AI roles shape driver decisions in automated vehicle takeovers
Advisor, Co-pilot, or Guardian? AI Role Framing and Human Decision Behavior in Automated Vehicle Takeovers
Human-Computer Interaction
Summary
Taking over control from a self-driving car can be tricky, and different ways AI helps people make this switch matter. The authors studied three AI roles—Advisor, Co-pilot, and Guardian—to see how they affect how quickly and accurately drivers react. They found that when the AI acts like an Advisor or Guardian, people decide faster and more accurately than with a Co-pilot role. Also, how the AI explains things and which side it refers to makes a difference in how fast people respond.
What this means in practice
- •For automotive user interface designers: Design in-car AI messages based on role framing to improve driver takeover speed and accuracy in level 3 automated vehicles.
- •For driver assistance system engineers: Implement speech-based takeover alerts that adjust explanation style and directional cues to reduce driver workload.
Tested on simulated data.
Authors
Wei-Hsiang Lo, Jingyi Xie, Gaojian Huang
Abstract
Artificial intelligence (AI) technology has gradually been integrated into automated vehicles. However, how this technology could systematically support takeover transitions in SAE Level 3 automated vehicles remains unknown, even though this level may remain a major form of driving automation in the near future. To address this, we implemented the three AI roles proposed by the triadic Human-AI collaboration framework (Advisor, Co-pilot, and Guardian) as potential authority-ability configurations for AI agents within an automated system. We then examined their interactions with explanation type (why and action) and directional semantics (ipsilateral and contralateral) in an automated driving simulator study with 24 participants. Results showed that, across the three AI roles, Advisor had the shortest decision-making time, followed by Guardian and Co-pilot. Advisor and Guardian were associated with higher decision-making accuracy and higher satisfaction ratings than Co-pilot. Guardian was associated with the lowest workload, followed by Advisor and Co-pilot. The effect of explanation type on decision-making time depended on AI role. Action explanations were faster under Advisor, whereas why explanations were faster under Co-pilot. For directional semantics, ipsilateral semantics were associated with shorter decision-making time than contralateral semantics. Overall, these findings provide preliminary guidance for designing AI-role authority-ability configurations and their associated speech-based takeover messages.