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 Sensing 2. Generating Synthetic Data for Intelligent Transportation System 3. Real-time and Streaming Analytics 4. Video Data Encoding and Decoding 5. Traffic Data Flow Prediction 6. Digital Twin System 7. Multi-modal Fusion-based Object Detection 8. Data Cross-Modal Alignment and Fusion Strategies
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Community Case Study
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Community Case Study
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy and Practice Reviews
Review
Study Protocol
Systematic Review
Technology and Code
Keywords: Multi-modal data fusion, autonomous driving, object detection, Internet of Vehicles, wireless sensing.
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
