Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

Assoc. Prof. Dr. Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

Kanagawa University | Japan

Assoc. Prof. Dr. Bin Zhang is an associate professor and principal investigator specializing in intelligent machines, robotics, and AI-driven systems. Education includes a Ph.D. and M.S. in Mechanical and Intelligent Systems Engineering from The University of Electro-Communications and a B.S. in Automation. Professional experience spans academia and industry, with roles in mechanical engineering education and robotics research. Research interests focus on service robots, autonomous navigation, human–robot interaction, perception, deep learning, and assistive systems. Research skills include robot design, control, SLAM, computer vision, sensor fusion, and machine learning. Awards and honors recognize excellence in robotics, AI applications, and assistive technologies. Overall, Assoc. Prof. Dr. Bin Zhang contributes impactful research and education advancing intelligent robotic systems for society.

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Featured Publications

A Study on Improving the Accuracy of Semantic Segmentation for Autonomous Driving
Computers, Materials & Continua, 2026 · Open Access
A Quantitative Diagnosis Method for Assessing the Severity of Intake Filter Blockage of Heavy-Duty Gas Turbines Based on the Fusion of Physical Mechanisms and Deep Learning
Applied Thermal Engineering, 2025

A Performance Diagnosis Method for Full Gas-Path Components of Heavy-Duty Gas Turbines Based on Model- and Data-Hybrid Drive

Proceedings of the Chinese Society of Electrical Engineering, 2025

An End-to-End Pillar Feature-Based Neural Network Improved by Attention Modules for Object Detection of Autonomous Vehicles

IEEJ Transactions on Electrical and Electronic Engineering, 2025

2D Mapping Considering Potential Occupancy Space of Mobile Objects for a Guide Dog Robot

IEEJ Transactions on Electronics, Information and Systems, 2025

Chao Li | Data Processing | Research Excellence Award

Dr. Chao Li | Data Processing | Research Excellence Award

Qingdao Technical College | China

Dr. Chao Li is an engineering scholar and lecturer whose work bridges professional education and applied research in advanced sensing technologies. He holds a doctoral degree in engineering and completed his undergraduate studies in industrial equipment and control engineering, where he built a strong foundation in intelligent systems. Since 2019, he has focused extensively on indoor mapping and positioning, integrating theoretical innovation with engineering-driven problem-solving. His research experience includes serving as a core contributor to multiple provincial key R&D initiatives and collaborations with major technology enterprises, where he helped develop applied solutions for real-world industrial environments. He has published several SCI-indexed journal articles as a first or corresponding author and holds an invention patent that reflects the practical impact of his work. In addition to research, he is dedicated to teaching and curriculum development in professional courses, promoting hands-on learning and interdisciplinary thinking. His academic achievements demonstrate a commitment to advancing positioning technologies, enhancing industry–academia collaboration, and addressing emerging challenges in smart manufacturing and intelligent monitoring. Looking ahead, he aims to continue deepening his contributions to indoor mapping and positioning, driving innovation that supports both scientific development and technological progress.

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Featured Publications

Li, C., Chai, W., Zhang, M., Sun, Z., Shao, G., & Li, Q. (2023). “A novel visual-aided method to enhance the inertial navigation system of an intelligent vehicle in indoor environments.” IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2023.3293884

Chai, W., Li, C., & Li, Q. (2023). “Multi-sensor fusion-based indoor single-track semantic map construction and localization.” IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2022.3226821

Li, C., Chai, W., Wu, Q., Li, J., Lin, F., Li, Z., & Li, Q. (2022). “A graph optimization enhanced indoor localization method.” In 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE). https://doi.org/10.1109/cipae55637.2022.00055

Li, C., Chai, W., Yang, X., & Li, Q. (2022). “Crowdsourcing-based indoor semantic map construction and localization using graph optimization.” Sensors. https://doi.org/10.3390/s22166263

Chai, W., Li, C., Zhang, M., Sun, Z., Yuan, H., Lin, F., & Li, Q. (2021). “An enhanced pedestrian visual-inertial SLAM system aided with vanishing point in indoor environments.” Sensors. https://doi.org/10.3390/s21227428