Dear Colleagues,

In recent years, vehicular networks have become more and more heterogeneous, complex, and dynamic, making it difficult to satisfy communication requirements of next-generation (6G) applications, including ultralow latency, high throughput, high reliability, and high security. Among other features, future vehicles will be equipped with on-board sophisticated sensors (such as light detection and ranging (LiDAR), cameras, radars, etc.), which will transform vehicles from simple transportation means to powerful computing and networking hubs with intelligent capabilities. However, current vehicular networks are not designed to handle such large amounts of sensors’ data, thus calling for new technological innovations in the field.

In this context, machine learning (ML), in particular deep learning (DL), has emerged as a powerful approach to optimize the efficiency and adaptability of vehicle and wireless communication. Machine learning defines methods to acquire and analyze large volumes of data, extract features from sensory observations to find patterns and underlying structures, and/or compress and represent data to facilitate storage and dissemination for vehicular applications. At the same time, machine learning can be exploited to facilitate AI-enabled networks, self-optimize operations from scheduling and routing to resource management and handoff control, as well as to improve network security. However, how to adapt vehicular networks (and their respective communication protocols) to exploit machine learning approaches represents an open research question. Additionally, whether data handling has to be done on board (centralized processing), or delegated to more powerful external nodes (distributed processing), remains unknown.

This Special Issue encourages authors from academia and industry to submit new research results about novel machine learning approaches to enable AI-based vehicular networks toward 6G. 

Dr. Marco Giordani
Guest Editor

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• data-driven techniques in vehicular networks
• data-driven design of communication protocols for vehicular networks
• machine learning for sensor-based object detection and/or tracking
• machine learning for resource-efficient data dissemination
• machine learning for cross-modal feature extraction in automotive sensors
• machine learning for compression of automotive sensors’ observations
• machine learning for sensors’ data dissemination in vehicular networks
• machine learning for decision making, network control and management
• on-board vs. edge-assisted learning in vehicular networks
• 6G-based sensing solutions for autonomous cars
• new sensor technologies for vehicular networks