Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles' external human-machine interfaces. (September 2020) - Dey, Debargha; Habibovic, Azra; Löcken, Andreas; Wintersberger, Philipp; Pfleging, Bastian; Riener, Andreas; Martens, Marieke; Terken, Jacques
Abstract: There is a growing body of research in the field of interaction between automated vehicles and other road users in their vicinity. To facilitate such interactions, researchers and designers have explored designs, and this line of work has yielded several concepts of external Human-Machine Interfaces (eHMI) for vehicles. Literature and media review reveals that the description of interfaces is often lacking in fidelity or details of their functionalities in specific situations, which makes it challenging to understand the originating concepts. There is also a lack of a universal understanding of the various dimensions of a communication interface, which has impeded a consistent and coherent addressal of the different aspects of the functionalities of such interface concepts. In this paper, we present a unified taxonomy that allows a systematic comparison of the eHMI across 18 dimensions, covering their physical characteristics and communication aspects from the perspective of human factors and human-machine interaction. We analyzed and coded 70 eHMI concepts according to this taxonomy to portray the state of the art and highlight the relative maturity of different contributions. The results point to a number of unexplored research areas that could inspire future work. Additionally, we believe that our proposed taxonomy can serve as a checklist for user interface designers and researchers when developing their interfaces. Highlights: An 18-item taxonomy is proposed to structure the description of eHMI concepts. The taxonomy captures the physical and functional design attributes of an eHMI. 70 eHMI concepts are systematically categorized using the taxonomy to provide a state of the art. Several research gaps are identified towards the development of optimal eHMIs.