7 citations · 13 across the 12 of their papers we have counts for
12 papers
Can We Trust AI in 6G? Verifiable and Auditable AI-Driven Trustworthy Wireless Networks
Genze Jiang, Yizhou Huang, Kezhi Wang
Mobile network operators are increasingly exploring the use of artificial intelligence (AI) to automate network tasks such as cell selection and mobility management. Current assura…
From Traditional Automation to Embodied Wireless Intelligence: Vision-Language-Action Empowered Physics-Aware Communication Networks
Genze Jiang, Kezhi Wang, Xiaomin Chen +1
Wireless network automation has progressed from rule-based self-organising networks (SON) to data-driven optimisation, yet existing systems remain fundamentally disembodied. They a…
BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry
Zuo Fei, Kezhi Wang, Xiaomin Chen +1
Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language mode…
FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration
Yizhou Huang, Gengze Jiang, Yihua Cheng +1
Accurate trajectory prediction is vital for safe autonomous driving, yet existing approaches struggle to balance modeling power and computational efficiency. Attention-based archit…
Agentic AI Empowered Intent-Based Networking for 6G
Genze Jiang, Kezhi Wang, Xiaomin Chen +1
The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executabl…
Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction
Yizhou Huang, Yihua Cheng, Kezhi Wang
Understanding human motion is crucial for accurate pedestrian trajectory prediction. Conventional methods typically rely on supervised learning, where ground-truth labels are direc…
Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM
Yizhou Huang, Yihua Cheng, Kezhi Wang
Motion prediction is crucial for autonomous driving, as it enables accurate forecasting of future vehicle trajectories based on historical inputs. This paper introduces Trajectory…
Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models
Yizhou Huang, Yihua Cheng, Kezhi Wang
Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this…
Stochastic Planning for ASV Navigation Using Satellite Images
Yizhou Huang, Hamza Dugmag, Timothy D. Barfoot +1
Autonomous surface vessels (ASV) represent a promising technology to automate water-quality monitoring of lakes. In this work, we use satellite images as a coarse map and plan samp…
Continual Model-Based Reinforcement Learning with Hypernetworks
Yizhou Huang, Kevin Xie, Homanga Bharadhwaj +1
Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model. In many instances of MBRL a…
Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition
Keenan Burnett, Jingxing Qian, Xintong Du +14
The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock…
Learning of Coordination Policies for Robotic Swarms
Qiyang Li, Xintong Du, Yizhou Huang +2
Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents. Biological swarms can achieve collective intelli…