Intelligent Transportation System
Intelligent Transportation Systems (ITS) aim to improve efficiency and safety in transportation networks through data-driven technologies. Current research heavily focuses on leveraging artificial intelligence, particularly deep learning models like YOLO for object detection and graph neural networks for traffic forecasting and optimization, along with federated learning to address data privacy concerns in distributed systems. These advancements are enabling real-time applications such as improved traffic signal control, autonomous vehicle navigation, and enhanced traffic monitoring, with significant implications for urban planning, resource management, and overall transportation sustainability.
Papers
YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems
Mujadded Al Rabbani Alif
Extralonger: Toward a Unified Perspective of Spatial-Temporal Factors for Extra-Long-Term Traffic Forecasting
Zhiwei Zhang, Shaojun E, Fandong Meng, Jie Zhou, Wenjuan Han
Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors
Longfei Han, Qiuyu Xu, Klaus Kefferpütz, Gordon Elger, Jürgen Beyerer
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
Abdelaziz Amara korba, Abdelwahab Boualouache, Yacine Ghamri-Doudane
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
Wenhua Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
Complementary Fusion of Deep Network and Tree Model for ETA Prediction
YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang