Interactive Output Modalities Design for Enhancement of User Trust Experience in Highly Autonomous Driving

Abstract

Autonomous driving (AD) technology has gradually matured, but the lack of trust and acceptance from users limits its adoption and diffusion. This paper aims to solve the challenges and alleviate the trust crisis by exploring reasonable interactive output modalities. Firstly, we propose a multimodal interactive output scheme with 9 different feedback level. Subsequently, we conduct simulated driving experiments on the scheme, using three methods: driving trust experience questionnaire (DTEQ), eye-tracking, and takeover desire recording. Finally, we analyze the trends and correlations of results at different interactive output levels. The results indicate that the user trust experience is highly positively correlated with the level of interactive output modalities. Under reasonable design, multimodal and high-level interactive output is beneficial for providing more comprehensive feedback information, reducing users’ visual workload, and improving trust. This work provides a foundation for enhancement of user trust experience, and helps promote the application and popularization of AD.

Keywords:

• User trust experience
• autonomous driving
• multimodal interactive output
• human-vehicle interaction
• driving simulator

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Notes on contributors

Jun Ma

Jun Ma, Professor and Ph.D. supervisor at Tongji University, College of Design and Innovation, & School of Automotive Studies, Tongji University. Research areas include intelligent interaction design, automotive interaction design, and automotive user experience.

Yuanyang Zuo

Yuanyang Zuo, Ph.D. candidate at Tongji University, studying at the College of Design and Innovation, Tongji University. Research interests include intelligent automotive interaction design and human factors and multimodal human-vehicle interaction.

Huifang Du

Huifang Du, Ph.D. candidate at Tongji University, studying at the College of Design and Innovation, Tongji University. Research areas include intelligent voice interaction, computer science, and automotive user experience design.

Yupeng Wang

Yupeng Wang, Postdoctoral researcher at Zhejiang Laboratory, researching at Research Center for Frontier Fundamental Studies, Research areas include intelligent product development, optical display design and computational imaging.

Meilun Tan

Meilun Tan, Master’s student in the field of Art and Design, studying at the Academy of Arts & Design, Tsinghua University, Research focus is on computer interaction design and visual experience design.

Jiateng Li

Jiateng Li, Ph.D. candidate at Tongji University, studying at the School of Automotive Studies. Research areas include applied ergonomics and driving distraction in the intelligent cockpit.