Multi-Modal Sensing Data for Intelligent Transportation

The rapid advancement of sensing technologies has opened new possibilities for intelligent transportation systems (ITS), enabling enhanced traffic management, safety, and environmental sustainability. Multi-modal sensing data, including inputs from cameras, radar, LiDAR, and GPS, allows for a more comprehensive understanding of transportation dynamics. By integrating these diverse data streams, ITS can provide real-time decision support, improve vehicle autonomy, and optimize infrastructure usage. However, challenges remain in effectively fusing and processing large volumes of multi-modal data, ensuring accuracy, and addressing privacy concerns. This Research Topic invites contributions that explore innovative approaches to data fusion, machine learning models, real-time analytics, and ethical issues surrounding multi-modal sensing for intelligent transportation. We welcome studies that contribute to the design, deployment, and scalability of intelligent systems for urban mobility, road safety, and smart city infrastructure.This article collection aims to address these issues by exploring novel techniques for data fusion, machine learning, and real-time analytics that can enhance the performance of intelligent transportation systems. By developing more accurate algorithms for sensor integration, improving vehicle-to-infrastructure communication, and ensuring scalability across various transportation modes, we can create systems that not only optimize traffic management but also contribute to safer, smarter cities. Additionally, ethical and privacy concerns must be addressed, ensuring that data usage in these systems is transparent, secure, and responsible.Topics of interest include (but are not limited to):1. Data Generation for Wireless Sensing2. Generating Synthetic Data for Intelligent Transportation System3. Real-time and Streaming Analytics4. Video Data Encoding and Decoding5. Traffic Data Flow Prediction6. Digital Twin System7. Multi-modal Fusion-based Object Detection8. Data Cross-Modal Alignment and Fusion Strategies Topic editors: Kaikai Deng, Ling Xing, Leiyang Xu, Yizong Wang, Dan Luo Source: https://www.frontiersin.org/research-topics/77518/multi-modal-sensing-data-for-intelligent-transportation