8 citations · 8 across the 5 of their papers we have counts for
5 papers
S-squared-VLA: Decoupling Semantic and Spatial Streams in Vision-Language-Action Models for Autonomous Driving
Jianguo Yu, Rukang Wang, Duanfeng Chu +3
Vision-Language Models (VLMs) have demonstrated remarkable potential for high-level reasoning in autonomous driving, yet they fundamentally struggle to generate precise, low-level…
D-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
Renju Feng, Rukang Wang, Ning Xi +4
Traditional end-to-end autonomous driving frameworks frequently suffer from the "style-averaging" dilemma when trained on high-variance human demonstrations, yielding homogenized,…
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving
Jingyi Wang, Duanfeng Chu, Zejian Deng +3
To address the challenge of insufficient interactivity and behavioral diversity in autonomous driving decision-making, this paper proposes a Cognitive Hierarchical Agent for Reason…
Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
Guipeng Xin, Duanfeng Chu, Liping Lu +3
Trajectory prediction is crucial for autonomous driving as it aims to forecast the future movements of traffic participants. Traditional methods usually perform holistic inference…
MixDehazeNet : Mix Structure Block For Image Dehazing Network
LiPing Lu, Qian Xiong, DuanFeng Chu +1
Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. H…