November 18, 2025
Star Grand, Broadbeach, Gold Coast, Australia | Room: Currumbin
Zoom Webinar: 843 0148 5732 Code: 037524
WORKSHOP OVERVIEW
Intelligent Vehicles (IVs), including drones, unmanned ground vehicles, autonomous ships, and various other autonomous platforms, are widely recognized as a means to enhance traffic efficiency, reduce workload, and drive the development of smart cities. Over the past decades, significant advancements have been made in the core functionalities of IV systems, such as localization, perception, and control. These technologies can deliver satisfactory performance in constrained or open areas with limited participants. However, their effectiveness is significantly challenged in complex urban scenarios characterized by dense traffic congestion and intricate environmental structures.
How to ensure the performance of existing functions in complex urban scenarios remains an unresolved issue.
Therefore, our workshop aims to foster the development of safety-certifiable navigation and controls for intelligent vehicles in complex urban scenarios.
TOPIC OF INTEREST
High - Precision Localization for Intelligent Vehicles via Multi - sensor Fusion: Integrating GNSS, IMU, LiDAR, Camera, and High - Definition Map.
Cost - Effective High - Precision Localization Methods for Intelligent Vehicles: Exploring Innovative Sensor Solutions.
Certifiable and Risk - aware Localization, Perception, Control, and Mapping for Intelligent Vehicles: Ensuring Safety and Reliability
Safe Collision Avoidance for Unmaned Aerial Vehicle Swarms in Dense Environments: Strategies and Technologies.
Formal Methods for Safety Monitoring and Assessment of Intelligent Vehicles: Quantifying and Mitigating Risks.
Discussion Themes:
How to fuse GNSS/RTK, IMU, LiDAR, radar, cameras and HD maps for reliable urban centimeter‑level localization?
What low‑cost sensor/algorithm combos best balance accuracy and deployability?
How can AI methods be used in estimation while ensuring interpretability and fail‑safe behavior?
What validation and monitoring strategies are needed to certify AI components in safety‑critical perception stacks?
Venue:
Schedule
INVITED SPEAKERS
Presentation Topic: Urban GNSS RTK/INS Integrated System: A Perspective on using FGO + AI for GNSS
Prof. Li-Ta Hsu (Senior Member, IEEE) received the B.S. and Ph.D. degrees in aeronautics and astronautics from National Cheng Kung University, Tainan, Taiwan, in 2007 and 2013, respectively. He is currently an Associate Professor with the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong. He is with Limin Endowed Young Scholar in Aerospace Navigation. He was a Visiting Researcher with the Faculty of Engineering, University College London, London, U.K., and Tokyo University of Marine Science and Technology, Tokyo, Japan, in 2012 and 2013, respectively. He was selected as a Japan Society for the Promotion of Sciences Postdoctoral Fellow with the Institute of Industrial Science, The University of Tokyo, and worked from 2014 to 2016. He is an Associate Fellow of the Royal Institute of Navigation. Prof. Hsu is a member of ION and serves as a member of the Editorial Board and reviewer in professional journals related to GNSS. In 2013, he received the Student Paper Award and two Best Presentation Awards from the Institute of Navigation (ION).
Presentation Topic: Can We Navigate Reliably Using Radar?
Prof. Timothy Barfoot (University of Toronto Robotics Institute) works in the area of autonomy for mobile robots targeting a variety of applications. He is interested in developing methods (localization, mapping, planning, control) to allow robots to operate over long periods of time in large-scale, unstructured, three-dimensional environments, using rich onboard sensing (e.g., cameras, laser, radar) and computation. Tim holds a BASc (Aerospace Major) from the UofT Engineering Science program and a PhD from UofT in robotics. He took up his academic position in May 2007, after spending four years at MDA Robotics (builder of the well-known Canadarm space manipulators), where he developed autonomous vehicle navigation technologies for both planetary rovers and terrestrial applications such as underground mining. He was also a Visiting Professor at the University of Oxford in 2013 and worked as Director of Autonomous Systems at Apple in California in 2017-9. Tim is an IEEE Fellow, held a Canada Research Chair (Tier 2), was an Early Researcher Awardee in the Province of Ontario, and has received two paper awards at the IEEE International Conference on Robotics and Automation (ICRA 2010, 2021). He is currently the Associate Director of the UofT Robotics Institute, Faculty Affiliate of the Vector Institute, and co-Faculty Advisor of UofT's self-driving car team that won the SAE Autodrive competition five years in a row. He sits on the Editorial Boards of the International Journal of Robotics Research (IJRR) and IEEE Transactions on Field Robotics (T-FR), the Foundation Board of Robotics: Science and Systems (RSS), and served as the General Chair of Field and Service Robotics (FSR) 2015, which was held in Toronto. He is the author of a book, State Estimation for Robotics (2017, 2024), which is free to download from his webpage (http://asrl.utias.utoronto.ca/~tdb).
Presentation Topic: Efficient and Robust LiDAR-centric Localization for Autonomous Drones
Prof. Fu Zhang received his B.E. degree in Automation from the University of Science and Technology of China (USTC), Hefei, Anhui, China, in 2011, and the Ph.D. degree in Controls from the University of California, Berkeley, CA, USA, in 2015. His Ph.D. work focused on self-calibration and control of micro rate-integrating gyro sensors. In 2016,
Prof. Zhang shifted his research to the design and control of Unmanned Aerial Vehicles (UAVs) as a Research Assistant Professor in the Robotics Institute of the Hong Kong University of Science and Technology (HKUST). He joined the Department of Mechanical Engineering, the University of Hong Kong (HKU), as an Assistant Professor from Aug 2018. He was promoted to an Associate Professor with tenure in 2024. Prof. Fu Zhang leads the Mechatronics and Robotic Systems (MaRS) Laboratory at HKU, which specializes in cutting-edge robotics and autonomous systems. (HKU MaRS Lab) And his current research interests are on robotics and controls, with focus on UAV design, navigation, control, and lidar-based simultaneous localization and mapping.
Presentation Topic: Advancing Autonomous Vehicles with Neuromorphic Event-based Vision
Dr. Yi Zhou is a Professor at School of Robotics, Hunan University (HNU). He received his B.Sc. degree in Aircraft Manufacturing and Engineering from Beijing University of Aeronautics and Astronautics (BUAA), China in 2012. He obtained his PhD from the Research School of Engineering at the Australian National University (ANU) at 2019. He was awarded the NCCR Fellowship Award for his research on event based vision in 2017 by the Swiss National Science Foundation through the National Center of Competence in Research (NCCR) Robotics. He directed the SLAM group at Motovis (Shanghai) Ltd. from 2018 to 2019. He was an postdoc research fellow at the robotic institute, HKUST from 2019 to 2021, where he led the research on neuromorphic event-based perception and navigation. His research interests include Visual Odometry/SLAM (Simultaneous Localization and Mapping), geometry problems in computer vision and dynamic vision sensors.
Presentation Topic: Implementing a Connected and Automated Vehicle Trial in Australian Urban Traffic: Key Learnings
Dr. Mao Shan received his PhD from The University of Sydney in 2014. Mao was a Research Associate at the Australian Centre for Robotics at The University of Sydney from 2014 to 2016, and a Research Fellow at Nanyang Technological University, Singapore, from 2016 to 2017. Currently Mao is a Senior Research Fellow at the Australian Centre for Robotics. His research interests include autonomous systems, V2X communication, cooperative perception, sensor fusion.
Presentation Topic: Enabling Both Safety and Performance for Difficult Dynamics
Prof. Kousik earned his B.S. in Mechanical Engineering from Georgia Tech in 2014, followed by M.S. and Ph.D. degrees in Mechanical Engineering from the University of Michigan in 2020. He completed a postdoctoral fellowship at Stanford University from 2020 to 2022, working with Grace Gao and Marco Pavone. Currently, he is the Principal Investigator of the Safe Robotics Lab (Safe Robotics Lab @ GT). Prof. Kousik focuses on bridging the gap between theory and application to ensure that formal safety guarantees are practical for real-world systems. His work incorporates techniques from control theory, robot motion planning, and data-driven design. Additionally, Prof. Kousik is an avid reader of science fiction, particularly works by authors from underrepresented populations in STEM.
Accepted Workshop Posters
OccCylindrical: Multi-Modal Fusion with Cylindrical Representation for 3D Semantic Occupancy Prediction
Zhenxing Ming, Julie Stephany Berrio, Mao Shan, Yaoqi Huang, Hongyu Lyu, Nguyen Hoang Khoi Tran, Tzu-Yun Tseng, Stewart Worrall.
Australian Centre for Robotics, The University of Sydney, Australia
Qijun Qin¹, Ziqi Zhang¹, Yihan Zhong¹, Feng Huang¹, Xikun Liu¹, Runzhi Hu¹, Hang Chen¹, Wei Hu¹, Dongzhe Su¹, Jun Zhang², Hoi-Fung Ng², Weisong Wen³
1.Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University
2.Hong Kong Applied Science and Technology Research Institute (ASTRI)
3.Nanyang Technological University
Yuanyuan Zhang, Yingying Wang, Baoshan Song, Weisong Wen
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University
Yixin Gao¹, Weisong Wen¹, Xikun Liu¹, Feng Huang¹, Jiahao Hu¹, Dongzhe Su²
1.Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University
2.Hong Kong Applied Science and Technology Research Institute (ASTRI)
Risheng Xu¹, Linyao Yang⁴, Yuanqi Qin⁴, Xiao Wang², Changyin Sun³
1. School of Artificial Intelligence, Anhui University, Hefei, China
2. National Key Laboratory of Optoelectronic Information Acquisition and Protection Technology, Hefei, China
3. Anhui University, Hefei, China
4. Zhijiang Laboratory, Hangzhou, China
ShouguoYin¹, Quancheng Du², Lingxi Li³, Xiao Wang¹⁴, Changyin Sun⁵
1. School of Artificial Intelligence, Anhui University, Hefei, China
2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
3. School of Electrical and Computer Engineering. Purdue University. Indiana. IN, USA
4. National Key Laboratory of Optoelectronic Information Acquisition and Protection Technology. Hefei, China
5. Anhui University, Hefei, China
Xiaoting Yao, Zhiyuan Xi, Peijun Ye, Zhiling Wang, Xiao Wang¹*
School of Artificial Intelligence, Anhui University, Hefei 230031, China
Hongbo Zhao¹, Zeqi Yin¹, Dawei Wang², Chen Zhuang¹
School of Electronics and Information, Beihang University
School of Electronics and Information, Northwestern Polytechnical University
ORGANIZERS
Hong Kong Polytechnic University
German Aerospace Center
Siemens AG
Beihang University
Shandong University
Technical University of Munich
Hong Kong Polytechnic University
Hong Kong Polytechnic University
Hong Kong Polytechnic University
Contact Us
If you have any questions, please contact organizers at:
Name: Dr. Feng HUANG
E-mail: darren-f.huang@connect.polyu.hk
Address: PQ502, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong
