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.

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.

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.

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.

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.