Special Issue of COMPUTERS & ELECTRICAL ENGINEERING ELSEVIER | Faculty of Civil and Environmental Engineering at the Gdańsk University of Technology

We invite you to submit scientific papers to the special issue of COMPUTERS & ELECTRICAL ENGINEERING ELSEVIER.

The special issue has been launched by:

• Ph.D. Pei Xiao (University of Surrey, United Kingdom),
• Prof. Alex Alexandridis (University of West Attica, Greece),
• Ph.D. Eng. Paweł Burdziakowski (Department of Geodesy WILIŚ, Gdańsk University of Technology).

Title of the special issue:

Multisensor Image Fusion in the Internet of Vehicles for Autonomous Systems (VSI-ifas))

Content of the special issue:

Evolutionary Autonomous Systems (AS) refer to technological frameworks or mechanisms that can adapt, learn, and improve their functionality over time without direct human intervention. Autonomous systems rely on various sensors (such as cameras, lidars, radars, etc.) to perceive and gather information from their environment. The evolutionary nature of multisensor image fusion involves a continuous learning process, where fusion algorithms or systems adapt and enhance based on new data, experiences, and feedback. This evolving capability is crucial in many applications where accurate and reliable fusion information is essential for decision-making and analysis. Moreover, the Internet of Vehicles (IoV) constitutes a dynamic ecosystem that connects vehicles, infrastructure, and the Internet. It combines cutting-edge technologies with AS to create intelligent networks that optimize and provide a range of innovative services. IoV relies on multiple sensors, communication devices, and data analytics for real-time information exchange, enabling autonomous decision-making. The evolution of IoV is closely related to the development of AS. In IoV-based AS systems, multisensor image fusion enables a more robust and accurate understanding of the environment by mitigating individual sensor limitations. With further IoV development, the integration of 5G and future wireless communication technologies will further improve connectivity, enabling faster and more reliable data exchange between autonomous systems.

Data fusion from multiple sensors in IoV contributes to better object detection, tracking, and localization. This comprehensive perception is crucial for ensuring the safety and performance of autonomous systems in real-time, considering various environmental factors and potential threats. Furthermore, integrating multisensor image fusion in IoV systems also presents challenges such as data synchronization, alignment, calibration, and computational complexity. Overcoming these challenges requires advanced algorithms, signal processing techniques, and sensor fusion methodologies to effectively combine data from different sources, while ensuring accuracy and real-time performance. Continuous progress in this field will be crucial for further development and implementation of AS systems in the future.

Topics include, but are not limited to, the following:

• Advanced methodologies and algorithms in multisensor image fusion techniques to enhance the accuracy of perception in IoV AS systems.
• Deep learning models for multisensor image fusion: object detection, classification, and scene understanding by AS systems.
• Real-time multisensor fusion algorithms in IoV-AS applications for privacy and security.
• Edge computing in multisensory fusion for faster and more flexible decision-making by IoV-based AS systems.
• Multisensor image fusion using artificial intelligence (AI) in IoV solutions for AS in the development of smart cities.
• Multisensory image fusion algorithm for autonomous dead reckoning systems with IoV support.
• New approaches to challenges and opportunities in multisensor image fusion in 5G networks and beyond for next-generation IoV-AS systems.
• Multisensor image fusion in IoV-based autonomous control switching mechanisms for smart device applications.
• AI trends in intelligent multisensor-based image fusion for intelligent industrial disturbance mitigation of AS systems.

Useful links:

► Journal Computers and Electrical Engineering | Journal | ScienceDirect.com by Elsevier (SC. 7.1, IF 4.3)
► Information about the journal COMPUTERS & ELECTRICAL ENGINEERING - Journal - MOST Wiedzy.
► Information about the special issue Call for papers - Computers and Electrical Engineering | ScienceDirect.com by Elsevier