We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidirectional bipartite graphs that effectively model the complex interactions within traffic scenes in real-time. By integrating Graph Neural Networks (GNNs) with high-order multi-aggregation strategies, we significantly enhance the modeling of traffic scene dynamics, providing a more accurate and detailed analysis of these interactions. Additionally, we incorporate inductive learning techniques inspired by the GraphSAGE framework, enabling our model to adapt to new and unseen traffic scenarios without the need for retraining, thus ensuring robust generalization. Through extensive experiments on the ROAD and ROAD Waymo datasets, we establish a comprehensive baseline for further developments, demonstrating the potential of our method in accurately capturing traffic behavior. Our results emphasize the value of high-order statistical moments and feature-gated attention mechanisms in improving traffic behavior analysis, laying the groundwork for advancing autonomous driving technologies. Our source code is available at: https://github.com/Addy-1998/High_Order_Graphs
YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion
Sun Yat-sen University（中山大学）；Wuhan University of Technology（武汉理工大学）；Southeast University（东南大学）；Hong Kong Baptist University（香港浸会大学）；City University of Hong Kong（香港城市大学）
Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions
Automatic Control Research Institute, China Nuclear Power Engineering Co., Ltd.（中国核电工程有限公司自动控制研究院）；CNNC Engineering Research Center for Fuel Reprocessing（中核集团燃料后处理工程研究中心）；Chinese Aeronautical Establishment, Aviation Industry Corporation of China, Ltd（中国航空工业集团有限公司中国航空研究院）
MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
Zhejiang University（浙江大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究所）
Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding
School of Computer Science and Engineering, and the MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University（计算机科学与工程学院，以及教育部先进计算机应用技术工程研究中心，北京航空航天大学）；School of Computer Science and Engineering, the School of Economics and Management, and the MIIT Key Laboratory of Data Intelligence and Management, Beihang University（计算机科学与工程学院，经济管理学院，以及工信部数据智能与管理重点实验室，北京航空航天大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中关村人工智能学院，中国人民大学）；DiDi Global Inc.（滴滴出行公司）
Peking University（北京大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；ETH Zürich（苏黎世联邦理工学院）；Tsinghua University（清华大学）
Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data
Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA（土木与环境工程系，卡内基梅隆大学）；Fujitsu Research of America, Pittsburgh, PA（富士通美国研究院）；Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA（Heinz信息学院）
