3 papers
cs.RO2025
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…
cs.RO2025
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…
cs.RO2024
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…