CiteScore 3.36
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Robot. AI | doi: 10.3389/frobt.2019.00117

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

  • 1Michigan college of engineering, University of Michigan, United States
  • 2College of Engineering, University of Massachusetts Amherst, United States
  • 3Toyota Research Institute (TRI), United States
  • 4Robotics Institute, University of Michigan, United States
  • 5School of Information, University of Michigan, United States

Pedestrians’ acceptance of automated vehicles (AVs) depends on their trust in the AVs. We
developed a model to promote pedestrians’ trust in AVs through implicit communications. To
empirically verify this model, we conducted a human subject study with 30 participants in a virtual
reality environment. The study manipulated two factors: AV driving behavior (defensive, normal,
and aggressive) and the traffic situation (signalized and unsignalized crossing). Pedestrians’ trust
in AVs was influenced by AV driving behavior as well as the condition/presence of a signal light.
The impact of the AV’s driving behavior on trust in the AV was dependent on the presence of a
signal light. There were also strong correlations between trust and pedestrian gaze at certain
areas/objects. We also present implications for research and design.

Keywords: Automated vehicles (AV), human-automation interaction, Automation trust, Uncertainty reduction theory, virtual reality, Implicit communication, Human robot interaction (HRI)

Received: 14 Mar 2019; Accepted: 25 Oct 2019.

Copyright: © 2019 Jayaraman, Creech, Tilbury, Yang, Pradhan, Tsui and Robert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Lionel P. Robert, University of Michigan, Robotics Institute, Ann Arbor, United States, lprobert@umich.edu