Keynote Speakers
 | Prof. Haitao ZhangNational Science Fund for Distinguished Young Scholars, IEEE Fellow Huazhong University of Science and Technology, ChinaHaitao Zhang is a Member of the Academic Committee and Vice Dean of the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology (HUST). He is a Chair Professor and Doctoral Supervisor, a recipient of the State Council Special Allowance, a National Science Fund for Distinguished Young Scholars recipient, and an IEEE Fellow. He also serves as a HUST Chief Professor, Director of the Ministry of Education Engineering Research Center for Autonomous Intelligent Unmanned Systems, and Principal Investigator of a major project under China's National Science and Technology Innovation 2030 Initiative on Next-Generation Artificial Intelligence. He received his Ph.D. from the University of Science and Technology of China in 2005 and conducted postdoctoral research at the University of Cambridge in 2007. He was promoted to professor in 2010. His research focuses on swarm intelligence and collaborative operations of unmanned surface vehicle swarms. He has published more than 150 SCI-indexed papers, including over 130 papers in leading journals such as Nature Machine Intelligence, Nature Communications, National Science Review, Automatica, and various IEEE Transactions journals. His theoretical findings have been highlighted in Nature Physics. His applied research has been translated into industrial applications by organizations including China Shipbuilding Industry Corporation and Guangzhou Shipyard International, supporting major national marine engineering projects such as cross-sea transportation infrastructure in the Guangdong-Hong Kong-Macao Greater Bay Area, offshore wind farms, and oil and gas resource exploration in the South China Sea. His work has also been reported on the official websites of the State Council and the Ministry of National Defense of China. He is the first-ranked recipient of two Hubei Provincial Natural Science First Prizes and one Guangdong Provincial Technological Invention First Prize. He has served on the editorial boards of international journals, including IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Circuits and Systems II: Express Briefs, and Engineering. Title: Cross-Domain Collaborative Coverage and Three-Dimensional Sensing by Autonomous Unmanned Surface Vehicle–Aerial Vehicle Swarms in Marine Environments |
 | Prof. Hui ZhangThe Yangtze River Scholar, IEEE Fellow Beihang University, ChinaHui Zhang, Professor, Doctoral Supervisor, National High-Level Young Talent. He has long been committed to scientific research related to special vehicle design, vehicle dynamics control, and intelligent driving. He has presided over numerous projects, including the International Cooperation Project of the National Key R&D Program of China and the Joint Key Program of the NSFC. He has published more than 150 SCI-indexed journal papers (including over 80 papers in IEEE journals, and won the IEEE Journal Annual Best Paper Award twice), been granted more than 60 invention patents, and published 2 monographs. As the lead recipient, he won the First Prize of the Scientific and Technological Progress Award of the China Society of Automotive Engineers (CSAE) and the First Prize of the Scientific and Technological Progress Award of the China Communications and Transportation Association (CCTA). As a core participant, he received the First Prize of the Natural Science Award of Jilin Province, the First Prize of the Technological Invention Award of the China Machinery Industry Federation, and the Second Prize of the Teaching Achievement Award of Beijing Municipality. He has been honored with the IEEE IES Early Career Award, the SAE International Ralph R. Teetor Educational Award, and the CSAE Outstanding Young Talent Award, among others. He currently serves as Vice Chair of the Technical Committee on Industrial Cyber-Physical Systems of the IEEE Industrial Electronics Society (IES), Co-Chair of the Intelligent and Connected Vehicles Technical Committee of SAE International, and Associate Editor/Editorial Board Member for prestigious journals including IEEE Transactions on Intelligent Vehicles (TIV), IEEE Transactions on Industrial Electronics (TIE), and Mechanical Systems and Signal Processing (MSSP). Title:Safety Technology of Intelligent Vehicles |
 | Prof. Yuan YuanThe National Youth Talent Northwestern Polytechnical University, ChinaYuan Yuan, Tenured Professor at the School of Astronautics, Northwestern Polytechnical University (NPU), Doctoral Supervisor, Recipient of the NSFC Excellent Young Scientists Fund. His main research focuses on the interdisciplinary field of game confrontation theory and artificial intelligence, with a core emphasis on the safe and autonomous control of unmanned systems under multi-source interference and adversarial environments. He has presided over the NSFC Excellent Young Scientists Fund Project and a national key R&D program with a funding scale of over 10 million RMB. He has published more than 40 SCI-indexed papers and 3 monographs in Chinese and English. He has won the First Prize of the Natural Science Award of the Chinese Association of Automation (CAA), the Second Prize of the Natural Science Award of Shaanxi Province, and the Wu Wenjun Outstanding Young Scientist Award in Artificial Intelligence. He was selected for the Soaring Overseas Scholar Program of NPU. He once served as Guest Editor for the International Journal of Systems Science, and currently holds positions as Editorial Board Member for journals including IEEE Transactions on Cybernetics, and Deputy Director of the Youth Working Committee of the Chinese Institute of Command and Control (CICC). Title:The multidimensional game of swarm spacecrafts confronting orbital threats Abstract:This report focuses on addressing key challenges in on-orbit game-theoretic decision-making for spacecraft formations over large space scales, considering strategy randomness, the complexity of collaborative decision-making, and the unclear mechanisms of strategic advantage. We conduct research on multidimensional game-theoretic decision methods by constructing a game-theoretic framework that integrates multi behavior, multi-layer, and multi-scale coupling. We design a hierarchical spatial reinforcement learning strategy and establish a cross-layer endgame analysis framework to systematically address critical issues such as game modeling, strategy optimization, and mechanism analysis in heterogeneous spacecraft formations. |
 | Prof. Zhanwen LiuThe National Youth Talent Chang'an University, ChinaZhanwenLiu, Ph.D., is a Professor (Grade III) and Doctoral Supervisor at Chang’an University. She is a recipient of the National Young Talent Program and the Shaanxi Provincial Distinguished Young Talent Award. She currently serves as Vice Dean of the School of Robotics and Embodied Intelligence, Vice Dean of the Shenzhen Research Institute, and Deputy Director of the Shaanxi Engineering Research Center for Internet of Vehicles and Intelligent Vehicle Testing Technology. Her research focuses on embodied intelligence for vehicle–road systems and autonomous testing. She has published over 30 high-quality research papers as first or corresponding author in prestigious international journals and conferences, including AAAI, ECCV, IEEE TMM, and IEEE TCSVT. She holds 51 granted international and Chinese invention patents and has contributed to the development of multiple national and industry association standards. She has led more than 20 national and provincial research projects, including subprojects under the National Key R&D Program of China and projects funded by the National Natural Science Foundation of China (NSFC), covering Key, General, and Young Scientists Fund projects.Her honors and awards include the 14th Shaanxi Youth Science and Technology Award, three First Prizes of the Shaanxi Provincial Science and Technology Progress Award, one Special Prize and one First Prize of the Shaanxi Provincial Higher Education Teaching Achievement Award, as well as one First Prize and one Second Prize of the China Highway and Transportation Society Science and Technology Award. Title:Embodied Intelligence for Vehicle-Road Systems: From Multimodal Perception to Closed-Loop Co-Evolution of Training and Testing Abstract:The rapid advancement of artificial intelligence, world models, and embodied intelligence is accelerating the evolution of vehicle-road systems from multimodal cooperative perception toward collective cognition, autonomous interaction, and continuous evolution. Complex and open traffic environments pose significant challenges, including difficulties in multi-source perception fusion, complex mechanisms of collective interaction, scarcity of long-tail risk scenarios, and insufficient integration of training and testing. Focusing on the technological evolution and key breakthroughs of embodied intelligence in vehicle-road systems, this report presents the team's research progress in multimodal cooperative perception, vehicle-road collective world models, safety-critical scenario generation, and interactive closed-loop integrated training and testing. It further discusses key technologies, including plug-and-play multimodal perception enhancement and collective traffic behavior modeling, lightweight edge deployment of large models, and closed-loop training-testing integrated autonomous evolution. Finally,the presentation highlights preliminary results from real-world deployments of integrated systems and equipment, including the vehicle–road–cloud integration pilot project on Shangyuan Road in Weiyang District, Xi'an; the digital twin system for safety monitoring and risk prevention in the Qinling Tunnel under China's Transportation Powerhouse Pilot Program; and the connected highway monitoring project of Yunnan Communications Investment Group. Building on these applications, the presentation discusses future trends in vehicle–road systems, particularly the transition from individual embodied perception to collective embodied intelligence, and from static testing to autonomous evolution through closed-loop integrated training and testing. |
 | Prof. Changxin LiuNational-Level Young Talent East China University of Science and Technology, ChinaChangxin Liu is a Professor with the East China University of Science and Technology, and has been selected for the National High-Level Young Talent Program and the Shanghai Municipal High-Level Talent Program. His research focuses on optimization and decision-making for complex manufacturing systems, and on distributed optimization and control. In these areas, he has published more than 50 papers in IEEE and IFAC journals and at conferences such as ICML. He has served as principal investigator on a research task of the National Science and Technology Major Project, a General Program grant from the National Natural Science Foundation of China, and an independent research project of the National Key Laboratory of Industrial Control Technology. He is Associate Editor of Circuits, Systems, and Signal Processing and serves on the Early Career Editorial Boards of Engineering and of Digital Twins and Applications. He is also a member of several technical committees, including the Technical Committee on Security and Privacy of the IEEE Control Systems Society, the Technical Committee on Big Data of the Chinese Association of Automation, and the Youth Working Committee of the Shanghai Association of Automation. His work has been recognized with the First Prize of the CAA Natural Science Award, the CAA Outstanding Doctoral Dissertation Award, and the Shaanxi Provincial Outstanding Doctoral Dissertation Award. Title:Distributed Optimization of Interconnected Systems: Methods and Applications Abstract: Interconnected systems are large-scale complex systems in which multiple subsystems are coupled to one another through coupling terms, and they arise in a wide range of applications, including multi-agent systems and large-scale machine learning training. Because each subsystem is autonomous and has only limited access to local information, the overall optimization and regulation of such a system can hardly be handled efficiently by centralized methods, and distributed optimization algorithms are therefore required for cooperative solving. This talk addresses distributed optimization under two typical interconnection structures: optimization with interconnection costs, and optimization with interconnection constraints. For the case of interconnection costs, we propose a robust distributed optimization algorithmic framework. This framework not only provides systematic guidance for designing new algorithms with improved performance, but also serves as a tool for analyzing existing algorithms, yielding tighter and more interpretable theoretical convergence results. For the case of interconnection constraints, we design a distributed optimization algorithm with zero constraint violation, which keeps the constraints satisfied throughout the iterations and is therefore particularly suited to scenarios with stringent safety requirements, such as the real-time online solution of safe distributed controllers based on constrained optimization. Finally, the effectiveness of the proposed algorithms is verified through simulations on distributed acoustic localization and experiments on multi-robot safe formation control. |
 | Prof. Lei ZuoProvincial Outstanding Young Scholar Chang'an University, ChinaLei Zuo is a Professor andPh.D. Supervisor at Chang'an University. He is a recipient of the Shaanxi Provincial Science Fund for Distinguished Young Scholars (Category B) and has been recognized as a "Key Faculty Member" at Chang'an University. He has served as the Principal Investigator (PI) for numerous research grants, including a sub-project of the National Key R&D Program of China, the National Natural Science Foundation of China (NSFC), the Key R&D Program of Shaanxi Province, the Natural Science Basic Research Plan in Shaanxi Province, the China Postdoctoral Science Foundation, and several projects funded by multiple industry-sponsored research projects. He has authored or co-authored over 40 peer-reviewed papers insome prestigious journals, and has co-authored three textbooks. Furthermore, he has led or participated in multiple educational and teaching reform projects, and received the First Prize of the Shaanxi Higher Education Institutions Science and Technology Research Excellence Award. He also serves as a regular reviewer for top-tier international journals, including Automatica, IEEE Transactions on Intelligent Transportation Systems, and IET Intelligent Transport Systems. Title:Traffic State Analysis, Modeling, and Optimal Regulation in Multi-Source Heterogeneous Complex Transportation Networks Abstract:To address the challenges of incomplete traffic state sensing, complex evolution mechanisms, and difficult cooperative regulation in multi-source heterogeneous complex transportation networks, this study conducts systematic research on traffic state analysis, traffic flow modeling, and optimal regulation. First, targeting the prevalent issues of multi-pattern missingness, node heterogeneity, and noise disturbances in real-world traffic sensing data, we develop traffic data imputation and robust traffic flow forecasting methods tailored to multi-scale spatiotemporal characteristics. By comprehensively capturing temporal dependencies, spatial correlations, and network topological features of traffic states, these methods enhance both the accuracy and robustness of traffic state reconstruction and prediction under complex observational conditions. On this basis, we further investigate the underlying evolution mechanisms of traffic flow. Leveraging empirical vehicle trajectory data, we analyze the dynamic coupling relationships among vehicle speed, spacing, and acceleration, reveal the spatial relaxation effect in car-following behavior, and formulate an improved car-following model to characterize the stability and evolutionary patterns across diverse traffic regimes. Finally, to meet the demands of multi-intersection cooperative operation and networked control in urban environments, a signal timing optimization method based on dual-perception max-pressure control is proposed. By integrating traffic state and intersection pressure information, this approach achieves cooperative optimization for multi-intersection signal control and is validated in real-world traffic scenarios in Qingdao, China. Overall, this work establishes a systematic research framework encompassing traffic state reconstruction and prediction, traffic flow evolution mechanism analysis, and networked cooperative regulation, providing theoretical foundations and methodological support for state analysis and cooperative control in multi-source heterogeneous complex transportation networks. |