11 citations · 18 across the 8 of their papers we have counts for
9 papers
AVERT-VLN: Abstention-aware Visual Error Recovery and Training for Vision-and-Language Navigation
Minrui Liu, Jingke Wang, Yuehao Huang +4
Deploying vision-and-language navigation (VLN) agents in unseen environments remains challenging because unfamiliar layouts and visual conditions can cause execution to go off trac…
MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence
Haoran Wen, Wenfu Wang, Kunsong Shi +15
General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate precise actions. Vision-language-ac…
DriveStack-VLA: Render-Teacher Alignment for BEV-Based DeepStack Vision-Language-Action Model
Jingke Wang, Zhenru Zhao, Shuangming Lei +8
Vision-Language-Action driving models convert a pretrained Vision-Language Model into a driving policy, allowing them to use world knowledge and follow language guidances. However,…
SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation
Ruoyu Wang, Jingke Wang, Yukai Ma +5
Recently, world models have made significant progress in enhancing end-to-end driving systems through both future situation forecasting and improved scene understanding. However, e…
LADY: Linear Attention for Autonomous Driving Efficiency without Transformers
Jihao Huang, Xi Xia, Zhiyuan Li +4
End-to-end autonomous driving has emerged as a promising paradigm. However, state-of-the-art methods rely heavily on Transformer architectures. The inherent quadratic complexity of…
Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
Mingyi Wang, Jingke Wang, Tengju Ye +2
Recent breakthroughs in large language models (LLMs) have not only advanced natural language processing but also inspired their application in domains with structurally similar pro…
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Tengju Ye, Wei Jing, Chunyong Hu +11
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such…
Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory
Yunkai Wang, Dongkun Zhang, Jingke Wang +3
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of…
Learning hierarchical behavior and motion planning for autonomous driving
Jingke Wang, Yue Wang, Dongkun Zhang +2
Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve th…