The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.

For vehicle safety, the in‐time monitoring of the driver and assessing his/her state is a demanding issue. Frustration can lead to aggressive driving behaviours, which play a decisive role in up to one‐third of fatal road accidents. Consequently, the authors present the automatic analysis of the emotional driver states of frustration, anxiety, positive and neutral. Based on experiments with normal drivers within cars in real‐world (low expressivity) situations, they use speech data, as speech can be recorded with zero invasiveness and comes naturally in driving situations. A careful selection of speech features, subject data identification, hyper‐parameter optimisation, and machine learning algorithms was applied for this difficult 4‐emotion‐class detection problem, where the literature hardly reports results above chance level. In‐car assistance demands real‐time computing. A very detailed analysis yields best results with relatively small random forests, and with an optimal feature set containing only 65 features (6.51% of the standard emobase feature set) which outperformed all other feature sets, producing 35.38% unweighted average recall (53.26% precision) with low computational effort, and also reducing the inevitably high confusion of ‘neutral’ with low‐expressed emotions. This result is comparable to and even outperforming other reported studies of emotion recognition in the wild. Their work, therefore, triggers adaptive automotive safety applications.

Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner.

Automated vehicles are entering the roads and automation is applied to cars, trucks, buses, and even motorcycles today. High automation foresees transitions during driving in both directions. The driver and rider state become a critical parameter since reliable automation allows safe intervention and transit control to the automation when manual driving is not performed safely anymore. When the control transits from automation to manual an appropriate driver state needs to be identified before releasing the automated control. The detection of driver states during manual and automated driving and an appropriate design of the human–machine interaction (HMI) are crucial steps to support these transitions. State‐of‐the‐art systems do not take the driver state, personal preferences, and predictions of road conditions into account. The ADAS&ME project, funded by the H2020 Programme of the European Commission, proposes an innovative and fully adaptive HMI framework, able to support driver/rider state monitoring‐based transitions in automated driving. The HMI framework is applied in the target vehicles: passenger car, truck, bus, and motorcycle, and in seven different use cases.

With the introduction of highly automated vehicles (HAV; SAE Level 4) driverless mobility systems may fundamentally change public transportation. For potential passengers of these systems, new situations may arise, leaving them without a driver to directly communicate with. This can be problematic when the driving automation is confronted with situations it might be unable to manage by itself, which would cause a minimal risk maneuver (MRM), e.g. a complete standstill of the vehicle. Some concepts enhance these systems with a remote assistant who can support the HAV, adding novelty to the transportation process. This increased novelty may result in user discomfort for passengers, causing a need for information systems that address this. One possibility to do so and to thus improve hedonic quality and thus comfort for passengers is by providing system transparency via a media rich in-vehicle Human-Machine Interface (iHMI), which provides information to passengers about the ADS and its processes. However, there is still a gap in research on the ideal communication mechanism to ensure good hedonic quality and comfort during these situations. We conducted a simulator study in virtual reality (VR), investigating interfaces based on media richness theory to address this issue. The interface provided a multi-layered iHMI consisting of transparency information presented with varying levels of media richness. In a block design, participants experienced three versions of an iHMI, one presenting information via text, another combining text and auditive presentation and a third adding a human avatar simulating Face-to-Face communication. After each scenario, participants completed questionnaires regarding understanding, predictability, trust and user experience. Study results reveal significant increases in the hedonic quality of user experience and trust towards the system for interfaces with increased media richness, providing a first step towards enhancing user comfort with iHMI in automated mobility systems during challenging situations for the automation system, such as MRM.