Tactical-Level Explanation is Not Enough: Effect of Explaining AV’s Lane-Changing Decisions on Drivers’ Decision-Making, Trust, and Emotional Experience

Abstract

Explanations have become increasingly vital in communicating with human drivers about the reasons for the decision-making of autonomous vehicles (AVs), particularly in tactical-level driving tasks. Focusing on lane-changing scenarios, we examine whether providing tactical-level explanations and in addition, whether providing a confirmation option, influences drivers’ decision-making, trust, and emotional experience. Thirty participants were equally assigned into three groups: indicator (I), explanation (E), and explanation + confirmation (EC), experiencing four lane-changing scenarios in a driving simulator. Real-time question probes and interviews were adopted to understand drivers’ decision-making process, and post-drive questionnaires on trust and emotional experience were given. Results indicated that merely providing tactical-level explanations had little effect on driver’s trust and experience, but caused worse decision-making performance. The option to confirm lane changes after an explanation promoted driver’s trust, but brought two-sided effects on decision-making performance. Situational trust and decision-making performance varied significantly across lane-changing scenarios.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities.

Notes on contributors

Yiwen Zhang

Yiwen Zhang is a Ph.D. Candidate at the College of Design and Innovation, Tongji University. He received his MEng degree in industrial design engineering from Shandong University. His research is in the field of human-intelligent system collaboration and interaction design in automated systems.

Wenjia Wang

Wenjia Wang is a master’s student for Interaction Design at the College of Design and Innovation, Tongji University. Her research interests include automotive user interfaces and collaborative design in the era of automated driving. She received her BE degree in Industrial Design from Hunan University.

Xinyan Zhou

Xinyan Zhou is a master’s student for Interaction Design at the College of Design and Innovation, Tongji University. His research interests include intelligent cockpit and automotive user interfaces in the era of automated driving. He received his BE degree in Vehicle Engineering from Tongji University.

Qi Wang

Qi Wang is an Associate Professor in the College of Design and Innovation at Tongji University. She received her Ph.D. in the Department of Industrial Design from the Eindhoven University of Technology. Her research interests include HCI, wearable systems, and smart textiles.

Xiaohua Sun

Xiaohua Sun is a Professor at the College of Design and Innovation, Tongji University, China. She received her Ph.D. degree in Design and Computation from the Massachusetts Institute of Technology in 2007. Her Center for Digital Innovation conducts research at the intersection of AI and HCI.