8 papers
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory
Haisheng Su, Zongdai Liu, Xin Jin +13
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
Zhenjie Yang, Yilin Chai, Xiaosong Jia +5
End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driving scenarios, particularly rare maneu…
Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
Xiaosong Jia, Yuqian Shao, Zhenjie Yang +3
With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop eva…
LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces
Muyun Jiang, Shuailei Zhang, Zhenjie Yang +9
Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interf…
DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving
Haisheng Su, Wei Wu, Feixiang Song +3
Recent advances towards End-to-End Autonomous Driving (E2E-AD) have been often devoted on integrating modular designs into a unified framework for joint optimization e.g. UniAD, wh…
EgoFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving
Haisheng Su, Wei Wu, Zhenjie Yang +1
Current End-to-End Autonomous Driving (E2E-AD) methods resort to unifying modular designs for various tasks (e.g. perception, prediction and planning). Although optimized with a fu…