报告人：方玉光，香港城市大学 教授

报告地点：球王会体育官网天心校区电子楼207

报告时间：2025年10月29日（周三）上午9:00-9:40

报告题目：All for One and One for All: Collaborative Perception Enhances Smart Mobility

报告人简介：

Dr. Yuguang “Michael” Fang received an MS degree in Mathematics from Qufu Normal University, Shandong, China in 1987, a PhD degree in Systems, Control and Industrial Engineering from Case Western Reserve University in 1994, and a PhD degree in Electrical and Computer Systems from Boston University in 1997. He joined the Department of Electrical and Computer Engineering at University of Florida in 2000 as an assistant professor, then was promoted to associate professor in 2003, full professor in 2005, and distinguished professor in 2019, respectively. Since August 2022, he has been a Hong Kong Global STEM Scholar and the Chair Professor of Internet of Things with the Department of Computer Science at City University of Hong Kong.

Dr. Fang received many awards, including the US NSF CAREER Award, US ONR Young Investigator Award, 2018 IEEE Vehicular Technology Outstanding Service Award, IEEE Communications Society AHSN Technical Achievement Award (2019), CISTC Technical Recognition Award (2015), and WTC Recognition Award (2014). He served as the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2013-2017) and IEEE Wireless Communications (2009-2012), and serves/served on several editorial boards of journals, including Proceedings of the IEEE (2018-present) and ACM Computing Surveys (2017-present). He served as the Technical Program Co-Chair of IEEE INFOCOM’2014. He has actively engaged with his professional community, serving as a Member-at-Large of the Board of Governors of IEEE Communications Society (2022-2024, 2025-2027) and the Director of Magazines of IEEE Communications Society (2018-2019). He is a fellow of ACM, IEEE, and AAAS.

报告内容简介：

Collaborative perception has emerged as a promising avenue to enhance safe driving performance by leveraging data fusion from the views of multiple vehicles. However, limited communication resources and sensing range pose serious constraints on such a collaborative perception for Connected and Autonomous Driving (CADs). In this talk, the speaker will discuss how to effectively utilize limited communication resource to share multiple view data for high-quality perception, laying the foundation for smart mobility. He will also introduce a priority-aware collaborative sensing framework and a novel domain generalization approach to address the critical design challenges in collaborative perception systems.