When driving a partially automated vehicle, maintaining situation awareness is essential for users to be better prepared to take over. A primary challenge is maintaining awareness while the user is occupied with another task without tunneling attention towards individual elements. To investigate this, we conducted an experimental study in our driving simulator (n = 20) comparing an indirect LED (light-emitting diode) visualization of relevant objects in the driver’s field of view with a combined condition of an indirect LED + direct HUD (head-up display) visualization. The participants’ situation awareness scores were higher under the combined condition. However, the scores dropped significantly for objects outside the LED + HUD visualization. We conclude that the indirect object indication is not effective in countering tunneling effects from the HUD, and neither does it provide a satisfactory trade-off when deployed on its own, i.e., without direct indication in addition.

Automated Driving (AD) has the potential to significantly reshape the transportation industry by improving safety, efficiency, and user comfort and acceptance. This research investigates driver behaviors during Take Over Requests (TORs) in automated vehicles at SAE Levels 2 and 3 using the HADRIAN Human-Machine Interface (HMI), designed to enhance driver support through real-time feedback and countdown displays. Analysis included clustering to develop distinct driving profiles based on key measurements collected through a driving simulator experiment, such as acceleration, deceleration, and speed, offering a deep understanding of driver behavior in responses to TORs. Three primary driving profiles were identified: “Passive driving and slow TOR response”, “Nervous driving and moderate TOR response”, and “Normal driving and quick TOR response”. This study also provides specific insights for each automation level, identifying profiles of driver behaviors at both SAE Levels 2 and 3. Results reveal that the nervous driving profile, although less frequent, poses significant safety implications due to higher deceleration rates and variability in speed and deceleration. Additionally, the study highlights that Non-Driving Related Tasks (NDRTs) increase the need for longer Take Over Time (TOT), with greater variability observed at higher automation levels, making accurate estimation of required TOT more challenging. The HADRIAN HMI is shown to positively impact driver performance by increasing TOT, allowing drivers more time to transition from automated to manual control comfortably. These insights can inform the design of more adaptive HMI systems, enhance real-time feedback mechanisms, and improve driver training programs to ensure safer transitions during TORs.

The use of partial driving automation is intended to increase both safety and comfort. However, the interaction with vehicle automation and the changing driving task, from steering to monitoring, can challenge the driver and compromise safety. To investigate driver-automation interaction, we conducted two field studies using two different SAE level 2 vehicles and involving a total of 132 drivers. In both studies, drivers drove on a public highway and gave insights on their trust and experiences in interacting with driving functions for longitudinal control (adaptive cruise control, ACC) and lateral control (lane-keeping assistance, LA). Participants of the second study could further experience lane-change assistance (LC). Our results confirm that trust increases with the use of automation. Comparing the driving functions, we found in the first study that drivers trusted and used ACC more than LA. Moreover, drivers were more positive about ACC and more critical about LA, but reported more interaction difficulties with the ACC. The data from the second study confirms the findings that drivers liked and trusted ACC more than LA. However, LC was liked and trusted even less than the LA. Our results indicate that drivers prefer driving functions that provide longitudinal vehicle control (ACC) to those that offer lateral vehicle control (LA). The lower preference for LC as identified in the second field study confirms our results, as LC forces the vehicle to move more sideways when changing lanes.

Supporting drivers in different levels of automation was one of the key goals in the HADRIAN project. Following the approach of a "fluid interface", i.e., an interface that considers the state of the driver, vehicle, and environment, and which uses different modalities to support the driver, a human–machine interface (HMI) was developed and compared to a baseline HMI in a simulator study (n = 39). The integrated fluid HMI aimed at supporting the driver in automated driving in SAE level 2 and 3 by providing mode relevant information, supporting the driver during take-over requests by the system, and supporting engagement in non-driving related tasks when allowed. The fluid HMI consisted of several components (head-up display, LEDs, haptic icons, sound, tablet) and featured driver monitoring and adaptive tutoring. Study results did not show significant differences between the HMIs regarding subjective measures such as user experience, usability, acceptance, or safety feeling. The various factors contributing to this conclusion are thoroughly discussed. However, objective measures in terms of eye movements and a safety analysis including driving data showed a significant benefit of the integrated fluid HMI over the baseline HMI. Participants had better mode awareness and a higher safety score with the integrated fluid HMI. Furthermore, valuable insights on how to further improve the HMI could be gained during the study.

Automated Driving (AD) technologies are transforming the mobility sector, promising enhancements in efficiency and safety as well as user experience tailored to diverse user needs. As the sector moves toward a more automated ecosystem, it's essential to recognize the varied expectations and requirements of its users. Elderly drivers, for instance, have distinct needs, from functional transportation to the aesthetic pleasure of driving. As age-associated impairments affect their driving capabilities, Advanced Driving Assistance Systems (ADAS) present an opportunity to augment their safety and confidence. Truck drivers or business people on the other hand have different sets of needs and expectations. The article emphasizes the role of User-Centered Design (UCD) in the HADRIAN project with the intent to fulfill the promise of AD. Through the iterative creation of personas, like Harold, representing the elderly, Sven, the seasoned truck driver, and Florence, the business woman, the project brings to light the requirements for AD from a user-centric focus. Such personas, backed by comprehensive research and expert insights, steer in the HADRIAN project not only the design of interfaces but influence the development of AD systems ensuring broader acceptance and inclusivity. As the landscape of urbanization and digitalization expands, coupled with the emergence of smart cities and shared mobility solutions, the integration of AD with these broader trends becomes imperative. This will require a larger ecosystem that allows to steer vehicle development processes increasingly toward the specific needs of its user groups, moving away from one-for-all vehicles. In the HADRIAN project we envisioned such an ecosystem to be able to step beyond currently prevailing vehicle development approaches and show how with a user-focused approach, the future of transportation can become a harmonious blend of safety, efficiency, and inclusivity, resonating with the real-world needs of diverse commuters.

The drivers' distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to "detect" eventual events, road traffic, and additional driving tasks that might contribute to the drivers' distraction. Therefore, the present study aimed at developing an EEG-based "Distraction index" obtained by the combination of the driver's mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver's attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers' distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers' distraction (all p < 0.01).

AbstractThe current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline “state-of-the-art” HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations.

Vehicle automation aims to improve safety and comfort, but using partial driving automation safely can challenge drivers. To investigate how drivers trust and rely on the automation, we conducted a field study with 100 participants. While driving on a public highway, they rated their trust and commented on their interaction with the automation systems for longitudinal (adaptive cruise control, ACC) and lateral control (lane-keeping assistance, LA). Our results show that trust in partial driving automation increases continuously by using it. Comparing the automation systems, we found that drivers trust and use the ACC more than the LA. While driving, drivers express more liking statements about the ACC and more disliking and criticism statements about the LA. However, the number of mentioned interaction difficulties is higher for the ACC. We discuss possible reasons for the observed differences between the longitudinal (ACC) and lateral (LA) automation.

Vehicles offering conditional automated driving (SAE Level 3 [1]) are now becoming available worldwide. At this automated driving level, it is possible for drivers to engage in non-driving related activities while the vehicle takes over all lateral and longitudinal maneuvers. However, drivers must remain fallback ready to reengage control when requested to do so by the vehicle. Human factors research in automation has for a long time established that it is difficult for drivers, who are out-of-the-loop, to quickly, effectively, and safely re-engage in the driving task. This video submission presents two methods to increase the acceptability and safety of automated driving at SAE level 3. These methods were developed in the EU Horizon 2020 project HADRIAN (Holistic Approach for Driver Role Integration and Automation Allocation for European Mobility Needs; https://hadrianproject.eu/). The first method makes the duration of SAE L3 drives more predictable and expectable to the driver by displaying the duration. Duration information is received by road infrastructure messages that are sent to the vehicle. Also, the amount of time that a driver has to reengage manual control after driving at SAE L3 is predicted and displayed to the driver. The second method provides the driver with various types of learning information and feedback to help improve the driver's skills in handling the automated vehicle. Detailed analyses describing the impact of these methods are currently under investigation and will be reported soon.

With increasing levels of automation on modern vehicles, drivers need more knowledge about their vehicle than ever before. Thereby, the display of vehicle status and alerts are important means for safe and effective use. Nevertheless, there are currently no common and consistent ways of providing such status in‐formation to drivers. This is different from other domains where standardized alert management structure are common such as in modern aviation. In current vehicles, initiation, use, and termination of automated driving assistance differs considerably between vehicle models and brands and require special learning ex‐periences by the driver and alerts are not prioritized and harmonized across the various onboard systems. With higher levels of automation such complexity is expected to increase enormously. To address this problem, we propose a common taxonomy of vehicle display status and alerting terminology that is based on flight deck regulations in aviation and propose simple principles for their use in a man‐aged display status and alerting system.

Cyclists belong to the group of vulnerable road users and, thus, need particular protection in road traffic.One way to enhance cyclists' safety is to use urban data (e.g., infrastructure data, accident statistics) to inform cyclists about potentially dangerous areas, allowing them to better adjust to the situation and elevate their self-protection.However, the question is how to inform cyclists about such dangerous areas.In this paper, we present the results of two field studies, investigating two wearables (headphones vs. smart glasses) and different signal options to inform cyclists about dangerous areas.Study participants were cycling along a predefined track and could experience the different wearables and signals.The main aim of the studies was to find out how cyclists perceive and experience the different approaches.Participants' impressions were captured with questionnaires and interviews.Our results show a clear preference of the headphones over the smart glasses and signaling with intermittent audio while being in the dangerous area.However, we also found that participants' acceptance of the approach was influenced by the additional perceived benefit the respective wearable would have in daily life.Using a wearable solely to be warned, although this would increase safety, was less acceptable.We discuss the implications of these findings for the design of cyclist warning systems.

For designing qualitative interfaces for Public Participatory Geographic Information Systems (PPGIS), the user and use case should be clearly defined. However, PPGIS users may differ significantly, e.g. regarding their cultural background, IT-literacy, or interests. Studies examining varying user types and their impact on PPGIS usability are, however, lacking. In this paper, we analyse the user spectrum through conducting a usability study with 73 participants located in Colombia, Uganda and Austria. We combined a qualitative survey (conducted in all three countries) with an eye-tracking based survey (conducted only in Austria). Most of the usability issues arose due to inexperience in using interactive maps or applications other than social media. Based on the findings, we explored which user context information had an impact on which usability problem. With this, we designed an adaptation gradient that can be used for future research on developing adaptive PPGIS interfaces.

Increasing the driving range of electric vehicles has been an important research endeavour to increase the wide acceptability of electric vehicles as means for more sustainable mobility. One specific stream of research is concerned with helping to improve the driver's individual driving style for increased, but also more predictable, vehicle range. Thereby, traditional approaches often utilize displays such as eco-driving indicators or route planning capabilities, but leave a driver's motivation toward more sustainable driving often untouched. In this study, we complement this existing research by investigating on-board displays to increase drivers' motivation toward efficient driving to achieve more long-term sustainable behavioral changes. Within the European project DOMUS, we developed a tablet application that aims at motivating drivers to drive more energy efficient by using a gamification approach. Drivers are challenged to drive energy efficient during a drive, thereby competing with other drivers. In a between-subjects driving simulator study, we compared this approach with other ways to enhance driving efficiency. We compared four different conditions: (1) Drivers were asked to drive energy efficient without further information, (2) they received a training for energy efficient driving in form of an instructional video before the drive, (3) they were challenged by the application to drive energy efficient, or (4) they were provided both – training and challenges. We found that energy consumption was mostly reduced with the training video (25.04%), followed by training and use of the application (23.79%), use of the application (18.85%), and no training (15.12%).

When drivers approach a potentially critical situation, they tend to glance over drivers of neighboring vehicles to gather a mutual understanding of the respective states and intentions. Then, experienced drivers can take quick decisions and prevent the onset of a danger. Yet, such a safety-effective behavior finds no equals in current automated driving, although the technologies to build a similar solution are already available. Therefore, it is important to investigate the effects of sharing drivers’ state among road users to understand the potential benefit for pre-critical situations. A networked simulators study was performed involving two drivers in a cut-in maneuver. Results indicate that when a driver is notified that the driver in the adjacent vehicle is distracted, the preferred reaction is to change lane, putting more space between the respective vehicles. Such a preventive action should therefore become the target behavior for automated vehicles capable of a human-like driving style.

The front-seat passenger in a vehicle may assist a driver in providing hints towards points of interest in a driving situation. In order to communicate spatial information efficiently, the so-called shared gaze approach has been introduced in previous research. Thereby, the gaze of the front-seat passenger is visualized for the driver. So far, this approach has been solely investigated in driving simulator environments. In this paper, we present a study on how well shared gaze works in a real driving situation (n = 8). We examine identification rates of different object types in the driving environment based on the visualization of the front-seat passenger's gaze via glowing LEDs on an LED-strip. Our results show that this rate is dependent on object relevance for the driving task and movement of the object. We found that perceived visual distraction was low and that the usefulness of shared gaze for navigational tasks was considered high.

Riding a highly automated bus has the potential to bring about a set of novel challenges for the passenger. As there is no human driver present, there is no one to talk to regarding driving direction, stops, or delays. This lack of a human element is likely to cause a stronger reliance on the in-vehicle means of communication, such as displays. In this paper, we present the results from a qualitative study, in which we tested three different on-screen visualizations for passenger information during an automated bus trip. The designs focused primarily on signaling the next stop and proper time to request the bus to stop in absence of a human driver. We found that adding geo-spatial details can easily confuse more than help and that the absence of a human driver makes passengers feel more insecure about being able to exit at the right stop. Thus, passengers are less receptive for visual cues signaling upcoming stops and more likely to input stop requests immediately upon leaving the station.

This paper presents an overview of a driving simulation platform, which is designed to let users to participate in a study over a longer period of time and in their own home. A small scale study of the platform is presented and an overview of the experiences in running such studies is summarised.

Advanced Driver Assistance Systems (ADAS) aim to increase safety by supporting drivers in the driving task. Especially older drivers (65+ years), given the nature of aging, could benefit from these systems. However, little is known about older drivers' acceptance of ADAS in general and how particular acceptance aspects influence their intention to use such systems. To address this research gap, we present results from a large-scale online survey (n=1328) with aging drivers, which was conducted in three European countries in 2019. We identified several demographic and driving -related variables, which are significantly related to acceptance. Furthermore, we found that older drivers' intention to use ADAS is most strongly predicted by favorable acceptance aspects (i.e., usefulness, reassurance, and trust), while unfavorable aspects (i.e., annoyance, irritation, and stress) were found to have less to none predictive power. The findings are discussed considering future research directions in this area.