Enhancing Human-Autonomous System Interaction and Team Dynamics in Automated Driving Systems (ADS)

As Automated Driving Systems (ADS) advance toward higher levels of automation (SAE Levels 3-5), the role of human drivers is shifting from active control to supervision and intervention. However, traditional human-automation interaction frameworks do not fully account for the dynamic team-based coordination required for effective ADS integration. Poor adaptability and coordination between drivers and ADS can result in critical safety failures, as seen in real-world incidents. This research focuses on human-autonomy teaming (HATs) in ADS, emphasizing collaborative adaptation, shared decision-making, and real-time interaction metrics to enhance safety and user experience. By incorporating team cognition theory and layered dynamical models, this research aims to develop human-machine teaming metrics that facilitate adaptive and context-aware cooperation. Key research questions include: (1) How do human drivers and ADS dynamically interact? (2) What team cognition metrics effectively measure human-ADS adaptability? (3) How can real-time analytics improve driver-ADS collaboration and safety?