Junyin Wang | Autonomous | Research Excellence Award

Mr. Junyin Wang | Autonomous | Research Excellence Award

Wuhan University of Technology | China

Mr. Junyin Wang is a researcher in intelligent transportation and computer vision, affiliated with Wuhan University of Technology, China. His work focuses on 3D perception for autonomous driving, with particular expertise in bird’s-eye-view (BEV) representation learning, camera–radar fusion, and knowledge distillation techniques for robust 3D object detection. He has authored 21 peer-reviewed publications, accumulating 40 citations with an h-index of 4, reflecting steady scholarly impact at an early career stage. Notable contributions include advanced dual-distillation and hybrid encoding frameworks published in leading venues such as Pattern Recognition and IEEE Transactions on Intelligent Transportation Systems. Wang has engaged in broad international and interdisciplinary collaboration, working with over 40 co-authors, indicating strong integration within the global research community. His research addresses critical challenges in perception reliability and sensor fusion, contributing to safer, more efficient intelligent transportation systems and supporting the societal transition toward autonomous and smart mobility solutions.

Citation Metrics (Scopus)

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Featured Publications

Xingxing You | Intelligent control | Editorial Board Member

Assist. Prof. Dr. Xingxing You | Intelligent control | Editorial Board Member

Assistant Professor | Sichuan University | China

Assist. Prof. Dr. Xingxing You is a developing researcher affiliated with Sichuan University, China, whose work spans advanced signal processing, intelligent control, and underwater imaging technologies. With 26 scientific publications, h-index 7and over 408 citations, the author demonstrates an emerging yet steadily growing influence in these fields. His research contributions include multi-level feature fusion strategies for perception-driven underwater image enhancement, advancing the reliability of visual sensing in complex aquatic environments, as well as novel critic-only self-learning optimal control methods for continuum robots operating under unknown disturbances, integrating extended state observer frameworks to elevate robustness and adaptability. These works reflect a broader expertise in machine learning–guided optimization, sensor fusion, and nonlinear dynamical systems, addressing real-world problems where conventional modeling is insufficient. Collaboration is a key dimension of his academic trajectory, with 55 co-authors across disciplines, indicating strong engagement within interdisciplinary research networks and an ability to participate effectively in multi-institutional scientific efforts. His research outcomes demonstrate relevance not only to academic communities working on robotics, automation, and digital signal processing, but also to domains such as marine engineering, environmental monitoring, and intelligent manufacturing. By focusing on interpretable enhancements, computational efficiency, and real-time control, his contributions help bridge theoretical advances and applied technological innovation. Overall, Xingxing You’s scholarly record showcases growing expertise, collaborative capacity, and a commitment to addressing technically challenging problems with practical societal implications.

Profiles: Scopus | ORCID

Featured Publications

1. Perception-driven underwater image enhancement via multi-level feature fusion. (2026). Digital Signal Processing: A Review Journal.

2. Critic-only based self learning optimal control for continuum robots with unknown disturbances via extended state observer. (2025). Nonlinear Dynamics.

Assist. Prof. Dr. Xingxing You’s work advances intelligent sensing and robust control systems, enabling more reliable robotic and imaging technologies in uncertain environments. His research contributes to global innovation by strengthening the scientific foundation for autonomous systems and enhancing their applications in marine exploration, environmental protection, and advanced robotics.