Benz EQS) on a standardized test track by 10 HCI and automotive experts under real-life conditions. Followed by an expert workshop that combined AI-supported semantic analysis with human-led thematic analysis to summarize strengths, limitations, and recommendations for action. Our findings expose two key misalignments: one between actual automation level and user-perceived support, and another between user expectations and real-world system behavior. Our insights aim to support the AutoUI community in shaping future ADAS.

The rapid development of automated vehicles offers promising avenues to enhance user experience and safety by integrating empathic in-vehicle interfaces. The report from the workshop Emotion GaRage series, held during the AutomotiveUI ’23 conference, investigates the potential of employing generative artificial intelligence (AI) to develop these empathic interfaces. Our workshop emphasized the importance of emotion recognition and regulation for passengers in driving contexts and the role of future in-vehicle empathic interfaces. Through collaborative design sessions, participants developed personalized in-vehicle agents to address drivers’ emotional states to improve safety and enjoyment. We explored insights by investigating the potential and possibilities of using generative AI in the design process. This report discusses the significance of empathic displays in facilitating the adoption of automated vehicles and explores the potential and limitations of using generative AI in the design phases.

Increased evidence suggests that cattle are the primary host of Influenza D virus (IDV) and may contribute to respiratory disease in this species. The aim of this study was to detect and characterise IDV in the Swedish cattle population using archived respiratory samples. This retrospective study comprised a collection of a total 1763 samples collected between 1 January 2021 and 30 June 2024. The samples were screened for IDV and other respiratory pathogens using real-time reverse transcription quantitative PCR (rRT-qPCR). Fifty-one IDV-positive samples were identified, with a mean cycle threshold (Ct) value of 27 (range: 15–37). Individual samples with a Ct value of <30 for IDV RNA were further analysed by deep sequencing. Phylogenetic analysis was performed by the maximum likelihood estimation method on the whole IDV genome sequence from 16 samples. The IDV strains collected in 2021 (n = 7) belonged to the D/OK clade, whereas samples from 2023 (n = 4) and 2024 (n = 5) consisted of reassortants between the D/OK and D/660 clades, for the PB2 gene. This study reports the first detection of IDV in Swedish cattle and the circulation of D/OK and reassortant D/OK-D/660 in this population.

This work-in-progress aims to support more realistic and nuanced representations of pedestrian behavior in automated driving research. We present a motion capture dataset comprising N = 11 participants, who were recorded crossing a street under varying weather conditions while carrying different objects. The dataset includes 220 motion sequences with detailed gait data, analyzed with a focus on walking speed, cadence, and step length. Results indicate that pedestrian gait is significantly affected when using a smartphone and when exposed to rain without an umbrella. Future work will expand the scenario diversity and develop a toolchain based on the Open Simulation Interface (OSI) to integrate that data into simulation environments, and provide an open-source dataset, enabling more ecologically valid studies of human-vehicle interaction.

In this workshop, we invite researchers, designers, and practitioners to explore together how life-cycle thinking can contribute to the design of intelligent, sustainable mobility solutions. While current research primarily focuses on making the usage period of such solutions more sustainable, we aim to take a broader perspective by integrating sustainability from the earliest design stages through to the end of the vehicle’s life. Therefore, we will use speculative and critical design thinking to inspire and explore challenges as well as opportunities concerning sustainability at every stage of the life-cycle, from Design and Production to Usage, and End-Of-Life. Next, we will lead an ideation and prototyping session, followed by an interdisciplinary discussion reflecting on how intelligent technology can promote sustainable mobility. The outcomes will include potential design ideas and future research directions for incorporating life-cycle considerations into future mobility solutions.

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation-maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

The rapid advancement of automated vehicles has aroused the curiosity of researchers in the automotive field. Understanding the emotional aspects of this technology is critical to improving human-vehicle interactions. The topics of the proposed workshop will be expanded from internal to external empathetic interface designs of automated vehicles. The workshop will gather researchers and practitioners to brainstorm and design affective internal and external interfaces for automated vehicles, targeting specific use cases within the social context. During the workshop, participants will use an affective design tool and generative AI to prototype affective interface designs in automated vehicles. With this creative approach, we aim to expand the knowledge of affective eHMIs in addition to in-vehicle designs and understand social factors that contribute to the user perceptions of automated vehicles.

Safety Assurance remains a challenge for the large-scale deployment of Automated Driving Systems (ADS). Safety models monitor the performance of the ADS. Most safety models are validated both in simulation and during on-road tests. However, first-hand experiences and analysis of ADS safety models are not easily accessible to the general research community. This paper introduces the RSS driving demonstrator an open-source simulation tool that enables first-hand experience of the Responsibility Sensitive Safety (RSS) safety model proposed by Intel and Mobileye and adopted by several Automotive Industry standards and regulatory frameworks. The RSS demonstrator enables first-hand interactions and experience of ADS safety model restrictions in both automated and manual driving conditions. As a User Experience (UX) tool, it provides quantitative safety metrics and flexible user interaction features. The results indicate it served to both evangelize the RSS ADS safety model with laymen population and is a versatile tool for Automotive UX development.

In the field of artificial intelligence (AI), sensor fusion [1] is becoming an increasingly important technology for many Internet of things (IoT) applications. For intelligent transportation system (ITS), we propose a wireless-assisted automatic online spatial calibration for sensor fusion. Due to the mobility of traffic objects such as vehicles, it is critical to make sure ground truth (GT) position and corresponding sample positions are measured based on raw data collected by different sensors at the same time. We use 5G time sensitive networking (TSN) to achieve this time alignment. Additionally, we illustrate how to perform automatic miscalibration detection and correction based on correctly grouped GT position and sample positions. There are many advantages of using our solution in practical sensor fusion systems that can significantly improve traffic safety and efficiency.

Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver's affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.

Areas of limited visibility are common in day-to-day traffic: be it static buildings, parked vehicles, traffic participants blocking the vehicle's line of sight, harsh weather conditions or just narrow curves that impede the automated driving sensor suite to inspect the road ahead. Autonomous vehicles have to be able to safely cope with this kind of constraints. The Responsibility-Sensitive Safety model (RSS) demands vehicles to exercise caution with respect to occlusions and to consider also occluded road agents. This paper provides a concrete implementation of how occlusions in RSS can be addressed and investigates the balance between safety and usefulness of the model when a reasonably foreseeable behavior of occluded road agents is assumed. We perform occlusion experiments in urban as well as on highway scenarios with the driving simulation platform CARLA applying different parameterization of the agents kinematic properties and the safety model parameters to analyse and judge the consequences with respect to safe driving and overcautious driving behaviors.

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

Empathic in-vehicle interfaces can address driver affect and mitigate decreases in driving performance and behavior that are associated with emotional states. Empathic vehicles can detect and employ a variety of intervention modalities to change user affect and improve user experience. Challenges remain in the implementation of such strategies, as a broader established view of practical intervention modalities and strategies is still absent. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of displays and alternatives suitable to various use case situations in current and future vehicle states. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this workshop, researchers will create a design flowchart for in-vehicle affective display designers when creating displays for an empathic vehicle.

There is little doubt that driving generates emotional responses, whether that’s the joy of freedom, the boredomBoredom of stop-and-go traffic or angerAnger towards unsafe maneuvers. In this chapter we provide an overview of emotionEmotions research applied to the automotive context and highlight the impact of emotional states in varying levels of driving automation. We review the most critical research findings on the impact of emotional states in driving performanceDriving performance including reaction time and take-overTake-over readiness. We also discuss the application of emotion regulationEmotion regulation strategies related to the driving task. Finally, we analyze the research challenges still present for robust emotional classification and personalization in their application to in-vehicle interactions. This technology offers great potential for the development of emotionally-aware in-cabin driver assistants which will play a critical role in the future of automated driving user experience development.

Ensuring the safety of autonomous vehicle (AV) relies on accurate prediction of error occurrences in its perception system. Due to the inter-stage functional dependence, the error occurred at a certain stage may be propagated to the following stage and generate extra errors. To quantify the error propagation, this paper adopts the physics-based simulation, which enables fault injection at different stages of an AV perception system to generate error event data for error propagation modeling. Amulti-stage Hawkes process (MSHP) is proposed to predict the error occurrences in each stage, with error propagation represented as a latent triggering mechanism. With explicitly considering the error propagation mechanism, the proposed outperforms benchmark methods in predicting error occurrence in a physics-based simulation of a multistage AV perception system. The proposed two-step likelihood-based algorithm accurately estimates the model coefficients in a numerical simulation case study.

Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.

The need for safety in automated driving is undisputed. Since automated vehicles are expected to reduce the number of fatalities in road traffic significantly, hundreds of millions of test kilometers would be required for statistical safety validation [1]. Physics-based safety verification approaches are promising in order to reduce this validation effort. Towards this goal, Mobileye introduced the concept of Responsibility-Sensitive Safety (RSS). In RSS, bounds for the reasonable worst-case behavior of traffic participants are assumed to be given, such as the reaction time or the maximum deceleration. These parameters have a crucial effect on the applicability of the approach: choosing conservative parameters likely hinders traffic flow, while the opposite could lead to collisions, as the assumptions are violated. Thus, in this work, we focus on finding reasonable parameters of RSS. Based on the physical limits, legal requirements and human driving behavior, we propose scopes and parameter sets that allow for a sound safety verification while not hindering traffic flow. Furthermore, we present an approach that explains seemingly frequent human drivers' RSS violations on highways and may lead to a useful extension of RSS.

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAV s.