Chen Hua — accepted AI-conference papers

2024

CMGFA: A BEV Segmentation Model Based on Cross-Modal Group-Mix Attention Feature Aggregator

Xinkai Kuang, Runxin Niu, Chen Hua, Chunmao Jiang, Hui Zhu, Ziyu Chen +1

RA-L 2024

Bird's eye view (BEV) segmentation map is a recent development in autonomous driving that provides effective environmental information, such as drivable areas and lane dividers. Most of the existing methods use cameras and LiDAR as inputs for segmentation and the fusion of different modalities is ac

Cited by 2 Source ↗Scholar ↗

2023

Efficient and High-Fidelity Mobility Prediction for Unmanned Ground Vehicles Based on Gaussian Sampled Terrain and Enhanced Neural Network

Chen Hua, Runxin Niu, Chunmao Jiang, Biao Yu, Hui Zhu, Bichun Li

RA-L 2023

To avoid unmanned ground vehicles being obstructed by deformed terrain in off-road, effective vehicle mobility analysis is required. However, the computational complexity of existing mobility analysis methods, such as discrete element analysis, poses significant challenges when applied to large-scal

Cited by 1 Source ↗Scholar ↗