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.

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.

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.