Yiwen Feng
Papers
1
Total Citations
4
H-Index
1
About
Yiwen Feng is a rising researcher in the field of autonomous systems and perception, with a focus on leveraging low-cost, commercial-off-the-shelf (COTS) millimeter-wave radar for 3D environmental understanding. Their key contributions center on overcoming the limitations of traditional computer vision in adverse conditions—such as low light, fog, or heavy rain—by developing novel radar-based methods for object detection and spatial mapping. Feng’s most cited work, "3D Bounding Box Estimation Based on COTS mmWave Radar via Moving Scanning" (2024), introduces an innovative approach to estimating object boundaries using moving radar scans, achieving robust 3D bounding box estimation without reliance on expensive LiDAR or high-resolution cameras. This work has already garnered 4 citations within its first year, signaling growing interest in their practical, scalable solutions for intelligent driving and robotic navigation. By addressing critical gaps in radar-based perception, Feng is paving the way for safer, more reliable autonomous systems in real-world, sensor-challenged environments. Their research holds promise for advancing cost-effective perception technologies, making them a notable emerging voice in the autonomous vehicle and robotics communities.
Research Focus
Key Achievements
Top Papers
- 1