Yicheng Gao

Nanjing University of Science and Technology

Papers

Total Citations

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About

Yicheng Gao is a researcher whose work lies at the intersection of computer vision, autonomous driving, and intelligent transportation systems. His most-cited paper, "An Illumination-Invariant Nonparametric Model for Urban Road Detection" (2018, 14 citations), introduces a novel approach to robust road detection under challenging lighting conditions. In this work, Gao proposes a shadow removal method that yields an illumination-invariant image representation by fusing data from a monocular camera and a single-line LIDAR sensor. This nonparametric model significantly improves the reliability of urban road detection, a critical task for autonomous vehicle navigation. Gao's contributions address a fundamental challenge in perception systems: maintaining performance across varying environmental conditions. His research has implications for advancing the safety and robustness of self-driving cars, particularly in complex urban settings. With a focused portfolio that demonstrates technical depth in sensor fusion and image processing, Gao is establishing himself as an emerging voice in the field of autonomous driving perception.