Over the past decade, autonomous systems have greatly improved how people live and work. However, realizing the full potential of these technologies is only possible if people establish appropriate trust in them. Therefore, prospective systems are expected to sense and respond to users' trust changes and ideally adapt to trust-aware design considerations. I will present my recent work on developing trust-aware customized adaptive systems in vehicle automation using Interactive Reinforcement Learning. We first designed an online driving simulator study to collect human trust dynamics while interacting with vehicle automation. After analyzing trust evolution characteristics, I modeled the trust as a dynamic system using the State Space (SS) model. Then, I proposed an Interactive Reinforcement Learning algorithm to integrate the previously designed trust models into the Inverse Reinforcement Learning (IRL) framework. As a result, the optimal policies recovered by the proposed algorithm can capture the driver preferences of different driving styles from large-scale naturalistic driving data and trust dynamics while interacting with the autonomous systems. Our proposed framework has implications for the design of future human-aware high-fidelity autonomous systems. I will conclude the talk with an overview of how our current work moves toward this future.

Presenter Bio:

Jundi Liu is a Postdoctoral Research Fellow in the Department of Industrial and Operations Engineering at the University of Michigan, working with Prof. Xi Jessie Yang. He graduated from the University of Washington (UW) in September 2022 with a Ph.D. in Industrial and Systems Engineering, working with Prof. Linda Boyle and Prof. Ashis Banerjee on trust-aware customized vehicle automation. Jundi received his B.S. in Computer Science and Engineering from Shanghai Jiao Tong University in 2016 and his M.S. degree in Industrial and Systems Engineering from UW in 2018. Jundi's research interest is to improve human-autonomy interaction through understanding and modeling human trust and to develop trust-aware adaptive systems to support human users in complex decision-making tasks. His research aims to create a holistic framework that enables human-aware high-fidelity autonomous systems.