Improved YOLOv3 Object Classification in Intelligent Transportation System
School of Automation, Southeast University（东南大学自动化学院）；Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University（东南大学复杂工程系统测量与控制教育部重点实验室）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）
The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years. In particular, the driver detection is still a challenging problem which is conductive to supervising traffic order and maintaining public safety. In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver and passenger and form a one-to-one correspondence between vehicles and drivers. The proposed model and contrast experiment are conducted on our self-build traffic driver's face database. The effectiveness of our proposed algorithm is validated by extensive experiments and verified under various complex highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.
Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
Wuhan University of Technology（武汉理工大学）；Nanyang Technological University（南洋理工大学）；Sony AI（索尼人工智能研究院）；Singapore University of Technology and Design（新加坡科技设计大学）；Guangdong University of Technology（广东工业大学）
Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
School of Energy and Electrical Engineering, Chang’an University, Xi’an, China（能源与电气工程学院，长安大学，西安，中国）；School of Information Engineering, Chang’an University, Xi’an, China（信息工程学院，长安大学，西安，中国）
A Novel Cloud-Based Diffusion-Guided Hybrid Model for High-Accuracy Accident Detection in Intelligent Transportation Systems
Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus（比特学院电子与电子工程系）；The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算机与信息系统学院量子云计算与分布式系统实验室）；qCLOUDS Laboratory（qCLOUDS实验室）
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems
Department of Computer Science, Iowa State University, Ames, IA, USA（Iowa State University）；Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, USA（Iowa State University）
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
TECNALIA, Basque Research & Technology Alliance (BRTA)（泰克纳利亚，巴斯克研究与技术联盟（BRTA））；University of Las Palmas de Gran Canaria（拉斯帕尔马斯大学）；National Technical University of Athens（雅典国家技术大学）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））
CogRail: Benchmarking VLMs in Cognitive Intrusion Perception for Intelligent Railway Transportation Systems
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；Signal & Communication Research Institute, China Academy of Railway Sciences（信号与通信研究所，中国铁路科学研究院）；Beijing Huairou Academy of Parallel Sensing（北京怀柔平行感知院）；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院，北京交通大学）；State Key Lab of Intelligent Transportation Systems, School of Transportation Science and Engineering, Beihang University（智能交通系统国家重点实验室，交通科学与工程学院，北京航空航天大学）
