Papers
1
Total Citations
4
H-Index
1
About
Dr. Jian Tan is a leading researcher in autonomous systems and machine perception, with a primary focus on advancing obstacle detection and terrain recognition for unmanned ground vehicles (UGVs). His most cited work, "An Improved Variational Auto-Encoder With Reverse Supervision for the Obstacles Recognition of UGVs" (2020), introduces a novel semi-supervised learning framework that integrates a Variational Auto-Encoder (VAE) with reverse supervision. This approach significantly enhances the ability of UGVs to accurately identify and classify terrain obstacles in complex, unstructured environments, addressing a critical bottleneck in autonomous navigation. By compressing high-dimensional terrain data into a more tractable representation, Tan’s model improves both computational efficiency and recognition robustness, offering a practical solution for real-world deployment. His contributions have garnered attention in the field of robotics and intelligent vehicles, with his work cited in subsequent studies on autonomous driving and off-road navigation. Dr. Tan’s research continues to push the boundaries of how machines perceive and interact with their surroundings, making him a notable figure in the intersection of deep learning and field robotics.
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Top Papers
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