Dr. Peng Zhi, Lanzhou University, China
Peng Zhi is a Ph.D. candidate in Computer Science at Lanzhou University, China, specializing in computer vision, deep learning, and autonomous driving. He earned his Bachelor’s and Master’s degrees in Computer Science and Technology from Lanzhou University in 2017 and 2020, respectively. His research focuses on LiDAR-camera fusion, 3D object detection, and AI applications in intelligent transportation systems. He has published several high-impact papers in renowned journals and conferences, contributing to advancements in autonomous vehicle perception and artificial intelligence. Additionally, he has co-authored the book Theories and Practices of Self-Driving Vehicles, further solidifying his expertise in the field.
Professional Profile:
ORCID
Summary of Suitability for the Best Researcher Award
Peng Zhi is a strong candidate for the Best Researcher Award, given his innovative contributions to computer vision, deep learning, and autonomous driving. As a Ph.D. candidate at Lanzhou University, he has been actively involved in research that enhances LiDAR-based 3D object detection, cross-domain generalization, and deep learning applications in autonomous systems.
🎓 Education
- Ph.D. in Computer Application Technology (2021 – Present)
Lanzhou University, Lanzhou, China
- Master’s in Computer System Architecture (2017 – 2020)
Lanzhou University, Lanzhou, China
- Bachelor’s in Computer Science and Technology (2013 – 2017)
Lanzhou University, Lanzhou, China
💼 Work Experience
- Ph.D. Candidate & Researcher (2021 – Present)
Lanzhou University, Lanzhou, China
- Conducts advanced research in computer vision, deep learning, and autonomous driving
- Publishes in top-tier journals and conferences
- Develops LiDAR and camera fusion models for 3D object detection
🏆 Achievements & Contributions
- Published Multiple Research Papers 📄 in top journals and conferences, including Tsinghua Science and Technology, Electronic Research Archive, and IEEE ITSC
- Author of a Book on Self-Driving Vehicles 📘 Theories and Practices of Self-Driving Vehicles (Elsevier, 2022)
- Developed DefDeN Model 🤖 A deformable denoising-based LiDAR and camera feature fusion model for 3D object detection
- Research on Autonomous Driving 🚗 Focused on boundary distribution estimation and cross-domain generalization for LiDAR-based 3D object detection
🏅 Awards & Honors
- Best Paper Award 🏆 at an International Conference on Intelligent Transportation Systems (ITSC)
- Outstanding Researcher Award 🎖️ at Lanzhou University for contributions to AI and autonomous driving
- National Scholarship 🏅 for academic excellence in computer science and AI research
Publication Top Notes:
Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments