13 papers
AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence
Wei-Bin Kou, Jingreng Lei, Guangxu Zhu +1
Mixture of Experts (MoE) are increasingly deployed over wireless cloud-edge networks, as a single edge device lacks sufficient resources to host large-scale models locally. In this…
The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments
Jiayi Chen, Weiting Ou, Guangxu Zhu
WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolat…
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
Jiayi Chen, Shuai Wang, Guangxu Zhu +1
Large foundation models enable powerful reasoning for autonomous systems, but mapping semantic intent to reliable real-time control remains challenging. Existing approaches either…
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Federated Learning (FL) enables collaborative training of autonomous driving (AD) models across distributed vehicles while preserving data privacy. However, FL encounters critical…
Statistic-Augmented, Decoupled MoE Routing and Aggregating in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Jingreng Lei +3
Autonomous driving (AD) scenarios are inherently complex and diverse, posing significant challenges for a single deep learning model to effectively cover all possible conditions, s…
Adverse Weather-Independent Framework Towards Autonomous Driving Perception through Temporal Correlation and Unfolded Regularization
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +5
Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc.…