Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT

In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

KEYWORDS:

• Automated vehicles
• in-vehicle intelligent agent
• automated vehicle trust
• driving performance

Disclosure statement

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

Additional information

Funding

The work was supported by the UPS Doctoral Fellowship.

Notes on contributors

Manhua Wang

Manhua Wang is a PhD candidate in Industrial and Systems Engineering at Virginia Tech. She received her M.S. in Information Science from the University of North Carolina at Chapel Hill. Her research aims to enhance the human-technology partnership, focusing on understanding and addressing human information needs in the context of intelligent transportation systems and future workplaces.

Seul Chan Lee

Seul Chan Lee is an Assistant Professor in the Division of Media, Culture, and Design Technology & Department of Human Computer Interaction at Hanyang University ERICA. His research goal is to explore users’ needs and requirements, evaluate system artifacts, and improve systems and devices based on the theories and methodologies of Human Factors, Ergonomics, and Human-Computer Interaction.

Myounghoon Jeon

Myounghoon Jeon is an Associate Professor in Industrial and Systems Engineering and Computer Science (by courtesy) at Virginia Tech. He received his PhD from Georgia Tech. His Mind Music Machine Lab conducts research on HCI with a focus on sound and emotion in the application areas of automotive user experiences, assistive robotics, and arts in XR environments.