To understand driver’s gaze behavior, the gaze is usually matched to surrounding objects or static areas of interest (AOI) at fixed positions around the car. Full surround object tracking allows for an understanding of the traffic situation. However, because it requires an extensive sensor set and a lot of processing power, it’s not yet broadly available in production cars. The use of static AOIs only requires the addition of eye tracking sensors. They are at fixed positions around the car and can’t adapt to the environment, therefore their usefulness is limited. We propose geopositioned 3D AOIs. With adaptability and the use of a small sensor set, they combine the strengths of both methods. To test 3D AOIs’ capabilities for gaze analysis, a driving simulator study with 74 participants was conducted. We show that 3D AOIs are suitable for driver’s gaze analysis and a promising tool for driver intention prediction.

Center touchscreens are the main Human-Machine Interface (HMI) between the driver and the vehicle. They are becoming, larger, increasingly complex and replace functions that could previously be controlled using haptic interfaces. To ensure that touchscreen HMIs can be operated safely, they are subject to strict regulations and elaborate test protocols. Those methods and user trials require fully functional prototypes and are expensive and time-consuming. Therefore it is desirable to estimate the workload of specific interfaces or interaction sequences as early as possible in the development process. To address this problem, we envision a model-based approach that, based on the combination of user interactions and UI elements, can predict the secondary task load of the driver when interacting with the center screen. In this work, we present our current status, preliminary results, and our vision for a model-based system build upon large-scale natural driving data.

While different studies have investigated the different types of signals to communicate that an automated vehicle will or will not yield, the question of “when” signaling intent is contributing best to cooperation is still open. We conducted a study investigating the effects of eHMIs’ signaling yielding intent to pedestrians at different times relative to the vehicles’ deceleration maneuver. We realized this scenario by means of a video game in which pedestrians need to safely cross the street. Our results show that signaling yielding intent with eHMIs before or simultaneously with a deceleration maneuver significantly shortens participant’s street crossing onsets while later presentation of yielding intent did not perform better than not presenting it at all. Our study suggests signaling yielding intent on the eHMI before or during the deceleration of the vehicles supports an efficient crossing of the pedestrian.

Car manufacturers introduced a variety of non-driving-related features to enhance the trip experience for drivers and passengers. However, using an in-vehicle infotainment system (IVIS) can be mentally demanding and cause driver distraction. In addition, the usage of an IVIS is often restricted to front-seat passengers, which limits the possibility of assisting the driver. To address these problems, we demonstrate InShift, an initial concept to foster co-driver participation. It presents a physical interaction concept that allows delegating selected (e.g., highly mentally demanding) IVIS functions towards the front-seat passenger. Based on the functions the driver wants to delegate, the IVIS touchscreen moves towards the co-driver to provide easier access. Results from a pilot study (N = 8) suggest that InShift offers a positive user experience for drivers and co-drivers. The qualitative feedback reveals that InShift supports driver-passenger collaboration and allows the co-driver to be a better assistant.

With the development of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X), the difficulty of predicting traffic accidents is decreasing. Vehicles can communicate with each other about their status and receive notifications from traffic infrastructures about the prediction movements of other objects on the road. The efficiency of showing warnings to human drivers will become the bottleneck of reducing traffic accidents. In this study, we propose Virtual Horse: an anthropomorphic notification interface using implicit information to implicate potential dangers to the driver. The virtual horse represents the car’s behaviors and indicates potential dangers via body language like a real horse. We expect this anthropomorphic interface can reduce drivers’ reaction time for preventing dangers. We developed a prototype and ran a pilot study in a web-based driving simulation. The results showed potential benefits of using the proposed interface as well as showing useful insights for our future development of a user study.

Modern vehicles are complex working environments and feature a multitude of functionalities. This applies in particular to commercial vehicles, which are equipped with even more functions than passenger cars. Research showed the potential of context-adaptive user interfaces for reducing the complexity of the human-machine interaction but has focused on passenger cars. Therefore, a context-adaptive touchscreen-based system is conceptualized specifically for commercial vehicles such as trucks based on existing findings and design guidelines. Acknowledging the importance of being able to gather early user feedback for allowing fast iteration cycles, an interactive prototype was implemented, and a modular study setup developed. This combination was tested in an initial user study, which evaluated the usability as well as user experience in terms of the novel context-adaptive interface.

Autonomous navigation systems for powered wheelchairs are drawing attention to support older adults in moving outdoors. It is important to evaluate how the users behave and feel when they experience autonomous navigation for their development. VR-based simulators are used to evaluate user's behavior and subjective assessment in various scenarios without putting anybody at risk. However, it is difficult to reproduce realistic motion feedback in the simulator using motion platforms. Moreover, this physical feedback influences user's perception of self-motion, which affects their behavior and feeling. In this paper, we proposed a novel simulator that gives real motion feedback while simulating various scenarios by combining a virtual reality headset and a real powered wheelchair. Experiment results showed that subjective assessment of comfort during autonomous navigation differed between the proposed simulator and a simulator without motion feedback, indicating that perception of the wheelchair's behavior was enhanced by real motion feedback.

Although cycling is a promising transport modality for the future, cyclists could not substantially benefit from the safety gain in the last decades. To improve cycling safety and convenience, as well as extending the user base, we proposed to develop a self-balancing, assisted, and connected bicycle that transfers several assistance functions from the vehicle to the cycling domain. In this work-in-progress we sketch our vision of this bicycle, discuss some challenges for its development, and present early results. We present a self-balancing function using reinforcement learning and current progress in the development of a virtual reality motion bicycle simulator. If the progress in the projects continues as expected, we will report results from studies with human users soon.

While using highly automated systems, various non-critical automated driving scenarios can be identified in which trust plays a role. In this study, we investigated the change of trust in these scenarios with a digital “Feeling of Trust” indicator, through video-based online experiments simulating automated driving. Initial results show that trust even changes in these scenarios and revealed multiple influential factors. While trust seems to drop consistently in certain cases, we found individual differences in other events. With our experimental setup and findings, we provide a tool to examine trust aspects in an online study. This contributes to the understanding of how to design human-vehicle interactions in highly automated cars with the goal to calibrate trust under ordinary non-critical events.

Driver support and assistance features for vehicles have grown a lot during the last years. Despite a wealth of features, car brands and manufacturers differ in the implementation of these features. What insights can we gain on what drivers choose to (partially) automate their vehicles, given all these options? In this work in progress paper, we report the interim results of a survey which investigated this question. The survey contained hierarchical questions asking mainly Dutch respondents for their automated driving feature preferences. Results show that respondents choose a large number of features and were very diverse in their preferences. Nevertheless, there seems to be a preference of control type features over warning and passive-assist features. Based on these findings, we concluded that our sample supports the continuous development of features.

Applications of our everyday devices like smartphones and computers are becoming increasingly smart, adaptive, and personalized. We assume that users will soon expect the same behavior from their cars. Research shows that well-designed adaptivity brings the potential to increase a system's usability and thereby offer a safer driver-vehicle interaction. While car manufacturers already present first interaction concepts, there still seems to be a research gap regarding human factors challenges of adaptivity. In this paper, we will present some of these challenges and explain their relevance within the automotive context. Additionally, we present our research approach for developing recommendations for the design of adaptive user interfaces for in-vehicle comfort and infotainment features.

Various factors influence drivers’ response to Take-Over Requests in automated driving, and a wide range of designs have been proposed to improve transitions. Still, little research has investigated how systems could deliver Take-Over Requests at the best moment in time. In this paper, we sketch the idea of a reinforcement learning agent that learns to deliver Take-Over Requests at the right time so that drivers’ performance gets optimized, which could help to increase driving safety. We implemented such a system in Unity to evaluate this approach using a simple driver model. Our agent receives coordinates of the upcoming road segment and learns to deliver a Take-Over Request at an appropriate moment within a short time window. The reward function is composed to minimize the lateral deviation in the subsequent phase of manual driving. The initial results obtained are promising, and we will evaluate the concept with real human users soon.

Recent interest in semi-autonomous platoons has lead to novel Human-Machine Interface research. Additionally, studies seem to feature autonomous platoon simulations more frequently than traditional field tests. Simulation-based approaches often support rapid prototyping and evaluation of various HMI designs. However, existing simulation platforms can be bulky, expensive or require specialized hardware, which may present barriers. In this work, we present WebPlatoon, a novel web-based simulation platform. WebPlatoon seeks to support the design and evaluation of shared control scenarios featuring semi-autonomous platoons. By leveraging web technologies, our platform aims to present initial steps towards portable, cross-platform, and accessible platforms for semi-autonomous platoon research.

Driver monitoring may become a standard safety feature to discourage distraction in vehicles with or without automated driving functions. Research to date has focused on technology for identifying driver distraction—little is known about how drivers will respond to monitoring systems. An exploratory online survey assessed the perceived risk and reasonableness associated with driving distractions as well as the perceived fairness of potential consequences when a driver monitoring system detects distractions under either manual driving or Level 2 automated driving. Although more research is needed, results suggested: (1) fairness was associated with perceived risk; (2) alerts generally were viewed as fair; (3) more severe consequences (feature lockouts, insurance reporting, automation lockouts, involuntary takeovers) generally were viewed as less fair; (4) fairness ratings were similar for manual versus Level 2 driving, with some potential exceptions; and (5) perceived risk of distractions was slightly lower with automated driving.

Public transportation will become highly automated in the future, and at some point, human drivers are no longer necessary. Today many people are skeptical about such scenarios of autonomous public transport (abbr.: APT). In this paper, we assess users’ subjective priority of different factors that lead to personal acceptance or rejection of APT using an adapted online version of the Q-Methodology with 44 participants. We found four prototypical attitudes to which subgroups of participants relate: 1) technical enthusiasts, 2) social skeptics, 3) service-oriented non-enthusiasts, and 4) technology-oriented non-enthusiasts. We provide an unconventional perspective on APT acceptance that helps practitioners prioritize design requirements and communicate, targeting users’ specific attitudes.

High-fidelity driving simulators can act as test beds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. XR-OOM is a mixed-reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. In this work-in-progress paper, we present system requirements and specify our initial proof-of-concept system design. This work enables us to further develop measures for using such systems in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in hybrid real-world/simulated driving tasks.

Advanced Driver Assistance Systems (ADAS) can be found in almost every vehicle nowadays. They support drivers and usually ensure comfort and road safety. While some ADAS have been on the market for years and have proven themselves, some have only recently become available or are still in development. The present study investigates in which situations and contexts ADAS are considered to be most useful. We explored in which contexts a high degree of automation is desired and which implications this has for the design of ADAS. We found that users prefer a high level of automation for motorway driving and parking, but not for urban and overland driving. Our results suggest that the use of and trust in ADAS is related to their complexity. Based on our results, we derive recommendations for interface designers and car manufacturers.

Autonomous vehicles may become an important part of traffic in the next few decades. One of the key factors that contribute to the acceptance of the technology is that the user can understand and to a certain degree predict the behaviour of the automation. One way to communicate the automation's intention and behaviour are graphical human-machine interfaces (HMIs). An online survey was conducted to evaluate five designs for HMIs for turning manoeuvres in automated vehicles. During the survey the participants rated the HMIs based on videos. The overall ratings don't show a clear favourite, however, a separation of the participants regarding their trust in automation and technical affinity indicate preferences regarding the preferred level of abstraction. Furthermore, 97% of the participants agreed that HMIs like the ones presented would help them gain or increase their trust in automated vehicles.

So far, user interfaces are mainly developed for customers using highly automated driving functions, but little is known about the requirements on human machine interfaces for function developers. However, good development interfaces are important for efficient validating in real traffic test drives. Therefore, we present a focus group study with software developers of such functions to characterize their needs. Firstly, we identify typical situations where the automated vehicle executes an action other than the one expected, and, secondly, we gather developers’ requests for information to increase the traceability of driving decisions in general. We found that developers perceive actions involving lateral motion as being more non-transparent than with longitudinal motion and therefore identify a need for a more detailed explanation of these. Additionally, explanations should include more environmental features to help understand the reasons behind an action, and at the same time be tailored to the developers’ information interests.

Automotive industry is evolving through “Electrification”, “Autonomous Driving Systems”, and “Ride Sharing”, and all three vectors of change are taking place in the same timeframe. One of the key challenges during this transition will be to present critical information collected through additional onboard systems, to the driver and passengers, enhancing multimodal in-vehicle interaction. In this research authors suggest creating embedded tactile-feedback zones on the steering wheel itself, which can be used to relay haptic signals to the driver with little to no visual demand. Using “Haptic Mediation” techniques such as 3D-printed Embedded Haptic Waveguides (EHWs) and Constructive Wave Interference (CWI), the authors were able to provide reliable tactile feedback in normal driving environments. Signal analysis shows that EHWs and CWI can reduce haptic signal distortion and attenuation in noisy environments and during user testing, this technique yielded better driving performance and required lower cognitive load while completing common IVIS tasks.

Teleoperated driving as an enabler has the potential to bridge the gap to fully automated driving (SAE Level 5 [13]) by monitoring and controlling remotely highly automated vehicles (AVs, SAE 4) whenever their automation fails to do so. To ensure safe and efficient teleoperation, a user-centered human-machine interface (HMI) considering use cases, scenarios, and sequences relevant in teleoperated driving needs to be designed. For this purpose, this paper presents as a grounding an extensive system to classify scenarios relevant to remote-controlled AVs from a control center perspective. It is based on four major categories pertaining to the vehicles, the teleoperation workstation, interaction partners, and the environment. The system will serve as a scaffolding to categorize a catalogue of more than 150 scenarios derived from several research projects and this system will be adapted in future research to fit an ever-broader range of scenarios in the teleoperation of AVs.

Recent in-car infotainment features focus especially on enhancing the experience while moving. However, the experience of a car ride is not limited to the in-car experience. Especially on long-distance journeys, breaks are crucial to fulfill basic human needs and to reduce driver fatigue. However, breaks cannot always be planned in advance and spontaneous breaks often lack experience, e.g., when it comes to food choices or points of interest. This results often in short breaks and limited recovery from stressful driving. To encourage drivers to take breaks, we present a concept that recommends exploratory stops based on occupants’ interests (e.g., viewpoints, restaurants). Results from a pilot study (N = 10) show that personalized breaks lead towards a positive user experience. The qualitative feedback reveals that such a system encourages drivers to take more breaks because the stop fits their interest, makes them curious about the location, and is perceived as easy to use while driving.

One basic function performed by autonomous vehicles is estimation of their relative position within the environment, using LiDAR and high-precision 3D point-cloud maps. We propose a method for acquiring the relative position of smartphones by exploiting the localization information of mobility systems and their user interaction functionality. Generally, the technology required for estimating the position of a device is a trade-off between accuracy and the simplicity of the device. Our proposed method is a real-time, self-positioning approach, using a vehicle’s own position and mapping information as an initialization reference for estimating a smartphone’s relative position. The system continues to track the smartphone and to estimate its position after the initial linkup with the vehicle. The results of our experimental evaluation confirm that the phone can be accurately tracked and its position corrected, both indoors and outdoors, without any special equipment, and with a sufficiently small processing load for the smartphone.

Balancing safety and comfort is an important requirement in highly automated driving. In order to achieve safe and comfortable transportation using automated driving technology, cooperative driving, in which the driver and the system work together on the common driving task, may be more effective than a system that completely replaces the driver. Therefore, in this research, we address a cooperative driving system that provides steering assistance and steer-angle regulation based on force-feedback and steer-by-wire systems. We evaluate the safety and comfort of the system through a simulated driving study under different levels of system intervention in lane change maneuvers. Experimental results show that steer-angle regulation without force feedback gives the highest sense of control (involvement in driving) to drivers, although the driving maneuver is overridden by the system to maintain safety in the current condition.

Techniques for Human-Vehicle Interaction can be easily bounded by the limited amount of computational resources within the vehicles. Therefore, outsourcing the computations from vehicles into a powerful, centralized server becomes the prime method in practice. Such formalization between edge vehicles and centralized servers is denoted as the Internet-of-Vehicles, which forms multiple vehicles as a distributed system. However, there are no available supports to examine the feasibility and suitability of Human-Vehicle Interaction techniques, in the context of Internet-of-Vehicles. Such examinations are essential for newly proposed techniques, by providing experimental characterizations, suggesting detailed implementation schemes and understanding its real-world effects under Internet-of-Vehicle settings.

In this work, we report our progress in terms of a general-purpose and portable emulation platform, for examining the effects of novel Human-Vehicle Interaction techniques under the Internet-of-Vehicles setting. The key idea of our work is two-folded: (1) we provide an automatic extractor to retrieve the key patterns from Human-Vehicle Interaction workloads, so that our platform can facilitate with the needs of different scenarios/techniques; and (2) we leverage the configurable networking connections to provide abstractions regarding the interactions between edge vehicles and centralized servers, so that we can enable various types of emulations (e.g. geo-distributed applications). Our current progress is the finalization of the first prototype, and we leverage this prototype to characterize the impacts of a Deep-Neural-Network-driven in-vehicle application. Our results reveal the impacts of different implementations under the Internet-of-Vehicles setting, and our future works would focus on enhancing the characteristics of our prototype and scaling our study into a broader range of applications in Human-Vehicle Interactions.

Intelligent Personal Assistants (IPAs) have grown into technologically mature systems. However, instruments used for evaluating the usability and user experience of IPAs were developed two decades ago. This bears the danger for research and development to apply inadequate measurements to a novel technology. In this study, more recent scales from human-robot-interaction were used for evaluating speech-based assistants in vehicles in a Chinese sample. However, it cannot be assumed, that adapting a questionnaire from another context and culture leads to objective, reliable and valid measurements. Therefore, data was examined regarding internal consistency and factor structure. Cronbach's alpha was considerably high. However, a factor analysis did not support the assumed four-factor structure but rather a two-factorial solution. Findings suggest that adapting a questionnaire from a different context and culture, can affect its psychometrics. Consequently, the underlying postulated constructs should be treated with caution.

To ensure safety in future mixed traffic, automated vehicles (AV) will have to interact in an understandable way with other, more vulnerable road users (VRU). Current research has shown positive effects on the AV-VRU interaction for external human-machine interfaces (eHMI). The paper presents preliminary results of an online video study, focusing on the evaluation of different eHMI designs in situations with multiple pedestrians. Three eHMI communication designs using a 360° LED-light band and different combinations of additional pedestrians’ positionings were analyzed. The main goal of this study was to investigate how the communication designs were interpreted in situations with multiple pedestrians and whether negative effects may arise through their presence. Preliminary results showed that participants crossed the street more often, experienced more certainty in their decision, and felt more addressed if they were interacting with an AV with intention-based communication strategy compared to a static eHMI or no eHMI.

The present work-in-progress paper describes the development of novel user interface concepts that allow human operators to collaborate with self-driving heavy vehicles in a mining context. Concept development was performed within a user-centered design process containing three main steps. First, a study was performed to identify interaction points between heavy vehicle drivers and other human operators in mines. Second, potential interaction technologies were investigated. Finally, suggestions for interaction models were designed and implemented in 3D animated movies. The concepts were designed to support human operators performing loading tasks together with self-driving vehicles and utilize voice interaction and an augmented reality head-up-display to facilitate the interaction. In addition to the mining context, similar concepts were developed to support forklift drivers performing loading tasks in logistic centers. In the next step of this project, the suggested interaction models will be evaluated with mine workers and forklift drivers.

External Human-Machine interfaces (eHMIs) are typically proposed to facilitate explicit communication of vehicle intent to pedestrians. However, implicit communication through vehicle kinematics or movement patterns is shown to be the primary indicator of driving behavior and intention in traffic, for both manually-driven and automated vehicles. Unfortunately, subtle changes in kinematics are often hard to perceive and make it difficult to comprehend a vehicle’s consequent intention. We created a novel eHMI concept by using fluid movements to highlight and exaggerate the movement of the vehicle, and therefore emphasize this movement-based implicit communication. Preliminary results showed that the movement of the fluid called attention to subtle changes in the acceleration and deceleration of the vehicle, and was able to effectively indicate the nature of the change of vehicle speed, which in turn alluded to its intentions in traffic. This approach explores design solutions to highlight vehicle kinematics in facilitating communication of vehicle intention in traffic, for both automated vehicles of the future, and manually-driven vehicles of today.

External Human-Machine Interfaces (eHMIs) are proposed to address the communication gap between automated vehicles (AVs) and other road users. However, existing concepts of eHMIs are often limited in their ability to communicate in a clear, unambiguous, and scalable way, which hinders their effectiveness. eHMIs typically communicate by showing the vehicle’s driving intention, situational awareness, or its path/ trajectory; none of which is independently able to sufficiently achieve effective and unambiguous communication. We created a novel eHMI concept which combines these three communication elements in one unified design that (1) promises a more seamless, two-way interaction between AVs and other road users, and (2) is capable of a better scalable, high-resolution communication, i.e., facilitating communication beyond one-on-one communication while mitigating ambiguity regarding the specific recipient of the communication message. This addresses some of the limitations of existing eHMIs and paves a path towards a novel approach in eHMI design for future AVs.

External human-machine interfaces (eHMIs) are shown to support Automated Vehicles (AVs) in interacting with vulnerable road users such as pedestrians. Typically, eHMI concepts are light- or sound-based designs that communicate the AV’s intention with abstract visualizations, which are unfamiliar, not fully intuitive, and require learning. In the natural world, animals are shown to communicate their intention or disposition with a variety of visible reactions, using posture, gesture, or other means, which have implicit meaning by association with centuries of evolution. We explore the design space of biomimicry-inspired communication between AVs and pedestrians using external Shape-Changing (eSC) interfaces. We created six distinct concepts of eHMIs that employ external shape change and evaluated them in a focus group. Results show that zoomorphic, shape-changing-based eHMI concepts are promising in achieving intuitive communication about an AV’s intention in traffic. This may help in reducing the learning effort associated with abstract eHMIs, and ease the integration of AVs.

Automated driving system - dedicated vehicles (ADS-DVs), specially designed for people with various disabilities, can be beneficial to improve their mobility. However, research related to autonomous vehicles (AVs) for people with cognitive disabilities, especially Autism Spectrum Disorder (ASD) is limited. Thus, in this study, we focused on the challenge that we framed: “How might we design an ADS-DV that benefits people with ASD and their caregivers?”. In order to address the design challenge, we followed the human-centered design process. First, we conducted user research with caregivers of people with ASD. Second, we identified their user needs, including safety, monitoring and updates, individual preferences, comfort, trust, and reliability. Third, we generated a large number of ideas with brainstorming and affinity diagrams, based on which we proposed an ADS-DV prototype with a mobile application and an interior design. Fourth, we tested both the low-fidelity and high-fidelity prototypes to fix the possible issues. Our preliminary results showed that such an ASD-DV would potentially improve the mobility of those with ASD without worries.

Facial Expressions are valuable data sources for advanced Human-Vehicle Interaction designs. However, existing works always consider the whole facial expressions as input, which restricts the design space for detailed optimizations. In this work, we make the hypothesis that facial expressions can exhibit significant variations during the driving procedure. Our goal in this work-in-progress is to justify this hypothesis, by performing detailed characterizations on the drivers’ facial expressions. To this end, we leverage Local Binary Fitting, a novel mechanism for selecting representative feature points from facial images on the fly, for our characterizations. Our characterizations reveal that, among six major components of facial feature points, there are significant variations of correlations with a certain vehicle status (i.e. Vehicle Speed), in terms of (1) the time spots during the driving procedure; and (2) the gender of the drivers. We believe our works can serve as a starting point to incorporate the characteristics of our findings with a great amount of adaptive and personalized Human-Vehicle Interaction designs.

In future urban traffic, communication abilities of automated vehicles (AVs) are needed to enable a safe interaction with pedestrians as so-called vulnerable road users. Dynamic human-machine interfaces (dHMIs) and external human-machine interfaces (eHMIs) are designed to enable AVs to communicate implicitly, e.g., via vehicle dynamics, and explicitly, e.g., by light signals. To this point, it is neither sufficiently studied how the exact interplay of both communication tools should take place nor how this should be considered for an automated bus. This study aims to shed light on pedestrians’ interaction with an automated bus by combining vehicle behavior (dHMI) and different eHMIs. The main focus was on the effect of contradictory communication messages on pedestrians’ willingness to cross. Results showed that for non-yielding conditions, a dynamic eHMI that displayed an erroneous yielding intent lead to a significantly higher pedestrians’ willingness to cross compared to static eHMI or no eHMI.

As automated vehicles become more prevalent, designing interfaces that best fit all users, especially ones in minority populations, is a pressing but difficult goal. System-driven adaptation is a commonly used approach as it is easier and created by experts but, has innate flaws. Customization, on the other hand, allows users to consciously alter the interface to appear and operate in a manner most suited to their needs and wants. However, various components of the interface have different constraints, capabilities, and requirements with the amount of customization appropriate. In this workshop, we will dissect an expansive taxonomy for customization and develop a series of levels in order to get the full benefits from customization, which in turn can help engineers and designers in creating more user-centered systems.

Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.

The emergence of automated driving systems will influence roles and practices in many parts of work life. Human factors and user interface design will play an important role in shaping the transition towards more productivity and well-being. The AutoWork 2021 workshop builds on its predecessor workshops by refining a research agenda and drafting concrete research studies and projects towards achieving this goal. This year’s version will especially tackle the challenge of designing both for bottom-up “worker-driven” empowerment and engagement in individual, partly automated transport, as well as for supporting affected users and stakeholders in the systemic economically-driven introduction of fully autonomous vehicles in closed intralogistics areas. In a two-session schedule tailored to fit the requirements of an online event, participants will evaluate gathered requirements from previous workshops and projects on these topics and define relevant user stories and elaborate experimental designs with measurable outcomes to contribute to the research roadmap.

“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.

Different drivers’ states and emotions can affect negatively the driving performance. Recent advances in affective computing now give the opportunity to measure the users’ state or emotions using various sources of data such as physiological signals or voice samples. Conveying biofeedback in the car could help to make roads safer and improve users’ health and mental state during a ride in an autonomous car. This workshop aims at selecting the drivers’ hazardous states and emotions that are crucial to be assessed, as well as how to convey the appropriate biofeedback to the driver, using multimodal interaction in the car.

Urban Air Mobility (UAM) is beginning to gain attention as an expansion of the current provided transportation. However, the unfamiliarity of air transport can make the users reluctant to try these distinctive experiences. Thus, in this workshop, we are going to present the current state of the UAM research trend and encourage participants to discuss in-depth and share the User Experience (UX) considerations for designers and developers of the technology. The aim of the workshop is to discuss potential issues and expected challenges for the introduction of UAM into the transportation system. We expect to share our understanding of UAM with experts in automotive fields and establish future research directions.

With continually advancing automation capabilities in vehicles, there is increasing potential for these capabilities to be used not only as stopgaps towards full automation but to enhance humans’ manual driving capabilities during this transition phase and beyond. By employing smart automation assistance (e.g., highlighting of relevant roadside information, maneuver interventions and corrections), it might even be possible to enable automation assisted “manual” driving for those, who might not be able to drive otherwise (older adults, individuals with impairments). In this workshop, we intend to explore this problem together with the participants and identify potentials for automation assistance to enhance manual driving performance, and what in-vehicle interfaces can contribute in this regard.

This online workshop invites participants to elaborate the infotainment systems of semi-autonomous cars with passengers’ priorities in mind and develop quick auto-UI solutions by utilizing “passenger infotainment modes”. These modes represent a front-seat passenger's changing relations with the driver, the surroundings, and the infotainment system. There can be various strategies for an automotive user interface to adapt to these diverse situations where the passenger prioritizes one agent over the other (e.g., co-navigating with the driver, enjoying the scenery without being interrupted by the system notifications, being immersed in a private bubble of entertainment). The workshop provides each team of participants with a future travel scenario and encourages them to enhance its narrative by defining possible activities or tasks delivered through automotive infotainment systems. Finally, these narratives are enriched further by addressing a selection of the passenger infotainment modes with new proposals for the auto-UI content, functionalities, or interactions.

This work aims to connect the Automotive User Interfaces (Auto-UI) and Conversational User Interfaces (CUI) communities through discussion of their shared view of the future of automotive conversational user interfaces. The workshop aims to encourage creative consideration of optimistic and pessimistic futures, encouraging attendees to explore the opportunities and barriers that lie ahead through a game. Considerations of the future will be mapped out in greater detail through the drafting of research agendas, by which attendees will get to know each other’s expertise and networks of resources. The two day workshop, consisting of two 90-minute sessions, will facilitate greater communication and collaboration between these communities, connecting researchers to work together to influence the futures they imagine in the workshop.

The elderly, children, and people with disabilities are among vulnerable users who can benefit the most from the autonomous vehicles (AVs). Yet, most AV concepts discussed in the past decade, including the AutomotiveUI conferences, seem to focus on the mobility needs of younger and middle-aged drivers, who are the overeducated working population, technological enthusiasts, and above middle-class users. In that regard, the third workshop on Localization vs. Internalization aims to explore these disparities, identify the accessibility barriers and search for research approaches that can increase the accessibility opportunities of the vulnerable AV end-users. Built upon the findings from the previous workshops, which focused on diversity, inclusion and differences among cultures regarding AV-related research approaches, the purpose of the present workshop is to provide an in-depth insight into the state of global AV research and identify areas that still need to be explored to increase the AV accessibility, locally and internationally.

Through automation, future mobility has the potential to offer services to a broader range of users than ever before. However, users at the margins are often not represented in the design process or vehicles, and specifically automated vehicles. This can lead to these users being overlooked and ultimately excluded from the use of automated mobility services. Therefore, it is vital to raise awareness for the inclusive design of future mobility services and reflect on how they can become part of automotive design and research practices. In this AutomotiveUI 2021 workshop, we will explore this topic from different angles through expert talks and reflections, followed by discussions. The expected outcome of the workshop is the development of and work on an agenda for accessible and inclusive mobility in the age of automated vehicles.

Intelligent agents (IAs) have been widely used at home and have been gradually introduced into driving contexts. While many studies researched agent features and their influences on user perception toward in-vehicle agents (IVAs), what attributes make IVAs unique and how people perceive them differently from at-home agents remain unclear. Therefore, the proposed workshop aims to bring up a discussion among researchers and practitioners worldwide to contribute insights to a list of characteristics and design considerations for in-vehicle intelligent agents. Features specialized in IVAs will also be discussed in the workshop, along with the preference for the agent form. We expect to extract innovative research and design considerations to benefit IVA research and the AutomotiveUI community.

It appears that autonomous systems are replacing human in the driving task. However, autonomous driving abilities do not mean that vehicles should not interact with their drivers/passengers or their environment anymore. There are still many scenarios where either the automated system cannot handle the driving very well, or human wants to spontaneously influence the behavior of the system to meet their preferences. Thus, beyond the hype of autonomous driving, a large space opens for human-vehicle cooperation at a different level of automated driving. As this topic draws more attention both by academia and industry, we organize this workshop to in-depth identify potential research opportunities of it under the latest technology of automated driving. In this workshop, participants will discuss the motivations of driver/passenger’s intervention, generate the use cases of cooperative driving, and explore means of cooperation and interaction that human and vehicle would exchange intent smoothly. It is expected that the workshop will consolidate existing knowledge of human-vehicle-environment cooperation and provide insight for future works.

Competition for visual attention in vehicles has increased with the integration of touch-based interfaces, which has led to an increased crash risk. To mitigate this visual distraction, we designed an in-vehicle gesture-based menu system with different auditory feedback types and hand-recognition systems. We are conducting an experiment using a driving simulator where the participant performs a secondary task of selecting a menu item. Three auditory feedback types are tested in addition to the baseline condition (no audio): auditory icons, earcons, and spearcons. For each type of auditory display, two hand-recognition systems are tested: fixed and adaptive. We expect we can reduce the driver’s secondary task workload, while minimizing off-road glances for safety. Our experiment would contribute to the existing literature in multimodal signal processing, confirming the Multiple Resource Theory. It would also present practical design guidelines for auditory-feedback for gesture-based in-vehicle interactions.

The transport logistics sector is expected to be a promising ground for the roll-out and business integration of automated vehicles. Within this transition towards driverless vehicles, fleet management and control interventions will have to be taken over by logistics personnel. However, so far, a specific needs and expectations analysis with regard to the involved stakeholders towards automated road transport logistics have only been analyzed to a limited degree, and consequently there is so far no systematic approach towards designing corresponding user interfaces. This demo video highlights the requirements gathering activities within the project AWARD, which investigates and develops all-weather autonomous real logistics operations and demonstrations. The demo video introduces into the different perspectives of the involved stakeholder groups, and it illustrates addressed use cases and operational scenarios. The derived acceptance factors model and first impressions of preliminary results are provided.

Automated driving seems promising to reduce crashes caused by human error. However, in the transition towards automated driving, a human is still required in some automation levels in some circumstances. Specifically, in conditional automation or SAE Level 3, a human needs to be able to continue the driving task any time the vehicle requests it. This means that throughout the L3 automated driving, this ”fallback-ready user” needs to remain in a state to continue driving, even when they are engaged in other tasks, such as watching a movie. We designed three interventions with the aim to increase their fallback-readiness and have tested them in a high-fidelity video driving simulation study. In this video, we present and describe the interventions, the study design and the setup to test the interventions.

Although automated driving (AD) systems progress fast in recent years, there are still various corner cases that such systems cannot handle well especially for predicting the behavior of surrounding traffic. This may result in discomfort or even dangerous situations. Results from a previous Wizard-of-OZ study suggest that the collaboration between human and system at the prediction level can effectively enhance the experience and comfort of automated driving. For an in-depth investigation of the confluence between AD and driver, a prototype was implemented in a driving simulator driven by a functional AD system that has been partially validated on the public road. Furthermore, we designed and implemented a gaze-button input for intuitive vehicle referencing and a graphical user interface (GUI) for enhancing the explainability of the AD system. Three typical driving scenarios in which an AD could take advantage of the human driver’s anticipation to drive more comfortable and personalized were created for subsequent evaluation.

In this research, we designed multimodal trip support combining autonomous vehicles and autonomous robots. The video introduces a scenario using a prototype of the system which enables a user to request a vehicle ride and service robots at one time. The vehicle takes the user from their location to another building. Robots take the user and their luggage from a vehicle drop-off to a destination in the building. The request is made on their personal device using a web application. In addition to the device, multimodal user interfaces such as robot’s HMIs (human-machine interfaces) are used for user interaction to provide a seamless experience of the trip. Its background system is connected to fleet management systems of vehicles and robots and building facilities, for example elevators and cameras, via network. Providing facility-vehicle and facility-robot interactions, the system supports smooth automatic operations of the vehicle and the robots on the way.

Ambient LED displays have been used to provide peripheral light-based cues to drivers about a vehicle's current state, along with providing requests for a driver's attention or action. However, few studies have investigated the use of an ambient LED display to improve drivers' trust, perceived safety, and reactions during L3 automated driving. Due to the ambient nature of an LED lightband display, it could be anticipated that it would provide reassurance of the automation status while automation is on, along with providing a gentle cue for non-urgent transitions of control. This video submission presents a methodological overview of a driving simulator study designed to evaluate the effectiveness of an ambient peripheral light display (Lightband HMI) in terms of its potential to improve drivers' trust in L3 automation, along with a comparison of a Lightband and Auditory HMI in terms of their effectiveness in facilitating transitions of control.

Intelligent agents (IAs) are widely being adopted in our daily lives, and much research on the design of communication with IAs have been conducted. However, almost all research focuses on the interaction between a human operator and an agent. As more and more IAs are being used, there is a possibility that more than two IAs coexist. Then, in a driving context, what if in-vehicle agents (IVAs) and other IAs coexist and show conflicting suggestions or responses? We are interested in answering the related research questions in this situation. As a first step, we developed scenarios and presented the video to embody futuristic situations that a user interacts with multiple IAs. It is expected that this effort can call attention to this topic, and the developed video can be utilized further to explore the related research questions.

Driving simulators are typically used to evaluate next-generation automotive user interfaces in user studies as they offer a replicable driving setting that also allows studying safety-critical and/or future systems. However, this AutomotiveUI experience research is often limited to university or company campuses and their students and staff. To combat that, we introduced a mobile driving simulator lab in a car trailer. We present features but also limitations of this lab, report on experiences after the first days of operation, and discuss further use cases beyond research. During 7 days of user studies with the trailer at a national garden festival, we conducted trials with more than 70 participants from diverse backgrounds. However, executing studies at public events also has its limitations, e.g., on accepted trial duration and potential for biased responses.

The test and development of Automated Driving Systems is usually realized by scenario based testing or virtual testing environments. These methods apply artificial targets to trigger the safety critical functions under specific predefined scenarios, as the NCAP or IIHS test catalogues. Despite having a good reproducibility, these approaches hardly permit the evaluation of new interaction concepts like external Human-Machine Interfaces (eHMIs), since the interaction between real users and the vehicle cannot be realistic reproduced without risks to the participants. The novel Mixed Reality Test Environment (MiRE) overcomes this limitation by the integration of Virtual Reality (VR) technologies, Dynamic Vehicle-in-the-Loop (DynViL) and the Virtual Environment. In MiRE the movement and positioning of the vehicles and the VRUs are tracked in real time and reproduced in the virtual environment. Synthetic data from the virtual environment is generated to stimulate the vehicle and the human participant, enabling a safe interaction between both entities.

This study focuses on human driving behaviour to identify key non-verbal cues which may inform a passenger of the driver’s intentions. An anthropological inquiry, supported by live remote field observations and follow-up interviews, aims to understand a) the nuances and mechanisms, i.e. intention cues, through which human drivers consciously or unconsciously convey their driving intention, b) how passengers recognise and interpret those intention cues, and c) the role that the clarity, ambiguity or absence of these cues may play in passenger comfort or trust. Lastly, this research designs a live remote observation protocol to analyse the exchange of subtle intention cues between driver-passenger pairs during the driving task.