Papers
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Total Citations
2
H-Index
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About
Zhan Jie is a rising researcher at the forefront of autonomous driving perception, with a primary focus on enhancing LiDAR reliability in adverse weather conditions. His most notable contribution, "Towards Visibility Estimation and Noise-Distribution-Based Defogging for LiDAR in Autonomous Driving" (2024), pioneers a novel approach to point cloud degradation caused by fog. By establishing a direct correlation between fog attenuation coefficients and visibility, Jie’s work introduces a noise-distribution-based defogging method that significantly improves sensor robustness—a critical step toward safer self-driving systems. Though early in his career, his research has already garnered attention, with his key paper accumulating citations that underscore its relevance to the robotics and intelligent vehicle communities. Jie’s work bridges the gap between environmental physics and practical sensor correction, offering a systematic framework for visibility estimation that could redefine how autonomous systems handle fog, rain, and other low-visibility scenarios. As the demand for all-weather autonomous navigation grows, Zhan Jie’s contributions position him as a promising innovator in LiDAR perception and environmental resilience.
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Top Papers
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