Toward AI standardization: A triadic human-ai collaboration framework for multi-level autonomous mobility
arXiv:2504.19120v2 Announce Type: replace-cross Abstract: The goal of the current study is to introduce a triadic human-AI collaboration framework that could be applied in transportation systems such as automated vehicles, micromobility systems, and vehicle teleoperation. Previous standards, such as SAE Levels of Automation, have focused on defining automation levels based on who controls the vehicle. However, it is still not clear how human users and AI should collaborate in real time, especially in dynamic driving contexts where roles can shift frequently. To fill this gap, this study propos
The rapid advancement of AI in real-time, dynamic environments like autonomous driving necessitates new collaboration models beyond traditional automation levels.
This framework is critical for establishing standards for safe and effective real-time human-AI collaboration, particularly in safety-critical applications like mobility.
The focus shifts from who controls to how humans and AI dynamically collaborate, influencing future regulatory frameworks and system designs.
- · AI ethicists
- · Autonomous vehicle developers
- · Regulators
- · Smart city planners
- · Traditional automation framework developers
New industry standards for human-AI interaction in complex systems will begin to emerge, accelerating safe AI deployment.
Public trust and adoption of AI systems, particularly in transportation, could increase due to clearer collaboration models and safety protocols.
This could become a foundational model for human-AI collaboration in other critical sectors beyond mobility, influencing industrial and military applications.
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Read at arXiv cs.AI