Effect of Proactive Interaction on Trust in Autonomous Vehicles

Jingyue Sun, Yanqun Huang (), Xueqin Huang, Jian Zhang and Hechen Zhang
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Jingyue Sun: Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
Yanqun Huang: Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
Xueqin Huang: Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
Jian Zhang: Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
Hechen Zhang: Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China

Sustainability, 2024, vol. 16, issue 8, 1-16

Abstract: With rapid advancements in autonomous vehicles (AVs), mistrust between humans and autonomous driving systems has become a focal concern for users. Meanwhile, proactive interaction (PI), as a means to enhance the efficiency and satisfaction of human–machine collaboration, is increasingly being applied in the field of intelligent driving. Our study investigated the influence of varying degrees of PI on driver trust in Level 4 (L4) AVs set against a virtual reality (VR)-simulated driving backdrop. An experiment with 55 participants revealed that, within an autonomous driving scenario without interference, elevated PI levels fostered increased trust in AVs among drivers. Within task scenarios, low PI resulted in enhanced trust compared to PI characterized by information provision. Compared to females, males demonstrated reduced trust in medium PIs. Drivers with elevated extroversion levels exhibited the highest trust in advanced PIs; however, the difference between excessively and moderately extroverted participants was not significant. Our findings provide guidance for interaction designs to increase trust, thereby enhancing the acceptance and sustainability of AVs.

Keywords: autonomous vehicles; human–vehicle interaction; trust; proactive interaction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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