Beyond Levels of Driving Automation: A Triadic Framework of Human-AI Collaboration in On-Road Mobility
This addresses the need for better human-AI interaction in automated vehicles, but it is incremental as it builds on existing classifications like SAE Levels.
The study tackles the problem of unclear real-time collaboration between humans and AI in dynamic driving contexts by proposing a triadic framework with three AI roles (Advisor, Co-Pilot, Guardian) that adapt to human needs, laying a foundation for adaptive strategies in automated vehicles.
The goal of the current study is to introduce a triadic human-AI collaboration framework for the automated vehicle domain. Previous classifications (e.g., SAE Levels of Automation) focus on defining automation levels based on who controls the vehicle. However, it remains unclear how human users and AI should collaborate in real-time, especially in dynamic driving contexts, where roles can shift frequently. To fill the gap, this study proposes a triadic human-AI collaboration framework with three AI roles (i.e., Advisor, Co-Pilot, and Guardian) that dynamically adapt to human needs. Overall, the study lays a foundation for developing adaptive, role-based human-AI collaboration strategies in automated vehicles.