AUTHOR=Bonneviot Flavie , Coeugnet Stéphanie , Brangier Eric 

TITLE=How to improve pedestrians' trust in automated vehicles: new road infrastructure, external human–machine interface with anthropomorphism, or conventional road signaling?

JOURNAL=Frontiers in Psychology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1129341

DOI=10.3389/fpsyg.2023.1129341

ISSN=1664-1078

ABSTRACT=To be accepted, automated vehicles need to gain the trust of all road users. To make technology trustworthy, automated vehicles need to transmit crucial information to pedestrians through a human-machine interface, so that pedestrians can accurately predict their next behavior and act. However, the unsolved core issue in the field of vehicle automation is to know how to successfully communicate with pedestrians in a way that is efficient, comfortable, and easy to understand. This study investigated the impact of three human-machine interfaces specifically designed for pedestrians' trust during the street crossing in front of an automated vehicle. The interfaces used different communication channels to interact with pedestrians i.e., through a new road infrastructure, an external human-machine interface with anthropomorphism, or with conventional road signaling. Mentally projected in standard and non-standard use cases of human-machine interfaces, 731 participants reported their feelings and behavior through an online survey. Results showed that human-machine interfaces were efficient to improve trust and willingness to cross the street in front of automated vehicles. Among external human-machine interfaces, anthropomorphic features showed significant advantages in comparison with conventional road signals to induce pedestrians' trust and safer crossing behaviors. More than the external human-machine interfaces, findings highlighted the efficiency of the trust-based road infrastructure on the global street crossing experience of pedestrians with automated vehicles. All of these findings support trust-centered design to anticipate and build safe and satisfying human-machine interactions.