Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.

External Human-Machine Interfaces (eHMIs) are expected to bridge the communication gap between an automated vehicle (AV) and pedestrians to replace the missing driver-pedestrian interaction. However, the relative impact of movement-based implicit communication and explicit communication with the aid of eHMIs on pedestrians has not been studied and empirically evaluated. In this study, we pit messages from an eHMI against different driving behaviors of an AV that yields to a pedestrian to understand whether pedestrians tend to pay more attention to the motion dynamics of the car or the eHMI in making road-crossing decisions. Our contributions are twofold: we investigate (1) whether the presence of eHMIs has any objective effect on pedestrians’ understanding of the vehicle’s intent, and (2) how the movement dynamics of the vehicle affect the perception of the vehicle intent and interact with the impact of an eHMI. Results show that (1) eHMIs help in convincing pedestrians of the vehicle’s yielding intention, particularly when the speed of the vehicle is slow enough to not be an obvious threat, but still fast enough to raise a doubt about a vehicle’s stopping intention, and (2) pedestrians do not blindly trust the eHMI: when the eHMI message and the vehicle’s movement pattern contradict, pedestrians fall back to movement-based cues. Our results imply that when explicit communication (eHMI) and implicit communication (motion-dynamics and kinematics) are in alignment and work in tandem, communication of the AV’s yielding intention can be facilitated most effectively. This insight can be useful in designing the optimal interaction between AVs and pedestrians from a user-centered design perspective when driver-centric communication is not available.

Drivers in negative emotional states, such as anger or sadness, are prone to perform bad at driving, decreasing overall road safety for all road users. Recent advances in affective computing, however, allow for the detection of such states and give us tools to tackle the connected problems within automotive user interfaces. We see potential in building a system which reacts upon possibly dangerous driver states and influences the driver in order to drive more safely. We compare different interaction approaches for an affective automotive interface, namely Ambient Light, Visual Notification, a Voice Assistant, and an Empathic Assistant. Results of a simulator study with 60 participants (30 each with induced sadness/anger) indicate that an emotional voice assistant with the ability to empathize with the user is the most promising approach as it improves negative states best and is rated most positively. Qualitative data also shows that users prefer an empathic assistant but also resent potential paternalism. This leads us to suggest that digital assistants are a valuable platform to improve driver emotions in automotive environments and thereby enable safer driving.

In this paper, we investigate the effect of an external human-machine interface (eHMI) and a conspicuous external vehicle appearance due to visible sensors on pedestrian interactions with automated vehicles (AVs). Recent research shows that AVs may need to explicitly communicate with the environment due to the absence of a driver. Furthermore, in interaction situations, an AV that looks different and conspicuous owing to an extensive sensor system may potentially lead to hesitation stemming from mistrust in automation. Thus, we evaluated in a virtual reality study how pedestrian attitude, the presence/absence of an eHMI, and a conspicuous sensor system affect their willingness to cross the road. Results recommend the use of an eHMI. A conspicuous appearance of automated-driving capability had no effect for the sample as a whole, although it led to more efficient crossing decisions for those with a more negative attitude towards AVs. Our findings contribute towards the effective design of future AV interfaces.

Technological developments in the domain of vehicle automation are targeted toward driver-less, or driver-out-of-the-loop driving. The main societal motivation for this ambition is that the majority of (fatal) accidents with manually driven vehicles are due to human error. However, when interacting with technology, users often experience the need to customize the technology to their personal preferences. This paper considers how this might apply to vehicle automation, by a conceptual analysis of relevant use cases. The analysis proceeds by comparing how handling of relevant situations is likely to differ between manual driving and automated driving. The results of the analysis indicate that full out-of-the-loop automated driving may not be acceptable to users of the technology. It is concluded that a technology that allows shared control between the vehicle and the user should be pursued. Furthermore, implications of this view are explored for the concrete temporal dynamics of shared control, and general characteristics of human machine interface that support shared control are proposed. Finally, implications of the proposed view and directions for further research are discussed.

In this paper, we present the results of an online survey (N = 170) on emotional situations on the road. In particular, we asked potential early adopters to remember a situation where they felt either an intense positive or negative emotion while driving. Our research is motivated by imminent disruptions in the automotive sector due to automated driving and the accompanying switch to selling driving experiences over horsepower. This creates a need to focus on the driver’s emotion when designing in-car interfaces. As a result of our research, we present a set of propositions for affective car interfaces based on real-life experiences. With our work we aim to support the design of affective car interfaces and give designers a foundation to build upon. We find respondents often connect positive emotions with enjoying their independence, while negative experiences are associated mostly with traffic behavior. Participants who experienced negative situations wished for better information management and a higher degree of automation. Drivers with positive emotions generally wanted to experience the situation more genuinely, for example, by switching to a “back-to-basic” mode. We explore these statements and discuss recommendations for the design of affective interfaces in future cars.