47 citations · 84 across the 10 of their papers we have counts for
11 papers
Reducing Multi-Horizon Photovoltaic Forecast Error through Tree-Anchored Expert Combination
Sun Ze, Zhou Liguo, Xu Yuqing +3
A strong direct tree model may remain the best standalone photovoltaic forecaster, yet a weighted combination can reduce its residual errors. We examine whether complementary tempo…
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Rui Song, Lingjuan Lyu, Wei Jiang +2
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by tr…
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
Rui Song, Runsheng Xu, Andreas Festag +2
Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly pr…
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving
Haichuan Li, Liguo Zhou, Alois Knoll
Autonomous driving has been an active area of research and development, with various strategies being explored for decision-making in autonomous vehicles. Rule-based systems, decis…
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation
Hanzhen Zhang, Liguo Zhou, Ruining Wang +1
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of ge…
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
Rui Song, Liguo Zhou, Lingjuan Lyu +2
Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying…
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks
Rui Song, Anupama Hegde, Numan Senel +2
Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and sche…
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan +2
Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrast…
Time-coded Spiking Fourier Transform in Neuromorphic Hardware
Javier López-Randulfe, Nico Reeb, Negin Karimi +6
After several decades of continuously optimizing computing systems, the Moore's law is reaching itsend. However, there is an increasing demand for fast and efficient processing sys…
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
Marco Oliva, Soubarna Banik, Josip Josifovski +1
State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural kno…
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robu…