Using Passenger Elicitation for Developing Gesture Design Guidelines for Adjusting Highly Automated Vehicle Dynamics

Abstract

Highly Automated Vehicles (HAVs) could benefit safety, accessibility, energy conservation and so on. Realizing the potential benefits depends on the extent of usage, which is significantly related to whether HAV driving behavior matches passenger preference. This study explores gesture interaction guidelines for adjusting HAV dynamics using passenger elicitation. Inspired by user-elicited gesture design, we conducted user testing with virtual reality simulation that can present author-created HAV ride plots. The outcomes of the previous study include knowledge of passengers' gesture interaction with HAVs for adjusting vehicle dynamics in aspects of intentions, gesture collections, consensus extent among the participants, gesture characteristics, gesture design mental models, and initial gesture interaction design guidelines. Based on gained knowledge, we will conduct further research exploring, practicing and evaluating the whole-body gesture interaction guidelines for adjusting HAV driving behaviors.