Xiaolong Zhang | Intelliget Vehicle | Best Researcher Award

Assoc Prof Dr. Xiaolong Zhang | Intelliget Vehicle | Best Researcher Award

Fuzhou University | China

Author Profile

Scopus

πŸ‘¨β€πŸ« Summary

Dr. Xiaolong Zhang is an Associate Professor at the School of Mechanical Engineering and Automation, Fuzhou University, China. His expertise lies in artificial intelligence, robotics, and vehicle dynamics. He has authored over 20 academic papers and holds 26 patents, showcasing his strong contributions to intelligent systems and control technologies.

πŸŽ“ Education

He received his Ph.D. in Mechanical Engineering from Huazhong University of Science and Technology, Wuhan, in 2021, focusing on advanced control and robotic systems.

πŸ’Ό Professional Experience

Dr. Zhang has led and contributed to 8 research projects and 3 industry consultancy assignments. He is actively collaborating with Geely Auto on smart tire technologies, bridging the gap between academia and industry.

πŸ“ˆ Academic Citations & Publications

He has 20 publications in reputed SCI and Scopus-indexed journals, with a citation index of 12. He also holds one editorial appointment, reflecting his recognition in the academic community.

πŸ› οΈ Technical Skills

His core competencies include robot dynamic systems, motion planning, reinforcement learning, vehicle control algorithms, and AI-based autonomous navigation.

πŸ‘¨β€πŸ« Teaching Experience

Dr. Zhang teaches undergraduate and graduate courses in robotics, control theory, and intelligent vehicle systems, and supervises student research in related domains.

πŸ”¬ Research Interests

His main interests revolve around artificial intelligence, robotics, autonomous vehicles, and reinforcement learning, with a strong focus on real-time dynamic control and decision-making systems.

πŸš— Key Contributions

He proposed a novel continuous lane-changing decision and planning method using hierarchical reinforcement learning (HRL). The approach separates high-level decision-making and low-level motion planning, using a coupled training strategy to ensure smooth speed and steering control, improving the convergence and performance of HRL in autonomous driving.

🀝 Memberships & Affiliations

He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Chinese Mechanical Engineering Society (CMES).

πŸ“–Publications

A cascaded strategy-based hierarchical reinforcement learning algorithm for lane change decision-making
  • Year: 2025