How can design help enhance trust calibration in public autonomous vehicles?

TL;DR

This paper explores how design tools can improve trust calibration in public autonomous vehicles by enhancing passenger interactions, aiming for safer and more positive shared transportation experiences.

Contribution

It introduces design strategies specifically aimed at calibrating trust and improving data accuracy in autonomous vehicle passenger interactions.

Findings

Design tools can effectively calibrate trust levels in autonomous vehicles.

Enhanced passenger interactions lead to increased trustworthiness.

Improved data accuracy supports safer autonomous vehicle operation.

Abstract

Trust is a multilayered concept with critical relevance when it comes to introducing new technologies. Understanding how humans will interact with complex vehicle systems and preparing for the functional, societal and psychological aspects of autonomous vehicles' entry into our cities is a pressing concern. Design tools can help calibrate the adequate and affordable level of trust needed for a safe and positive experience. This study focuses on passenger interactions capable of enhancing the system trustworthiness and data accuracy in future shared public transportation.