From the 1 of 8 linked papers with an AI index.
6 papers · 1 filter
S-squared-VLA: Decoupling Semantic and Spatial Streams in Vision-Language-Action Models for Autonomous Driving
Jianguo Yu, Rukang Wang, Duanfeng Chu +3
The paper introduces S-squared-VLA, a vision‑language‑action model that separates semantic intent reasoning from spatial geometry processing to improve low‑level control for autono…
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,…
ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving
Renju Feng, Ning Xi, Duanfeng Chu +6
This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods…
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…
EPN: An Ego Vehicle Planning-Informed Network for Target Trajectory Prediction
Saiqian Peng, Duanfeng Chu, Guanjie Li +2
Trajectory prediction plays a crucial role in improving the safety of autonomous vehicles. However, due to the highly dynamic and multimodal nature of the task, accurately predicti…
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…