Teleoperation is considered an interim solution for the introduction of automated vehicles in public transport. It describes the possibility for a remote driver to take control of the vehicle if there are uncertainties regarding the current driving situation. Despite the efforts of researchers and legislators and the obvious advantages of the technology, explicit regulations for teleoperation are still pending. One reason for this is the absence of clarity regarding which aspects need to be regulated and in what form. The aim of this paper is to provide an overview of different categories and specifications that need to be defined to support the derivation of legal requirements. For this purpose, we conducted a workshop in Germany with experts from various disciplines who have been involved in the integration of automated driving for many years. Based on insights from the fields of law, technology, human factors, and licensing authorities, a list of the requirements was developed. By presenting a holistic approach, the results should help to develop a common consensus on the scope of the regulations and to define clear requirements and specifications for the approval process of teleoperation on public roads.

With the accelerating embrace of automated vehicles, lack of public trust has become a critical concern worldwide. Nonetheless, the literature shows a significant and thorough effort to increase trust in automated vehicles. Biophilia is one strategy being utilized to establish a trustworthy environment, although it has not yet been applied in the automotive industry, thus, a biophilic interior design was developed and applied in a static driving simulator using a mixed reality headset for investigating the effect on the driving experience. A gender-balanced sample (11 F,11 M) was asked to perform an in-lab experiment and experienced four different driving scenarios, with and without Biophilic interventions. The regression results showed that initial trust is the only effective parameter on affective trust in automated vehicles is$(P=0.05)$, and no significant relationship was found between gender and affective trust. As conclusion, the study allowed to assess that initial trust in self-driving cars plays a more important role than gender or driving experience contrary to initial expectations.

For expert users to accept Generative AI (GenAI) as a true collaborative partner, it must move beyond simple task-awareness to an understanding of their workflow’s underlying structural rules. This paper introduces a paradigm for AI collaborators that moves beyond simple task awareness to an understanding of the semantic and hierarchical relationships within a component-based system. We investigate this concept within the context of the design-to-code workflow, where inefficiencies arise from the modification of components within design systems. Through two empirical studies with designers and developers, we found that GenAI output was often rejected because it violated the component hierarchy. Designers required granular and visual control for refinements, whereas developers valued automated setup but required transparent validation of the generated code’s logic. Based on these findings, we contribute design guidelines for achieving Component-Structure Awareness (CSA), with two core principles: the Atomic Recommender, which provides assistance that respects the component hierarchy, and Communication Archetypes, which allow GenAI to adapt its interaction style to the user’s role and the atomic nature of their task. This work provides a new, higher-level concept for designing the next generation of truly collaborative GenAI agents.

As an alternative to the conventional control allocation, as described by the SAE, cooperative driving concepts have emerged in recent research. These concepts aim towards making the human driver and automated vehicle work together in a team. For effective collaboration, it is critical that the driver be able to predict the actions of the automation. In a simulator study, we investigated the effects of communicating the automation's decision and level of confidence to the human driver in order to increase predictability in a cooperative driving scenario. We found that participants tended to take longer to make a choice when a decision and level of confidence were displayed. Additionally, displaying the automation's decision significantly increased the participants' decision time and improved the correctness of their decisions. In addition, all tested HMIs received positive evaluations for trust in automation and usability, with the Baseline HMI receiving significantly higher scores.

Human factors in the context of advanced driver assistance systems (ADAS) are commonly investigated in driving simulators. However, the validity of this approach has not been sufficiently verified. This article compares human factors concerning a level 2 ADAS between real-world driving and two different simulators (simBIG vs. simSMALL) in a three-part user study (N = 93). The results show that usability and the mental model were valid in both simulators. Cognitive load, acceptance, and user experience were also valid, but only in simBIG, with some anecdotal support for the validity of simSMALL. Surprisingly, trust was higher in the simulators compared to real-world driving. Participants consistently took over control from the level 2 system at the same locations throughout simulated and real-world drives, and system usage increased over time in all three test environments. These findings indicate relative validity of the simulators. Further research is necessary to verify the transferability of the presented findings to other ADAS and simulators.

Shared Automated Vehicles (SAVs) present a promising solution for future urban mobility. However, SAVs will reach the limits of their capabilities in some edge cases. Similar to personal AVs, passengers in SAVs might be utilized for this purpose, thus, methods for interaction between passengers and the automation or a teleoperator need to be explored. This study investigates whether the presence of other passengers leads to more passive behavior in critical situations (bystander effect). The results did not show significant differences in the participants’ behavior depending on whether the ride was experienced alone or with other passengers. However, the qualitative data indicate that the presence of other passengers can trigger psychological processes that promote a bystander effect. The findings emphasize the importance of considering group effects in the context of SAVs. By understanding these dynamics, we aim to help design SAVs to promote safer and more inclusive future transport systems.

As the industry advances towards the technical realization of passenger drones, there is a noticeable trend where the wishes and acceptance factors of future users are not given primary consideration. Although several companies are progressing with prototype development and market-entry preparations, ongoing research on the requirements for electric Vertical Take-Off and Landing (eVTOL) vehicles remains crucial. For such a novel form of aerial transportation, understanding passenger needs is key, as their acceptance and willingness to use these services will ultimately drive revenue for manufacturers and airlines. To this end, our study, involving 51 participants in South Korea, delved into three key areas of user acceptance: safety considerations of eVTOLs, preferred vertiport locations, and concerns about weight. The findings indicate that piloted passenger drones are acceptable to the public, particularly when they assure safety and offer sufficient luggage storage, among other factors. These insights are vital for the engineering design and development of eVTOLs, highlighting opportunities for weight and energy savings by aligning with passenger preferences and requirements.

Urban Air Mobility (UAM) has the potential to revolutionize the way we travel. As transportation evolves, passenger drones, also known as air taxis, are poised to integrate into our daily mobility routines. Ensuring the overall safety of these aerial vehicles through air traffic certification and operational air traffic control is paramount. It is crucial to identify and evaluate problematic scenarios that may arise during operation, particularly their impact on passenger acceptance and user experience (UX). Critical situations can emerge, potentially causing passenger distress and, in worst-case scenarios, posing safety risks. Due to their lower flight weight and altitude, turbulence plays a significantly more prominent role for these vehicles compared to larger passenger aircraft. To address this issue, we have undertaken a virtual reality study designed to explore turbulence experiences during flight. This research primarily concentrates on evaluating passengers’ safety perceptions and examining the efficacy of various mitigation and calming strategies. These strategies include but are not limited to, breathing exercises and the deployment of a supportive avatar. The study aims to gain a deeper understanding of passenger responses to in-flight turbulence and to assess the potential of these interventions to enhance passenger comfort and safety. Preliminary findings indicate that calming animations and breathing exercises can have a positive impact on passengers’ perception of safety. The insights gained from this research aim to contribute to the development of support measures for all types of critical flight situations.

The advancement of automated shuttle buses has yet to reach a stage where they can operate fully autonomously on public roads. To address this limitation, academia and industry are exploring remote monitoring and control as an interim solution. Despite these efforts, certain scenarios, such as assisting a disabled person with boarding, remain challenging to manage remotely. Our study, conducted in a shuttle bus mock-up, explores the potential for passenger involvement in managing situations like this via collaboration with a remote control center or the vehicle itself. Our results indicate a general willingness of passengers to help in exceptional cases. However, there was a noticeable reluctance to deal with technical malfunctions, with participants showing low acceptance levels for such interventions. Interestingly, the study revealed that experiencing these critical situations led to a significant increase in trust toward automated driving technologies. These insights have important implications for developing Human-Machine Interfaces that effectively support passengers and remote operators in handling unusual situations.

Virtual Reality (VR) is commonly utilized to examine driver interactions with vulnerable road users (VRUs) in an effective and secure manner. Recent studies, however, have highlighted issues in VR simulations, particularly concerning the authenticity of state-of-the-art pedestrian agent behaviors. These inaccuracies can compromise the perceived realism of the situation, potentially leading to unrepresentative driver reactions. This paper aims to show-case enhancements in pedestrian agent models and evaluate their subsequent advantages. To this end, real pedestrian movements, captured via motion-capture technology, were compared with outputs from a contemporary pedestrian agent model within a VR driving simulator experiment. The findings underpin the advantages of using motion-captured pedestrians to enhance social presence. Additionally, participant feedback emphasized that certain elements, such as head movements, explicit gestures, and subtle cues like hesitation before entering the road, were crucial in distinguishing realistic from unrealistic agents. These insights contribute significantly to refining the focus for systematic advancements in (pedestrian) agent models in VR environments. Such improvements are pivotal in augmenting the users’ sense of presence and the behavioral accuracy of the simulations.

Driving automation is being pushed towards widespread adoption, with significant progress being made continuously. Once the automated vehicle takes over the driving task, the question arises as to how people want to be driven by automation. In order to gain insights into this, a driving simulator study was conducted, in which N = 49 participants experienced an automated urban drive where pedestrians crossed or attempted to cross the road in front of the automated vehicle at various points. The driving style of the automated vehicle was manipulated (aggressive/defensive), while participants rated their desire for control, trust in automation, and acceptance. The results show that there is no general preference for one driving style over the other. Rather, the preferred behavior of the automation depended on the respective traffic scenario, with drivers preferring defensive driving in some crossing situations and aggressive driving in other situations. The present study indicates that, generally, defensive driving behavior is not necessarily the solution preferred by the user. Instead, a more nuanced approach based on the traffic scenario is recommended.

Cooperation between drivers and automated vehicles requires transparent communication of the automation’s current status. This can be achieved by communicating confidence or certainty in its current perception or decision. We evaluate different sensory modalities for communicating information about how safely an automated driving system can perform the driving task in critical traffic situations to a driver who is present as a cooperator. We aimed to improve communication between the driving system and the human driver, ultimately increasing the overall driving experience and performance. In a virtual reality driving simulation study with 34 participants, we presented confidence information across three modalities: visual, auditory, and vibrotactile, compared to a baseline condition. Our results indicate that communicating automation uncertainty through the auditory and vibrotactile modalities improved user experience, trust in automation, and perceived safety. At the same time, interactions with the Non-Driving Related Task were reduced by communicating confidence information in critical driving situations.

One way to improve road safety for cyclists is the development of hazard notification systems. Instead of in field experiments, such systems could be tested in safe and more controlled simulated environments; however, their validity needs verification. We evaluated the validity of mixed reality (MR) simulation for bicycle support systems notifying of dooring hazards. In a mixed-design study (N=43) with environment type(MR/test track) as within and hazard notifications (with/without) as between factor, comparing subjective and objective measures across environments.

Perceived security is crucial for the widespread adoption of shared automated vehicles (SAVs) and shuttle buses. However, there is currently no validated instrument to measure perceived security in this context, and little research has been done to determine the factors that contribute to perceived security. We propose the Perceived Security Scale for Shared Automated Vehicles (PSSAV), a questionnaire that assesses various aspects of perceived security in SAVs. The scale was evaluated using an exploratory, data-driven approach in a pilot study with 60 German participants, and a main study with 114 German and 101 Colombian participants experiencing a positive or negative ride in an automated shuttle bus (between-subjects design) presented as videos in an online study. The results suggest that trust, privacy, and control are key factors that influence security in the context of SAVs. The PSSAV questionnaire is reliable and sensitive to manipulation, indicating its construct validity.

Head-mounted displays (HMDs) are considered a promising, highly immersive display technology, which has been widely discussed in the context of driving simulation. The literature is heterogeneous to date with regard to the effects of HMDs on simulator sickness, the sense of presence, and perception. In the present study, a comparison between a modern HMD with a screen-based (LED wall) simulator is conducted in a repeated-measures driving simulator study including N = 31 subjects. The results indicate that the HMD is neither better nor worse, but performs equally well as the screen-based simulator in terms of simulator sickness, presence, and active distance perception. Evidence for passive distance and speed perception was only anecdotal, though also mostly points at a null-effect. The only (anecdotal) evidence of worse performance in the HMD simulator was in an active braking task. Accordingly, the present study did not identify disadvantages of using current HMDs in driving simulation.

The introduction of automated mobility will lead to new forms of interaction with vehicles. Exactly how this interaction will occur, particularly in coordinating connected vehicles, is largely unexplored. We investigated whether a built-in system that directly combines a messenger function with the route change function is better for destination coordination among multiple vehicles than traditional messenger apps on the smartphone. We conducted a user study with N = 17 participants who experienced a simulated ride in a low-fidelity prototype of a shared automated vehicle (SAV). Each participant tested both interaction options with imaginary passengers in a connected SAV. Results showed a high usability for both solutions. However, the qualitative interview data showed participants preferred communication via the built-in messenger system. The simple and clear operation of the holistic concept was particularly emphasized, while concerns were expressed about privacy and hygiene.

The introduction of automated vehicles forces us to redesign the human-machine interface (HMI) in motor vehicles, and vibrotactile cues have already been proven to be a suitable means to restore driver attention in situations of visual distraction. As vibration can be provided in many different ways, this paper presents the results of a user study aimed at specifying suitable vibration patterns that are accepted by potential users. N = 14 participants performed a visual-motoric task while being presented with six different seat vibration patterns supporting a visual task. The initial results confirmed a reduced reaction time when using vibrotactile cues and showed similar results for lateral versus whole-seat cues. As an implication, lateral concepts are suggested for further studies, as they showed a tendency to be perceived as more pleasant and comfortable and were overall preferred more often compared to whole-seat vibration patterns, which in turn were easier to perceive.

The landscape of transportation is evolving, giving rise to novel mobility concepts such as automated shuttle buses which might become a commonplace mode of transportation in the next decades. However, the individual user’s and society’s acceptance of this new form of mobility requires finding solutions for basic human factor issues and a pivotal aspect of achieving this acceptance revolves around comprehending the decisions and behaviors of the automation. In this work, we aimed to explore passengers’ explanation needs within automated shuttle buses and derived strategies for delivering essential information effectively. To accomplish this, we conducted a user study involving N=16 participants. This study encompassed a brief driving simulation experiment that compared default explanations with on-demand explanations, followed by group discussions. Initial results revealed that default explanations, accessible on a shared screen visible to all passengers, emerged as the preferred method for receiving information. Moreover, participants expressed a preference for explanations to be provided solely in safety-critical or unusual situations. Additionally, our analysis underscored the psychological dynamics of group settings and the apprehension of potential judgment by peers as influential factors in participants’ evaluations of information concepts.

Although automated driving is becoming a more widespread technology, there is still a lack of understanding about how to best communicate information to drivers in the specific situation of automated parking. This mixed-method user study aimed to address this gap by evaluating the preferences of users for information about the vehicle’s behavior in automated parking and the impact on user experience and situation awareness. An explainable concept displayed as augmented visualizations in the vehicle’s windshield was prototyped and evaluated in a driving simulation study and a qualitative interview. N = 25 participants provided insights into the development of more effective and user-centered interface designs for automated parking. As a result, the explainable concept was preferred by the participants and led to a higher user experience and explainability. This work contributes to the design and evaluation of future automated parking systems and provides a step towards more user-friendly automated driving experiences.

Automated vehicles (AVs) enable driver-passengers to perform non-driving related tasks (NDRTs). However, it remains unclear if and how they would engage with NDRTs. Therefore, we developed a questionnaire on NDRTs, the QNDRT, for SAE Level 3 (L3) and 5 (L5) driving automation. It comprises 24 items with a general NDRT Work subscale querying general aspects of working while driving in an AV (8 items; L3: 4 items, L5: 4) and a specific NDRT Communication subscale assessing different communication modalities (16 items; L3: 8, L5: 8). Hence, we carried out a cross-sectional questionnaire study and queried 725 participants (351 female, 374 male) from 18 to 96 years. We applied factor analyses and extracted a stable unidimensional factor structure for both NDRT subscales with good psychometric properties (e.g. high and clear factor loadings; satisfactory communalities, item discrimination, and internal consistency). The initial findings revealed that significantly smaller values were assigned to both subscale factors by participants older than 55 years in contrast to younger ones and by women when compared to men. The QNDRT can be used in future (quasi-)experimental L3 and L5 studies and in population surveys to obtain more insight into working and communicating while driving in an AV.

Cycling is an environmentally friendly means of transport with growing popularity. However, there is still potential for increased road safety in the future. We argue that by making assistance systems available to cyclists, accidents could be prevented. In this paper, we focus on potential accidents caused by vehicle doors opening in a cyclist’s path of travel, which can lead to serious injuries to the cyclist. Using a mixed-methods approach, we explored how messages informing about a potentially opening door ahead are perceived and understood regarding usability and intuitiveness in a bicycle simulator study (N=24). We investigated how visual messages, visual messages and auditory icons, and visual and voice messages on a head-mounted device are subjectively perceived. We also assessed our participants’ attitudes toward using such systems and mixed reality simulations for bicycle safety research in general. Our results show that participants preferred visual messages and auditory cues and found these types of notifications more enjoyable than visual messages alone. Furthermore, the results suggest that such a system would be used while cycling. Participants agreed that mixed reality simulation is suitable for testing/evaluating novel support systems and finding initial insights in the first step but confirmed that real-world testing on the road is mandatory nonetheless.

Future users of automated vehicles will demand the ability to perform diverse and extensive non-driving related tasks. However, prevailing restrictions in the car require new interaction concepts to enable productive office work. Intelligent voice-based interfaces may be a solution to facilitate productivity while at the same time keeping the “driver in the loop” and thereby maintaining safety. In this work, we investigated the repair problem of productive speech-to-text input in a highly automated vehicle. We examined the user experience of selecting/navigating to an incorrectly recognized word using only speech, pointing and clicking on a touchpad, and using mid-air hand gestures. Results indicate that hand gestures (condition VaG) have high hedonic quality but are not considered viable for error correction in productive text input. On the other hand, the unimodal (Voice-only; baseline) and touchpad-based point-and-click (VaT) approaches to error correction were rated equally well in the hypothesized “mobile office” automated vehicle. The utilized remote study execution methodology proved to be a useful intermediary tool between pure online surveys and on-site studies for qualitative research during a pandemic but suffered from a lack of fidelity and options for objective usability and safety evaluation.

Advanced Driver Assistance Systems have been integrated in vehicles with the aim of reducing traffic accidents caused by human error. Due to the safety-critical application of these systems, a reliable implementation requires an extensive testing process of hardware and software units as well as the entire integrated system. The increasing complexity of these systems, which incrementally have more control over the vehicle, makes purely real-world safeguarding impracticable. As a solution, complementary virtual tests are used to support the validation process in a safe and accelerated way. However, a method for distributing test cases across real and virtual domains according to the relevance of the test method has yet to be defined. In this paper, we investigate different levels of reality in testing ADAS/automated driving functions and derive advantages, disadvantages, and system limitations for each. An emergency braking system serves as a use case, which is evaluated using Hardware-in-the-Loop, Sensor-in-the-Loop, Vehicle-in-the-Loop test methods, as well as vehicle tests on a proving ground. The obtained results show that simulation-based testing is a useful complement to real-world testing. However, various phenomena such as actuator delays or camera lenses have a large impact on the obtained results and need to be taken into account (in models) to ensure the realism of the simulation-based approaches. In addition, the inclusion of real components increases the deviations between test repetitions, being necessary to conduct real tests to evaluate the final performance of the integrated system.

Studies on imminent Take-Over Requests (TORs) in automated driving have mainly addressed safety aspects rather than user experience (UX). In this study, we investigated the fulfillment of user needs during SAE L3 driving on a test track. Participants engaged in non-driving related tasks (NDRTs; using a smartphone or the auditory modality) had to respond to critical TORs to prevent an accident. Our results, based on qualitative methods, show that participants expect L3 vehicles to be safe and confirmed this assessment after the test track experience. Furthermore, participants preferred NDRTs using the auditory modality over the smartphone to maintain situation awareness. Our study indicates that drivers may behave responsibly in L3 vehicles, provided they are supported with user interfaces that fulfill their psychological needs.

Increasing vehicle automation presents challenges as drivers of automated vehicles become more disengaged from the primary driving task, as there will still be activities that require interfaces for vehicle-passenger interactions. Windshield displays provide large-content areas supporting drivers in non-driving related tasks. This work addresses user preferences as well as task and safety aspects for 3D augmented reality (AR) windshield displays in automated driving. Participants of a user study (N = 24) were presented with two modes of content presentation (multiple content-specific windows vs. one main window), and could freely choose their preferred positions, content types, as well as size, and transparency levels for these content windows using a simulated "ideal" windshield display in a virtual reality driving simulator. We found that using one main content window resulted in better task performance and lower take-over times, however, subjective user experience was higher for the multi-window user interface. These insights help designers of in-vehicle applications to provide a rich user experience in automated vehicles.

With the future availability of highly automated vehicles (AVs), vulnerable road users (VRUs) will encounter vehicles without human operators. To compensate for the lack of eye contact, realizing communication via external human-machine interfaces (eHMIs) is planned. The adequacy of this regarding people with intellectual disabilities (IDs) is, however, still unknown. This work compares eHMI concepts by their perceived user experience (UX) for people with and without ID to evaluate the inclusiveness of current eHMI concepts. We analyzed related work and derived two representative concepts for a visual and an auditory eHMI. Subsequently, a survey of N=120 participants (64 with, 56 without ID) was performed, comparing the perceived UX of the selected eHMI concepts for visual, auditory, and combined modalities, and a baseline without eHMI using videos of simulations. We then had them assessed using the modified user experience questionnaire - short (UEQ-S). We found that auditory eHMIs performed worse than visual or multi-modal ones, and multi-modal concepts performed worse for people with ID in terms of pragmatic quality and crossing decisions. Our insights can be taken by both industry and academia, to make AVs more inclusive.

Current systems for driver drowsiness detection often use driving-related parameters. Automated driving reduces the availability of these parameters. Techniques based on physiological signals seem to be a promising alternative. However, in a dynamic driving environment, only non- or minimal intrusive methods are accepted. In this work, a driver drowsiness detection system based on a smart wearable is proposed. A mobile application with an integrated machine learning classifier processes heart rate from a consumer-grade wearable. A simulator study (N=30) with two age groups (20-25, 65-70 years) was conducted to evaluate acceptance and performance of the system. Acceptance evaluation resulted in high acceptance in both age groups. Older participants showed higher attitudes and intentions towards using the system compared to younger participants. Overall detection accuracy of 82.72% was achieved. The proposed system offers new options for in-vehicle human-machine interfaces, especially for driver drowsiness detection in the lower levels of automated driving.

Automated Vehicles (AVs) are expected to radically disrupt our mobility. Whereas much is speculated about how AVs will actually be implemented in the future, we argue that their advent should be taken as an opportunity to enhance all people’s mobility and improve their lives. Thus, it is important to focus on both the environment and the needs of target groups that have not been sufficiently considered in the past. In this paper, we present the findings from a qualitative study (N=11) of public attitude on hypothetical implementation scenarios for AVs. Our results indicate that people are aware of the benefits of shared mobility for the environment and society, and are generally open to using it. However, 1) emotional factors mitigate this openness and 2) security concerns were expressed by female participants. We recommend that identified concerns must be addressed to allow AVs fully exploiting their benefits for society and environment.

Even though the safeguarding of automated driving functions is very present in automotive research, semiautomated and manually controlled systems will continue to be predominant in the next decade. Still, automation features such as driver assistance systems are becoming more prevalent and can operate more intuitively with the driver in a closed loop. Efficient interaction with the driver in partially automated systems can further improve road safety in the meantime. In this context, assessing the driver's perception of nearby traffic is crucial to making driver assistance safer and more collaborative. We propose a data-driven method to detect traffic-objects a driver is visually observing in the headway and side traffic using a multilabel neural-network as a classifier. This functionality is necessary to distinguish well controlled anticipatory driving from missing perception in partially automated driving. This could be an early reaction to decelerating vehicles ahead, even before a critical situation arises, or verification of the driver's perception of a cut-in. We validate the method using a data-driven ground truth from wearable eye trackers and automatically generated labels. Additional information comes from cameras installed in the vehicles to monitor the driver and traffic, as well as from measurement equipment that records CAN bus data. The proposed method achieves a Hamming accuracy of 69%, outperforming previous geometric models.

Trust is important in determining how drivers interact with automated vehicles. Overtrust has contributed to fatal accidents; and distrust can hinder successful adoption of this technology. However, existing studies on trust are often hard to compare, given the complexity of the construct and the absence of standardized measures. Further, existing trust scales often do not consider its multi-dimensionality. Another challenge is that driving is strongly context- and situation-dependent. We present the Situational Trust Scale for Automated Driving, a short questionnaire to assess different aspects of situational trust, based on the trust model proposed by Hoff and Bashir. We evaluated the scale using an online study in the US and Germany (N=303), where participants faced different videos of an automated vehicle. Results confirm the existence of situational factors as components of trust, and support the scale being a valid measure of situational trust in this automated driving context.

With increasing automation, vehicles could soon become mobile work- and living spaces, but traditional user interfaces (UIs) are not designed for this domain. We argue that high levels of productivity and user experience will only be achieved in SAE L3 automated vehicles if UIs are modified for non-driving related tasks. As controls might be far away (up to 2 meters), we suggest to use gaze-based interaction with windshield displays. In this work, we investigate the effect of different dwell times and feedback designs (circular and linear progress indicators) on user preference, task performance and error rates. Results from a user study conducted in a virtual reality driving simulator (N = 24) highlight that circular feedback animations around the viewpoint are preferred for gaze input. We conclude this work by pointing out the potential of gaze-based interactions with windshield displays for future SAE L3 vehicles.

While automated vehicles are supposed to become places for purposes beyond transportation, motion sickness is still a largely unsolved issue that may be critical for this transformation. Due to its previously shown positive impact on the gastric and central nervous system, we hypothesize that olfaction (in particular the scents of lavender and ginger) may be able to reduce motion sickness symptoms in a non-invasive manner. We investigate the effects of these scents on the driver-passenger in chauffeured drives in a test track study with a reading-span non-driving related task. Evaluation of self-rated (Simulator Sickness Questionnaire, UX Curves) and physiologically measured motion sickness (Electrogastrography, Electrocardiography), and observations are presented and discussed. Results indicate that the issued scents were detrimental to the well-being of participants in the comparisons between post-task (baseline, scented) and pre-test measurements, with symptoms in the lavender-scented group being perceived as slightly less harsh than in the ginger-scented group.

Recent research indicates that transparent information on the behavior of automated vehicles positively affects trust, but how such feedback should be composed and if user trust influences the amount of desired feedback is relatively unexplored. Consequently, we conducted an interview study with (N=56) participants, who were presented different videos of an automated vehicle from the ego-perspective. Subjects rated their trust in the vehicle in these situations and could arbitrarily select objects in the driving environment that should be included in augmented reality feedback systems, so that they are able to trust the vehicle and understand its actions. The results show an inverse correlation between situational trust and participants’ desire for feedback and further reveal reasons why certain objects should be included in feedback systems. The study also highlights the need for more adaptive in-vehicle interfaces for trust calibration and outlines necessary steps for automatically generating feedback in the future.

Driver drowsiness is a major cause of traffic accidents. Automated driving might counteract this problem, but in the lower automation levels, the driver is still responsible as a fallback. The impact of driver drowsiness on automated driving under realistic conditions is, however, currently unknown. This work contributes to risk and hazard assessment with a field study comparing manual to level-2 automated driving. The experiment was conducted on a test track using an instrumented vehicle. Results (N=30) show that in automated driving, driver drowsiness (self-rated with the Karolinska Sleepiness Scale) is significantly higher as compared to manual driving. Age group (20--25, 65--70 years) and driving time have a strong impact on the self-ratings. Additionally, to subjective measures, a correlation was also identified between drowsiness and heart rate data. The gained knowledge can be useful for the development of drowsiness detection systems and dynamic configuration of driver-vehicle interfaces based on user state.

Today’s developments in the automotive industry are moving towards automated driving. At the highest levels, the driver becomes the passenger, which presents a new challenge for human-computer interaction. People not only have to trust in the automated system but are also confronted with increased complexity, as it is often not clear what the automated vehicle is about to do. An ambient light display is one way to give the driver a clearer picture of the car’s intentions and to keep complexity low. We have examined the impact on trust and user experience in more detail using two concepts for an ambient light display. One design provides information about detected potential conflicts in the current trajectory. The other design also highlights the future trajectory. We implemented both concepts as virtual light bars at the bottom of the screens. We evaluated them in a fixed-base driving simulator with 18 participants against each other and a baseline condition without additional information. Our scenario is a fully automated journey (SAE Level 5) through a German town. Although the two concepts do not differ much from each other, only the display showing both information – possible conflicts and future driving route – provided a clear added value for the users.

With the increasing knowledge advancements and availability of mixed reality (MR), the number of its purposes and applications in vehicles rises. Mixed reality may help to increase road safety, support more immersive (non-) driving related activities, and finally enhance driving and passenger experience. MR may also be the enabling technology to increase trust and acceptance in automated vehicles and therefore help on the transition towards automated driving. Further, automated driving extends use cases of virtual reality and other immersive technologies. However, there are still a number of challenges with the use of mixed reality when applied in vehicles, and also several human factors issues need to be solved. This paper aims at presenting a research agenda for using mixed reality technology for automotive user interfaces (UIs) by identifying opportunities and challenges.

Windshield displays (WSDs) are a promising new technology to augment the entire windscreen with additional information about vehicle state, highlight critical objects in the surrounding, or use the screen as replacement for a conventional display. Typically, augmentation is provided in a screen-fixed manner as overlay on the windscreen. However, it is unclear to date if this is optimal in terms of usability/UX and further there is no golden standard suggesting where to place and how to manage content on such large displays in a vehicular environment. In this work, we propose ”StickyWSD” – a world-fixed positioning strategy – and evaluate its impact on quantitative and qualitative measures compared to screen-fixed positioning. Results from a user study conducted in a virtual reality driving simulator (N = 23) suggest that the dynamic world-fixed positioning technique shows increased task performance and lowered error rates as well as faster take-over times. Subjective evaluations show no clear preferences between both conditions. We propose to display text content on the WSD in world-fixed modality but further studies on context- and content-awareness are required.

Drowsiness is a major cause of fatal traffic accidents. Automated driving is intended to counteract this problem, but in the lower levels of automation, the driver is still responsible as a fallback. Current drowsiness detection methods are often based on driving behavior parameters. Since the automation of the driving task reduces the scope of use of these parameters, alternatives are necessary. Particularly methods that include physiological signals seem to be auspicious. However, inside a vehicle, only non- or minimally intrusive measurement techniques are allowed. In this work, a machine learning-based driver drowsiness detection method is presented applied solely to physiological data from non-intrusive wrist-worn smart wearable devices. A user study (N=30) on a test track with SAE level-2 automated driving was conducted where heart rate data with three commercially available fitness trackers were recorded. Different machine learning models were tested in a 2- and 3-level classification of drowsiness. For both cases and with all tested devices, high accuracies (>90%) could be achieved. The proposed methodology provides new options for the development of intelligent driver-vehicle interaction concepts and interfaces, especially for driver drowsiness detection on the way to fully automating the driving task.

Observing surrounding traffic is one of the primary responsibilities of a driver and research has shown that a proportion of accidents could have been prevented if the driver had reacted faster to, e.g., braking vehicles. One reason for slowed reaction is missing visual attention, used, for instance, to set up the navigation system or operate the multimedia system. This reduces driver's ability to perceive other vehicles. In this work, we propose a method to quantify the effect of missing perception of leading vehicles. We extend Time-To-Collision (TTC) to a probabilistic TTC (PTTC) model considering the (additional) perception of the driver instead of the vehicle's sensor system only. The underlying theory is that whenever the driver's gaze is off the road, he cannot observe whether vehicles ahead apply the brake. The position of leading vehicles is modeled probabilistically, according to the gaze-off-road time. Results from a naturalistic driving study (vehicle sensors, driver's face) indicate, e.g., a reduced environmental perception during lane change situations, but with little impact on road safety. Data further show that in situations with high TTC, drivers tend to operate controls in the center console. PTTC will now be used as a more realistic, driver related, TTC variant.

The increasing number and complexity of advanced driver assistance systems (ADAS) pave the way for fully automated driving. Automated vehicles are said to increase road safety and prevent human-made (fatal) accidents, amongst others. In the lower levels of automation, however, the driver is still responsible as a fallback authority. As a consequence, systems that reliably monitor the driver's state, especially the risk factor drowsiness, become increasingly essential to ensure the driver's ability to take over control from the vehicle on time. In research, the use of supervised machine learning for drowsiness detection is the prevalent method. As the ground truth for drowsiness is both application- and user-dependent, and no golden standard exists for its definition, measures are usually applied in the form of observer ratings. Also, in this work, observer ratings were investigated with regard to the required level of detail/complexity. To this end, video data, recorded within a simulator study (N = 30) comprised of each 45-minute manual and automated driving sessions, were evaluated by trained raters. Correlation analysis results show that - depending on the number of drowsiness levels - a comparable ground truth can be generated by reducing the rating frequency and thus the rating complexity by a factor of five. The knowledge gained can be used in future studies in this research area, the collection of a reliable and valid ground truth of drowsiness, as well as for improving the process in developing interactive drowsiness detection systems.

Automated vehicles (AVs) introduce a new challenge to human-computer interaction (HCI): pedestrians are no longer able to communicate with human drivers. Hence, new HCI designs need to fill this gap. This work presents the implementation and comparison of different interaction concepts in virtual reality (VR). They were derived after an analysis of 28 works from research and industry, which were classified into five groups according to their complexity and the type of communication. We implemented one concept per group for a within-subject experiment in VR. For each concept, we varied if the AV is going to stop and how early it starts to activate its display. We observed effects on safety, trust, and user experience. A good concept displays information on the street, uses unambiguous signals (e.g., green lights) and has high visibility. Additional feedback, such as continuously showing the recognized pedestrian's location, seem to be unnecessary and may irritate.

In the domain of automated driving, numerous (technological) problems were solved in recent years, but still many limitations are around that could eventually prevent the deployment of automated driving systems (ADS) beyond SAE level 3. A remote operating fallback authority might be a promising solution. In order for teleoperation to function reliably and universal, it will make use of existing infrastructure, such as cellular networks. Unfortunately, cellular networks might suffer from variable performance. In this work, we investigate the effects of latency on task performance and perceived workload for different driving scenarios. Results from a simulator study (N=28) suggest that latency has negative influence on driving performance and subjective factors and led to a decreased confidence in Teleoperated Driving during the study. A latency of about 300 ms already led to a deteriorated driving performance, whereas variable latency did not consequently deteriorate driving performance.

With increasing automation, vehicles could soon become "mobile offices" but traditional user interfaces (UIs) for office work are not optimized for this domain. We hypothesize that productive work will only be feasible in SAE level 3 automated vehicles if UIs are adapted to (A) the operational design domain, and (B) driver-workers' capabilities. Consequently, we studied adapted interfaces for a typical office task (text-comprehension) by varying display modality (heads-up reading vs. auditory listening), as well as UI behavior in conjunction with take-over situations (attention-awareness vs. no attention-awareness). Self-ratings, physiological indicators, and objective performance measures in a driving simulator study (N = 32) allowed to derive implications for a mobile workspace automated vehicle. Results highlight that heads-up displays promote sequential multi-tasking and thereby reduce workload and improve productivity in comparison to auditory displays, which were still more attractive to users. Attention-awareness led to reduced stress but later driving reactions, consequently requiring further investigations.

In the evolution of technical systems, freedom from error and early adoption plays a major role for market success and to maintain competitiveness. In the case of automated driving, we see that faulty systems are put into operation and users trust these systems, often without any restrictions. Trust and use are often associated with users' experience of the driver-vehicle interfaces and interior design. In this work, we present the results of our investigations on factors that influence the perception of automated driving. In a simulator study, N=48 participants had to drive a SAE level 2 vehicle with either perfect or faulty driving function. As a secondary activity, participants had to solve tasks on an infotainment system with varying aesthetics and usability (2x2). Results reveal that the interaction of conditions significantly influences trust and UX of the vehicle system. Our conclusion is that all aspects of vehicle design cumulate to system and trust perception.

Technology acceptance is a critical factor influencing the adoption of automated vehicles. Consequently, manufacturers feel obliged to design automated driving systems in a way to account for negative effects of automation on user experience. Recent publications confirm that full automation will potentially lack in the satisfaction of important user needs. To counteract, the adoption of Intelligent User Interfaces (IUIs) could play an important role. In this work, we focus on the evaluation of the impact of scenario type (represented by variations of road type and traffic volume) on the fulfillment of psychological needs. Results of a qualitative study (N=30) show that the scenario has a high impact on how users perceive the automation. Based on this, we discuss the potential of adaptive IUIs in the context of automated driving. In detail, we look at the aspects trust, acceptance, and user experience and its impact on IUIs in different driving situations.

Overreliance in technology is safety-critical and it is assumed that this could have been a main cause of severe accidents with automated vehicles. To ease the complex task of permanently monitoring vehicle behavior in the driving environment, researchers have proposed to implement reliability/uncertainty displays. Such displays allow to estimate whether or not an upcoming intervention is likely. However, presenting uncertainty just adds more visual workload on drivers, who might also be engaged in secondary tasks. We suggest to use olfactory displays as a potential solution to communicate system uncertainty and conducted a user study (N=25) in a high-fidelity driving simulator. Results of the experiment (conditions: no reliability display, purely visual reliability display, and visual-olfactory reliability display) comping both objective (task performance) and subjective (technology acceptance model, trust scales, semi-structured interviews) measures suggest that olfactory notifications could become a valuable extension for calibrating trust in automated vehicles.

Due to the increasing complexity of current vehicle functions and their interaction, the effect of a single function can change. Safety and comfort functions are one of the most important topics in vehicle development. In order to quantify the influence of Advanced Driver Assistant Systems (ADAS) on the vehicle guidance, which are the vehicle control inputs of the driver, we present an assessment methodology based on Field Operational Tests (FOT) and Naturalistic Driving Studies (NDS) using vehicles of different generations and consider country-specific traffic composition. We identify situations of interest by characteristics of driver guidance and the responding system feedback. Therefore, we study the usage and the effect of longitudinal guidance systems like Dynamic Cruise Control (DCC) and Adaptive Cruise Control (ACC) on driver guidance. As an indicator, we use temporal and spatial reserves in the interaction with other road users and the influence on lateral guidance. Results indicate that the general distance-behavior with and without DCC/ACC differs. System usage and common configurations differ, and a marginal effect on lateral guidance is noticeable.

As automated vehicles increase on public roads, research on communication with vulnerable road users (VRUs) becomes increasingly important. Recently, numerous solutions to tackle this problem have been presented. However, it is not clear whether such concepts fit the needs and requirements of future users. The aim of this work is to identify the requirements and expectations of users after real-life exposure with an automated vehicle. We conducted a field study (N=32) in a small town where an automated vehicle is in regular operation and collected both subjective (surveys, semi-structured interviews) and objective (video analysis) data. Results suggest that VRUs prefer rather simple forms of communication derived from well-established concepts like traffic signs, horns or indicators. Furthermore, we could identify important scenarios that have yet not been addressed, and our findings are in particular useful for the provision of automated vehicles in shared spaces.

Drivers' ability to engage in non-driving related tasks (NDRTs) is a promise of automated driving and office work an important use-case therein. We claim that potentially negative effects on road safety need to be compensated by adaptive in-vehicle interfaces that support NDRTs by design. In this paper, we present the conception and evaluation of a novel dual-task interface that is based on findings from both automotive research and office ergonomics. The steering wheel prototype aims at enabling productivity while retaining or even improving safety in Take-Over situations. In a driving simulator study with N=22 participants, we tested the prototype in two variations, haptic vs. touchscreen keyboard design, against the baseline “notebook on the lap”. Results show significant improvements regarding gaze reaction, typing performance, and subjective ratings of the haptic as compared to the touch keyboard. Most promisingly, Take-Over reaction time decreased by 40 percent when using the haptic prototype instead of the conventional notebook. Based on our findings, we recommend the use of adaptive input devices to assist the driver and prevent mode confusion. We further suggest avoiding the use of tablets in L3 driving - even when integrated into the steering wheel - in order to meet safety requirements.

Windshield displays (WSDs) are a promising new technology to augment the entire windscreen with additional information about vehicle state, highlight critical objects in the surrounding, or serve as replacement for conventional displays. Typically, augmentation is provided in a screen-fixed manner as overlay on the windscreen. However, it is unclear to date if this is optimal in terms of usability/UX. In this work, we propose ”StickyWSD” – a world-fixed positioning strategy – and evaluate its impact on quantitative measures compared to screen-fixed positioning. Results from a user study conducted in a virtual reality driving simulator (N = 23) suggest that the dynamic world-fixed positioning technique shows increased performance and lowered error rates as well as take-over times. We conclude that the ”StickyWSD” approach offers lot of potential for WSDs that should be researched further.

The prevalence of Automated Driving Systems (ADS) is expected to open up many possibilities for different user groups with individual needs and challenges. Former secondary/tertiary tasks can become primary tasks, and driving with all its interactions and responsibilities steps back or disappears at all. At higher levels of AD it is expected that the elderly could maintain or regain individual mobility, thus, play a major role for future markets. To understand individual mindsets concerning technology acceptance and user needs we conducted an explorative interview study (N=27). In a simulated automated driving environment, driving experience over time was compared across three age groups (elderly people >65, younger adults <30, younger adults <30 with age simulation suite), utilizing the STAM model for content analysis. Results of the age-comparison indicate no major differences in the general technology acceptance, however, fine-grained analysis revealed interesting differences in participants' perceptions concerning UX design requirements.

Drowsiness is a main cause of serious traffic accidents, and problematic within the ongoing automation of the driving task. Several approaches for drowsiness detection have been published and are in operation in production cars for manual driving. To assess differences in the development of drowsiness between manual and automated driving, and to further investigate the potential of subjective ratings, we conducted a driving simulator study (N=30). The self-assessment was based on the Karolinska Sleepiness Scale (KSS), during and after driving. Furthermore, we examined the impact of travel time and driver age (20-25, 65-70 years). Results confirm that driving mode and travel time have a significant effect on the development of drowsiness. In both age groups, self-ratings were higher for automated driving and particularly by younger subjects. All subjects estimated themselves drowsier during driving. The gained knowledge can be helpful for the development of future driver-vehicle interfaces in driver drowsiness detection.

A major promise of automated vehicles is to render it possible for drivers to engage in nondriving related tasks, a setting where the execution pattern will switch from concurrent to sequential multitasking. To allow drivers to safely and efficiently switch between multiple activities (including vehicle control in case of Take-Over situations), we postulate that future vehicles should incorporate capabilities of attentive user interfaces, that precisely plan the timing of interruptions based on driver availability. We propose an attention aware system that issues Take-Over Requests (1) at emerging task boundaries and (2) directly on consumer devices such as smartphones or tablets. Results of a driving simulator study (N=18), where we evaluated objective, physiological, and subjective measurements, confirm our assumption: attention aware Take-Over Requests have the potential to reduce stress, increase Take-Over performance, and can further raise user acceptance/trust. Consequently, we emphasize to implement attentive user interfaces in future vehicles.

User experience (UX) evaluation is a growing field with diverse approaches. To understand the development since previous meta-review efforts, we conducted a state-of-the-art review of UX evaluation techniques with special attention to the triangulation between methods. We systematically selected and analyzed 100 papers from recent years and while we found an increase of relevant UX studies, we also saw a remaining overlap with pure usability evaluations. Positive trends include an increasing percentage of field rather than lab studies and a tendency to combine several methods in UX studies. Triangulation was applied in more than two thirds of the studies, and the most common method combination was questionnaires and interviews. Based on our analysis, we derive common patterns for triangulation in UX evaluation efforts. A critical discussion about existing approaches should help to obtain stronger results, especially when evaluating new technologies.

Automated driving eliminates the permanent need for vehicle control and allows to engage in non-driving related tasks. As literature identifies office work as one potential activity, we estimate that advanced input devices will shortly appear in automated vehicles. To address this matter, we mounted a keyboard on the steering wheel, aiming to provide an exemplary safe and productive working environment. In a driving simulator study (n=20), we evaluated two feedback mechanisms (heads-up augmentation on a windshield, conventional heads-down display) and assessed both typing effort and driving performance in handover situations. Results indicate that the windshield alternative positively influences handovers, while heads-down feedback results in better typing performance. Text difficulty (two levels) showed no significant impact on handover time. We conclude that for a widespread acceptance of specialized interfaces for automated vehicles, a balance between safety aspects and productivity must be found in order to attract customers while retaining driving safety.

A prerequisite to foster proliferation of automated driving is common system acceptance. However, different users groups (novice, enthusiasts) decline automation, which could be, in turn, problematic for a successful market launch. We see a feasible solution in the combination of the advantages of manual (autonomy) and automated (increased safety) driving. Hence, we've developed the Hotzenplotz interface, combining possibility-driven design with psychological user needs. A simulator study (N=30) was carried-out to assess user experience with subjective criteria (Need Scale, PANAS/-X, HEMA, AttrakDiff) and quantitative measures (driving behavior, HR/HRV) in different conditions. Our results confirm that pure AD is significantly less able to satisfy user needs compared to manual driving and make people feeling bored/out of control. In contrast, the Hotzenplotz interface has proven to reduce the negative effects of AD. Our implication is that drivers should be provided with different control options to secure acceptance and avoid deskilling.

Safety systems in automated vehicles use surround sensors to perceive their local environment. In contrast to radar sensors, laser scanners provide precise spatial information, which can be used to identify critical traffic situations and trigger reversible or irreversible safety systems. As a consequence, already small errors in sensor data measurements could lead to severe accidents. The performance of surround sensors depends on the ambient atmosphere and weather condition. It is known that rain and fog have negative influence and leads to signal degradation. In winter time or cold climate, the ambient air temperature requires less moisture to attain high relative humidity or to become saturated. Therefore, the hot exhaust gases of vehicles are condensed instantly and can be seen as a distinct mass of fog. To the best of our knowledge, nobody has investigated effects of exhaust gases at low temperatures on sensor quality so far. Based on static and dynamic tests at different temperatures, we have shown that exhaust gases are visible for laser scanners and lead to degraded performance. The results can be used to assign the priorities of sensors in sensor data fusion processes or to develop novel filter algorithms for low temperature situations.

Automated vehicles, but also safety or driver assistance systems for manually driven cars, will soon face situations where they have to choose between several options with negative or even lethal outcome for the one or the other party. Experimental ethics is an approach to evaluate expectations humans put into the morality of digital agents. With this work we present a personalized abstraction of the “Trolley Problem” evaluated in a driving simulator. The aim of the study was to assess drivers' individual attitudes in ethical decisions and derive common knowledge about how to solve such situations. In contrast to previous work, the study at hand looks at the problem from a holistic point of view, including uncertainty and accident risk (presented to subjects as their probability to survive). Furthermore, subjective scales and semi-structured interviews to determine subjects' justifications for ethical decisions complemented the setting. Our results (n=40) suggest that most drivers want their vehicles to act in an utilitarian way and opt for the more severe collision — even when their own probability to survive is substantially low. In addition, age and size of the injured party have a significant effect on the results. Qualitative data (interviews) indicate that the justification, in particular for decisions with the same outcome, strongly differs as most people have embodied their own moral concepts.

The rise of autonomous and fully autonomous vehicles requires a closer examination of how people will interact with them. In this workshop, we especially address two areas: (1) What can we learn from mobile HCI for the interaction design between human drivers and the autonomous vehicle? (2) What opportunities for mobile HCI arise due to new freedoms for drivers, when they also take on the role of passengers in autonomous vehicles? The workshop will seek to further develop the challenges involved and/or to suggest early solutions through the use of a highly participative format.

Technological advances will soon make it possible for automated systems (such as vehicles or search and rescue drones) to take over tasks that have been performed by humans. Still, it will be humans that interact with these systems - relying on the system ('s decisions) will require trust in the robot/machine and its algorithms. Trust research has a long history. One dimension of trust, ethical or morally acceptable decisions, has not received much attention so far. Humans are continuously faced with ethical decisions, reached based on a personal value system and intuition. In order for people to be able to trust a system, it must have widely accepted ethical capabilities. Although some studies indicate that people prefer utilitarian decisions in critical situations, e.g. when a decision requires to favor one person over another, this approach would violate laws and international human rights as individuals must not be ranked or classified by personal characteristics. One solution to this dilemma would be to make decisions by chance - but what about acceptance by system users? To find out if randomized decisions are accepted by humans in morally ambiguous situations, we conducted an online survey where subjects had to rate their personal attitudes toward decisions of moral algorithms in different scenarios. Our results (n=330) show that, despite slightly more respondents state preferring decisions based on ethical rules, randomization is perceived to be most just and morally right and thus may drive decisions in case other objective parameters equate.

Intelligent vehicles use surround sensors such as radar, lidar and camera to perceive their local environment. The generated information are processed by a control unit to identify critical traffic situations such as rear-end collisions and trigger reversible or irreversible safety systems. Incorrect measurements can result in accidents with fatal consequences. Therefore, high reliability and accuracy is a mandatory requirement. The performance of surround sensors depend on the ambient atmosphere and weather conditions. It is known that fog has a negative influence on wave propagation especially in the visible range. Hence, surround sensors must be tested under realistic conditions. To cope with this problem, this paper presents a novel setup together with a test methodology to investigate the influence of fog on automotive radar, lidar and camera sensors. Based on static indoor tests, we have shown that the measurements are consistent with literature and enable reproducible testing.

It is expected that automated vehicles (AVs) will only be used when customers believe them to be safe, trustworthy, and match their personal driving style. As AVs are not very common today, most previous studies on trust, user experience, or acceptance measures in automated driving are based on qualitative measures. The approach followed in this work is different, as we compared the direct effect of human drivers versus automated driving systems (ADSs) on the front seat passenger. In a driving simulator study (N=48), subjects had either to ride with an ADS, a male, or a female driver. Driving scenarios were the same for all subjects. Findings from quantitative measurements (HRV, face tracking) and qualitative pre-/post study surveys and interviews suggest that there are no significant differences between the passenger groups. Our conclusion is, that passengers are already inclined to accept ADS and that the market is ready for AVs.

Steering a vehicle has become a challenging task and this is underpinned by the fact that more than ninety percent of vehicle accidents are caused by driver errors. Cautionary, not the classical driving errors lead to this number, but accidents caused by distracted drivers reaching or exceeding their cognitive limits. To address this problem, i.e., to mitigate driving problems caused by excessive information, we propose to induce a non-conscious behavioral change in drivers by employing subliminal techniques. Within a driving simulator study we have demonstrated the feasibility of the approach to support drivers with added information without dissipating available attention resources. In a Lane Change Task (LCT) similar to ISO 26022-2010 we exposed drivers to sequences of briefly flashed visual stimuli (subliminally flashed lane change requests) to change their steering behavior. The results of the study, while mainly not statistically significant, still give support to our hypotheses that there are positive differences between control group (no subliminal messages or negative primes) and test group (exposed to positive subliminal cues). More research and experimentation is needed to improve on the perception of information priming, but we are confident that subliminally driven interfaces will find their way as additional information provider into the cars of the future.

In this letter, we detail a modular approach for measuring the secondary physical and emotional effects of ambient intelligence (AmI) technology in traffic. Using the case of merges on to a highway, we assess the results of a system that advises the driver to change early to a lane on the left to create space for merging cars downstream (tested using a cellular automata simulation). The indirect impact of the system downstream, namely how the remaining lane changes from the merge lane to the innermost lane proceed, is then evaluated using a time-discrete, space-continuous microscopic traffic simulation tool. This yields detailed results concerning driver interactions that can also be used to derive an estimate of driver anger in the situation. We have used real geographic, traffic and psychological data to test the system, and different models are used to accomplish various tasks. The approach yields (surprisingly) negative results concerning the indirect emotional impact of this AmI intervention which may be due to the nature of the lane changing model used and the chosen parameters. We argue that such an approach is also applicable to similar types of systems, where different data and model types are suited to different scenario elements.

Driven by technological advancements, gesture interfaces have recently found their way into vehicular prototypes of various kind. Unfortunately, their application is less than perfect and detailed information about preferred gesture execution regions, spatial extent, and time behavior are not available yet. Providing car (interior) manufacturer with gesture characteristics would allow them to design future in-vehicle concepts in a way to not interfere with gestural interaction. To tackle the problem, this research aims as preliminary work for a later standardization of the diverse properties of gestures and gesture classes similarly to what is already standardized in norms such as ISO 3958/4040 for placement and reachability of traditional controls and indicators. We have set up a real driving experiment recording trajectories and time behavior of gestures related to car and media control tasks. Data evaluation reveals that most of the subjects perform gestures in the same region (bounded by a "triangle" steering wheel, rear mirror, and gearshift) and with similar spatial extent (on average below 2 sec.). The generated density plots can be further used for an initial discussion about gesture execution in the passenger compartment. The final aim is to propose a new standard on permitted gesture properties (time, space) in the car.

Driving a modern vehicle is a complex, cognitive demanding task involving concentrated observation of the road, roadside, car status, information displays of assistance systems, etc. Drivers are conscious about this overabundance of information, nevertheless, they are operating tertiary controls, talking on the phone, smoking cigarettes, having lunch, reading maps or meeting agendas, or working on their computer. As a consequence -- caused by visual/manual/cognitive demand and limited multitasking capabilities-- precarious driving situations are created. Solutions are rare but badly needed to prevent imminent danger on the roads. To explore the potential of thermal imaging to infer mental conditions of the driver in an unobtrusive manner, and to use this information to automatically react to a detected risky state, we have developed the "FaceLight" prototype and performed a lab-based driving simulator study to evaluate the interface under conditions of varying workload. With "FaceLight" the driver can be interpreted as sort of signal light, with a 'red face' (hot surface temperature) standing for high stress or cognitive demand while a 'green face' (cooler temperature) equals to a more relaxed, stress-free mental state. Initial results have revealed that this technology has potential to capture shifts in the mental state of an individual in an inattentive manner, but highlighted also that a lot of influencing factors still need to be incorporated to reliably recognize a specific state solely based on facial skin temperature.

Vehicle area network (VAN) communications and related services are getting more pervasive [1]. However, even though user-centered design has been emphasized, VAN services have often been developed through a technology-driven approach. This paper presents cross-cultural survey results on VAN services in three different countries: Austria, USA, and South Korea. The current research compared the state-of-the-art of drivers' current in-vehicle technology use and investigated their needs and wants for plausible new services in the near future. Further, we validated our next generation in-vehicle interface concepts stemming from our previous participatory design process [2]. Results showed clear differences between Austrians vs. Americans and Koreans. Even though Koreans and Americans in our survey were older than Austrians, they seemed more open-minded to VAN services (e.g., social networks in car, V2V services, in-vehicle agent, etc) in general and rated them more positively. Through these cross-cultural needs analyses of end users, designers and practitioners are expected to gain insights into developing a standardized service across cultures as well as culturally tuned in-vehicle interfaces. Moreover, we hope that this initial international collaboration can serve as a good test bed for future research and hope to expand our consortium with more colleagues in the AutomotiveUI community for further cross-cultural studies.

Human-computer confluence (HCC) aims at investigating how the emerging symbiotic relation between humans and computing devices can enable new forms of sensing, perception, interaction, and comprehension. Latest advancements in information and communication technology have been the key enabler that this vision actually became reality. The concept of driver-vehicle confluence is understood as a specific instantiation of HCC, and its main objective is to understand the symbiosis between drivers, cars, and the infrastructure within an arbitrarily large region of interest. This covers not only information sharing within a collective of cars, for example about an oil spill on the road -- more important is to reason about driver states, learn about social connections and emotional influences, and forecast driver action or vehicle movement. All these can be achieved by modeling driver behavior, studying distributed negotiation processes, performing driving studies and simulations, and relating the results back to observations made in reality.

Experienced poker players have the ability to suppress and hide emotions and reactions to avoid providing information about the quality of the dealt private cards and the own probability of winning to the adversaries. Besides unswayable luck and bravery, bluffing is the only skill that could massively improve the own chance of winning. This paper investigates whether a subliminal reaction in terms of changing facial surface skin temperature can be linked to the quality of the dealt private cards (i.e., the probability of winning the actual hand). Therefore, a dataset containing thermal imaging has been recorded during a No Limit Texas Hold'Em Poker tournament-session with six players in total and two players being observed with a high-resolution thermal imaging camera and manual provision of their dealt private cards as ground-truth. Preliminary results show that the facial skin temperature varies massively (±1.2°C), which constitutes the research hypothesis that a significant change in the surface face skin temperature can be linked to the quality of the dealt cards in terms of winning chance for an actually played hand.

Following laws and provisions passed on the national and international level, the most relevant goal of future traffic and vehicular interfaces is to increase road safety. To alleviate the cognitive load associated with the interaction with the variety of emerging information and assistance systems in the car, subliminal stimulation is assumed to be a promising technique. To assess the potential of subliminal cues that could be used as their interaction means in future vehicles, we have organized a workshop within the frame of the automotive user interfaces conference (AutoUI 2011) to discuss this topic in a group of experts. This paper summarizes the findings from that workshop and should give researchers a starting point for their own activities in the field by indicating sort of grand research challenges and most critical issues. In particular, the goal of this summary article is to make this challenging research field more 'tangible' for researchers working in a range of disciplines, such as engineering, neuroscience, computer science, and psychophysiology. While currently discussed in the automotive domain only, the principles, research questions, and findings could immediately (and easily) be transferred to and adopted in other research fields. Interaction based on subliminal techniques can have an impact on society at large, making significant contributions toward a more natural, convenient, and even relaxing future style of interaction with any complex systems.

To cope with the rising volume of information in humancomputer interfaces, explicit and attentive interaction is more and more frequently replaced by implicit means of information exchange, supported by context-and activity-aware systems and applications. The trend of excessive information is, however, still ongoing, calling for further solutions to reduce a persons cognitive load or level of attention. Subliminal interaction techniques are considered a promising approach to deliver information to a person without causing much supplementary workload. This workshop aims at discussing the potential of subliminal perception to improve the information flow for human-computer interaction in the light of the fact that, up to now, the results have been mixed. One group of researchers has provided evidence that subliminal stimulation works, but the other has found that it does not, or even cannot, work. To clarify this issue, experts from various domains attending the workshop will discuss how subliminal effects can be scientifically supported or how a certain claim could be empirically refuted.

Mental overload is a problem drivers are increasingly exposed to in today's complex task of vehicle operation and is one of the causes of traffic accidents or hazards. To keep road safety high but allow for additional information to be forwarded to the driver, we propose to employ subliminal persuasion: a technique where the information is transferred below the level of conscious awareness. Thus, the driver becomes aware of the information, but his/her cognitive load is unaltered. To analyze the potential of this approach, we have designed a case study implementing an “eco-driving” strategy operating in the background. Driving economy is thereby estimated based on vehicles' mileage gathered in real time from numerous sensors in and around the car, and information is conveyed to the driver with very light, not attentively perceivable, vibration patterns originating from tactor elements integrated into the safety belt or the car seat. The main research hypothesis followed in this paper and investigated in real driving studies is that drivers would operate their vehicles more economically on vibrotactile instructions perceived inattentively, as compared with the case without any notifications. Indeed, results indicate an improvement in driving economy for segments driven with subliminal feedback compared with routes driven without assistance but not without qualifications. Statistical significance has been proven for the safety belt interface, whereas it has not been substantiated for the tactile car seat. (However, more research is needed to validate the applicability of subliminal persuasion across a wider range of driving and in-vehicle tasks.).

Following laws and provisions passed on the national and international level, the most relevant goal of future vehicular interfaces is to increase road safety. To alleviate the cognitive load associated with the interaction with the variety of emerging information and assistance systems in the car (and to increase driving performance as well), subliminal persuasion is assumed to be a promising technique to reduce the amount of information the driver must store and recall. Subliminal cues could be provided across appropriate sensory modalities, according to the specific nature of the current task, and corresponding to drivers' cognitive abilities.

Both amount as well as dynamicity of content to be displayed in a car increases steadily, forcing manufacturer to change over to customizable screens integrated in dashboard and center console instead of dozens to hundreds of individual control signals. In addition, new requirements such as Internet access in the car or web services accessible while driving invalidates rudimentary display formats. Traditional forms of interaction such as buttons or knobs are unsuitable to respond to dynamic content shown on digital screens, requesting new mechanisms for distraction-free yet effective user (driver) input. We pick up this problem by introducing a novel sensing device allowing for natural, contactless, and eyes-free operation by relating finger movements in the area of the gearshift to screen coordinates. To assess quality features of this interface two research questions were formulated, (i) that the application of such a device would allow for natural, intuitive mouse pointer control in a similar manner than traditional forms of input and (ii) that the interface is insusceptible to varying workload conditions of the driver. Results from experimentation have revealed that, with respect to the first hypothesis, proximity sensing in a two-dimensional plane is a viable approach to directly control a mouse cursor on a screen integrated into the dashboard. A generally accepted conclusion on the assumption that the index of performance of the interface does not change with varying workload (hypothesis ii) cannot be drawn. To simulate different conditions of workload a dual task signal-response setting was used.

Recent advances in wireless/mobile communication and body worn sensors, together with ambient intelligence and seamless integrated pervasive technology have paved the way for applications operating based on social signals, i.e., sensing and processing of group behavior, interpersonal relationships, or emotions. Thinking in large, it should be apparent that modeling social systems allowing to study crowd behavior emerging from individual entities' (agents') condition and/or characteristics is, in fact, a challenging task. To address the heterogeneity, analytical agent-based models (ABMs) are gaining popularity due to its capability of directly representing individual entities and their interactions, unfortunately, ABMs (in which each agent has unique behavior) are not very well suited for large populations, expressed by exponentially rising simulation time. To solve this problem, the questions (i) how does the parallel execution of such models scale with capabilities of both the machine (number of cores, cluster size, etc.) and agents (behavioral adaptation function, interaction extent, etc.) and (ii) what is, in comparison, the performance coefficient applying the approach of model execution on graphical processors (GPUs) with its different pipelining architecture, need answers. To this end, we have performed simulation runs with parameter variation on a real parallel and distributed hardware platform using Cilk as well as on a GPU employing OpenCL. Simulation efficiency for two realistic models with varying complexity on a scale of 107 agents has shown the usefulness of both approaches.

In the last decade the shift of information and communication systems from a purely system level to the social level is already observable. However, the integration of social / cognitive aspects into so-called social computing is not an easy task due to conceptual differences in domains. A Socio-Technical System (STS) is a recent term intending to differentiate between a social system mediated by natural sciences or information technology. Even if the mediation of social / cognitive aspects is "theoretically" governed by technology, the gap between "socio" and "technical" is historical and huge. Furthermore the fact that with every passing year, the technical systems become more intelligent with respect to interaction with people and their pervasiveness, a special attention should be given to modelling both social and technical components and interaction between them. For example while modelling (and simulating) an emergency situation from a public facility, the possible availability of technology at the environment (e.g. situation-aware exit signs, interactive displays, etc.) and personal (e.g. cell phones, specialized wearables etc.) level, along with its social / cognitive influence must not be overruled. To address this challenge, we have integrated cognitive decision making model abstracted from psychological, neurological and social theories of human behaviour during evacuation situations into CA based simulation. Keeping focus on a scenario in which a small population of agents is technologically assisted, some of the most interesting finding are: (i) the inclusion of a representative and authentic social behaviour model into modelling a socio-technical system essentially produces fundamental differences in methodologies, (ii) the technologically assisted agents emerge as leaders during evacuation changing the intentions of many agents within their influence (iii) even a small population of such leaders in sufficiently large population is enough to guarantee a remarkable difference, particularly improving usage of possibly under-utilized exits.

In this paper, we summarize the initial results with regard to the question to what extent driving simulators can be used to serve as cheap and easy realizable environments for simulating on-the-road behavior. The aim of these first studies was to determine whether or not it is possible to replace real driving studies with experiments and furthermore, to identify parameters and/or restrictions for a second experimental series with improved settings. We have conducted two studies comparing the driver's reaction time in real and simulated environments with the final goal to provide a universal metric describing the differences in reaction time. The events were, in the case of simulation, triggered trace-driven or, in the real driving experiment, manually activated by the experimenter and notifications were forwarded to the driver using either a visual, auditory, or haptic sensory channel. The comparison of the two studies showed that (i) both settings provide similar results for the order of average response using the three feedback modalities and (ii) the experiment using a simulator performed, for the measure of reaction time, better in the range of 13% compared to the real driving study (the reason for this result is most likely caused by the fact that driving in a real world environment is much more challenging than in a driving simulator).

A general reduction of carbon dioxide emissions is an important topic currently discussed by both society and government -- lower allowed emission values would strongly affect automotive manufacturers as road transport produces, for example, about one fifth of the CO2 emissions in the European Union. But that's not all, also the individual driver could be affected from regulatory mechanisms as it is feasible, not least due to the broad availability of wireless and information technology in cars, to demand the assembly of a "personal carbon dioxide profile" including all the emissions accumulating from operating vehicles, traveling by plane, and even from using public transport, and in succession to charge a person based on its effective CO2 consumption. One problem arising in this field is that an individual usually is not aware about his/her CO2 consumption, neither about which means of transportation produces what amount of carbon dioxide (what is the personal fraction of CO2 for a large plane with 250 passengers traveling 5, 000km?), nor how exactly to drive a vehicle economic or wasting with respect to CO2 emission or what the difference between these two extreme values (in terms of cost) is. To counteract the issue of driving economy, which is the only the driver can directly control, we propose a inattentive operating vibro-tactile notification system integrated into the car (safety belt or seating), helping the driver in his/her (i) subjective CO2 valuation and (ii) reduction of CO2 emissions while driving.

A considerable increase in road traffic has provoked a total change in the operating paradigms of vehicles, shifting vehicle handling from “just steering” towards a complex adaptation task. With the emergence of wireless communication technology, vehicle operation can now incorporate for the first time ever beside the local driver-vehicle interaction also more significant information obtained from cars in the surrounding. With this foundation it would be possible to build collectively operating driver assistance systems, negotiating the interests of all road participants in a certain area with the final goal to improve global parameters such as road throughput or traffic fluidity, also having an effect on the individual car (driver), e. g. increased travel speed, less congestions, or a reduced level of cognitive load. The question addressed with this paper is whether or not the vehicle speed can be sustained while merging onto a motorway, leading to a more harmonious integration of the merging cars into the flowing traffic on the main road. To achieve this we propose the application of ambient intelligence (AmI) technology operating on the collective behavior of all cars in the periphery of the entrance ramp. To prove our hypothesis we applied the AmI technology to a data driven, true to scale simulation model of the Madrid motorway M30, one of the most busiest roads in Spain. The comparison of simulation runs with high volume of traffic showed that technology assistance could help to increase road throughput and minimize the variance of traffic flow, but on the other side demands solutions for one of the bigger problems of data driven simulation – missing or noisy data compromising simulation results.

In order to develop complexity science based modeling, prediction and simulation methods for large scale socio-technical systems in an Ambient Intelligence (AmI) based smart environment, we propose a scenario based modeling approach. With a case study on AmI technology to support the evacuation from emergency scenarios, i.e. the Life Belt, a wearable computing systems for vibro-tactile directional guidance, we introduce the concept of model scaling from a micro to a macro level. Aligned with the scenario, we present how crowd simulation strategies encoded into a small scale simulation setup can be extended to a mixed-level simulation based on combining model aspects also coming from the large scale model. The experimental results of a real evacuation trail at a local railway station are incorporated to compare the evacuation efficiency for three strategies: (i) Potential Map, (ii) Evacuees familiarity of the exits and (iii) Exits usage optimization. A comparison with the earlier results from small scale simulation suggest that a real large scale simulation results may not be similar to that of small scale simulation due to dynamics of crowd built up and complexity of building structure.

The today mainly incorporated sensory modalities vision and hearing are most often highly charged due to an increasing number, as well as a rising complexity of diversified assistance systems in vehicles. These systems, originally developed for "supporting" the driver in its tasks, increasingly directs to operation errors caused by cognitive overload. The sense of touch - as additional interaction channel - should have the ability to release cognitive load from visual and auditory channels by adapting in a natural, intuitive and non-distracting manner. A drawback of haptics is that feedback is reliant to both driver and environmental conditions. For instance, different sizes and weights of drivers' result in a changed haptic perceptivity, and vibro-tactile feedback potentially is interpreted erroneous due to personal sitting attitudes. A vibro-tactile seat, endowed with a set of unobtrusive embedded sensors and actuators, is proposed to counter action these issues. The input channel is used for determining the occupied seating surface and, in addition, is also responsible for observing disruptive vibrations, originating in the environment. These information is employed in order to reconfigure vibro-tactile output dynamically with the aim to guarantee consistent haptic perception for any person.

Driving a vehicle is a task affected by an increasing number and a rising complexity of Driver Assistance Systems (DAS) resulting in a raised cognitive load of the driver, and in consequence to the distraction from the main activity of driving. A number of potential solutions have been proposed so far, however, although these techniques broaden the perception horizon (e. g. the introduction of the sense of touch as additional information modality or the utilization of multimodal instead of unimodal interfaces), they demand the attention of the driver too. In order to cope with the issues of workload and/or distraction, it would be essential to find a non-distracting and noninvasive solution for the emergence of information.In this work we have investigated the application of heart rate variability (HRV) analysis to electrocardiography (ECG) data for identifying driving situations of possible threat by monitoring and recording the autonomic arousal states of the driver. For verification we have collected ECG and global positioning system (GPS) data in more than 20 test journeys on two regularly driven routes during a period of two weeks.The first results have shown that an indicated difference of the arousal state of the driver for a dedicated point on a route, compared to its usual state, can be interpreted as a warning sign and used to notify the driver about this, perhaps safety critical, change. To provide evidence for this hypothesis it would be essential in the next step to conduct a large number of journeys on different times of the day, using different drivers and various roadways.

With the emergence of pervasive computing technologies into vehicles, driving has moved from an active task of steering towards an interaction or adaptation task with respect to the driver-vehicle feedback loop. Up to now vehicular interfaces have mostly been evaluated from a single-driver single-car viewpoint, however, driving is a more complex task involving beside the local interaction - the interrelationship between all the cars in a certain community of interest. The question investigated in this research work is how a vehicle's local parameters in a bulk of cars (e. g. vehicle speed, braking parameters) affect the global behavior of this system (traffic congestion, driving speed variation, throughput). To explore this, two traffic models have been developed and simulated using the NetLogo simulation environment. Simulation results have shown that the intercar distance has a direct impact on both the throughput and the mean trip time. The proactive driving approach using vibro-tactile driver notification followed in the second, advanced model achieved much better results regarding these parameters compared to the simple manual-driven case. Finally, the outcomes legitimate the implementation of a prototype, and the installation of such a technology into a large number of cars in order to provide evidence for the improved traffic flow and decreased probability of traffic accidents in real driving scenarios.

To navigate in unfamiliar places is, for obvious reasons, particularly difficult for blind people. In this research we used vibro-tactile guidance cues with the goal of allowing users to reach their destination with the most efficiency. Our hypothesis was that adding distance information should impr

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project's objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

Embedded systems technologies and advances in micro electronics have accelerated the evolution of driver assistance systems towards more driving safety, comfort, entertainment and wayfinding. With the technological and quality progress, however, also goes the growing of interaction complexity, information overload, and intricate interface designs. Assessing interaction designs for in-car assistance services is an emerging and vibrant field of research. To avoid situations of possibly fatal danger when assessing driver assistance services in real driving situations, we propose trace-driven simulation to steer the experiments with users in a automotive driving simulator. Based on our own developments of driver assistance systems involving the sense of touch, i.e. exploiting haptics as a communication channel in vehicle-to-driver interactions, we demonstrate how pre-recorded traces of driving situations can control user studies. Our experiments show, that simulated driving journeys is a viable alternative to the more hazardous "on-the-road" user studies. With respect to haptics as an additional channel of communication we find that vibro-tactile stimuli are a promising means to raise driver attention when the visual and auditive channels fail due to overload.