I. Title: 

Sensor fusion based ambient understanding for wider and unstructured environment

II. Abstract:

Ambient understanding in unstructured environments is a critical topic in the field of autonomous systems. This wider measurement and understanding work is widely applied to autonomous driving, smart construction, and robotic excavation. However, many challenges had been existed in developing and optimizing perception systems with respect to severe weather conditions, heavy dust environment, and the high cost of traditional LiDAR sensors. This speech focuses on the recent transition from LiDAR-based systems to 4D Radar and Camera fusion technologies. It introduces our advanced algorithms for 3D object detection and SLAM that utilize 4D Radar to overcome environmental limitations in both autonomous driving and excavator operations. Also, it covers the related research and recent projects especially on the sensor fusion systems developed by the team of Intelligent Systems Laboratory (ISLab), Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Korea.

III. Short bio:

Prof. Kanghyun Jo graduated from Busan National University and an MS and PhD. with Computer Vision from Osaka University. Since 1998, he has been a professor in the Dept. of Electrical, Electronics, and Computer Engineering at the University of Ulsan, where he leads various industry-academic collaborations and international academic activities.

His work focuses on real‑time computer vision and deep learning for object recognition, action and facial expression analysis, and safety surveillance in autonomous driving. He pioneered lightweight deep learning models on embedded systems and multi‑sensor fusion techniques for precise vehicle and pedestrian tracking. He has also led major projects such as the development of a plastic waste recognition model, an early childhood plays evaluation platform, a digital mathematics play app, and digital twin platforms for wastewater treatment plants and campus infrastructure. 

Currently, he develops AI algorithms for citywide traffic and infrastructure data analysis, and constructs digital twin models integrating real-time sensor and image data to optimize traffic management and emergency response. In addition, he is conducting research on unmanned and large-scaled manufacturing environments by fusing 4D radar and camera sensors, establishing key technologies for smart city and intelligent industrial automation.

He had been involved in organizing many international conferences under IEEE IES(industrial electronics society) where he has been worked as AdCom Members and committee members, for example, ISIE, IECON, INDIN, ICIT, HSI, and IWIS. Among them, he recently has been managing the conferences, to name a few IEEE ISIE2024, HSI2025 and IWIS2025 as an organizing chair, in his town, Ulsan. He is also scheduled to host IWIS 2026 during Aug. 9 ~ 12, 2026.

Dr. Joshua Zhexue Huang, Academician of Russian Academy of Engineering, is a Distinguished Professor at the College of Computer Science and Software Engineering, Shenzhen University, and the Founding Director of the Big Data Institute. He currently serves as Chief Scientist of the ‌Greater Bay Area Ascend Computing Application Innovation Research Institute‌ at the Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen), and as Deputy Director of the ‌National Engineering Laboratory for Big Data System Computing Technology‌.

A pioneer in data mining, Prof. Huang is renowned for developing influential clustering algorithms, including ‌k-modes, fuzzy k-modes, k-prototypes, and w-k-means‌, which are widely cited and integrated into commercial software. His research spans ‌business intelligence, data mining, and big data analytics‌, with extensive industry consulting experience across Australia, Hong Kong, Taiwan, and mainland China.

Dr. Huang holds a PhD from the Royal Institute of Technology (Sweden) and has published over ‌400 research papers‌ with ‌20,000+ citations‌. In 2006, he received the inaugural ‌PAKDD Most Influential Paper Award‌. A globally recognized leader, he has chaired multiple international conferences and is listed among ‌Stanford University’s top 2% career-impact scientists‌ and ‌Elsevier’s Highly Cited Chinese Researchers‌.