Intelligent Wheels: AI, Next-Generation Networks, and Federated Learning in the Era of Connected Autonomous Vehicles
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 25 December 2025
Special Issue Editors
Interests: wireless networks; vehicular networks; network security; fog/edge computing; 6G and beyond
Interests: ad hoc networks; network tomography; connected vehicles; vehicular social networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence of connected autonomous vehicles (CAVs) with cutting-edge advancements in artificial intelligence (AI), machine learning (ML), deep learning, and next-generation networks (NGNs), such as 5G and beyond, could revolutionize transportation. This Special Issue aims to explore the transformative potential and inherent challenges of integrating these sophisticated technologies to create intelligent, efficient, and safe autonomous mobility solutions. We welcome original research contributions that address novel methodologies, innovative applications, and critical considerations in leveraging AI/ML for optimizing NGN performance, enabling robust V2X communication, ensuring data privacy through techniques such as federated learning, and enhancing the overall capabilities of CAVs. This Special Issue hopes to enable researchers and practitioners to share insights and advancements at the intersection of these rapidly evolving fields.
Potential topics of interest include, but are not limited to, the following:
- AI/ML-driven resource allocation and management in NGNs for CAVs.
- Deep learning models for enhanced perception, sensor fusion, and decision making in CAVs.
- Federated learning architectures for privacy-preserving collaborative learning among CAVs and infrastructure.
- Novel NGN architectures (e.g., network slicing, MEC) to support ultra-reliable low-latency communication (URLLC) for safety-critical CAV applications.
- AI-based anomaly detection and cybersecurity measures for CAV networks.
- The optimization of V2X (vehicle-to-everything) communication protocols using ML techniques.
- Energy-efficient NGN solutions for sustainable CAV operations.
- Scalable and resilient AI models for real-time traffic prediction and management in CAV environments.
- The integration of digital twin technology with AI and NGNs for CAV testing and validation.
- The ethical considerations and societal impact of AI-powered NGNs in autonomous driving.
- Advanced driver-assistance systems (ADASs) leveraging AI and NGN capabilities.
- Quality-of-service (QoS) and quality-of-experience (QoE) provisioning in AI-enabled CAV networks.
- The use of big data analytics and ML to gain insights from CAV-generated data.
- Standardization efforts and interoperability challenges for AI and NGNs in the CAV ecosystem.
- Security and privacy challenges in federated learning for CAVs.
Dr. Anirudh Paranjothi
Dr. Mohammad Khan
Guest Editors
Manuscript Submission Information
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Keywords
- connected autonomous vehicles (CAVs)
- next-generation networks (5G/6G)
- artificial intelligence (AI)
- machine learning (ML)
- deep learning
- federated learning
- V2X communication
- intelligent transportation systems (ITSs)
- mobile edge computing (MEC) for CAVs
- cybersecurity in CAVs
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