Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning. [PDF]
Sci RepLiu Q, Ma Y.
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DRLO-VANET: a deep reinforcement learning-based offloading framework for low-latency and energy-efficient task execution in VANETs. [PDF]
Sci RepNeelima S, Sree SR, Ramakrishnaiah N.
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Machine learning for improved path loss prediction in urban vehicle-to-infrastructure communication systems. [PDF]
Front Artif IntellBen Ameur M +3 more
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Design and analysis of optically transparent high gain grid array antenna for vehicular communications. [PDF]
Sci RepJoy J A +4 more
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Reinforcement learning for dynamic speed control in connected and autonomous vehicles: A review of applications and challenges. [PDF]
iScienceAlhmiedat T +6 more
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Camera-Assisted Beam Selection via Unified Learning in 6G Vehicle-to-Infrastructure (V2I) Scenarios
IEEE AccessZahra Zarei +2 more
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A performance-optimized V2V task offloading framework for real-time vehicular communication. [PDF]
Sci RepQayyum T +4 more
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Optimizing Urban Traffic Efficiency and Safety via V2X: A Simulation Study Using the MOSAIC Platform. [PDF]
Sensors (Basel)Alupoaei SI, Caruntu CF.
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Advancements in accident-aware traffic management: a comprehensive review of V2X-based route optimization. [PDF]
Sci RepZohir HM +3 more
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IoT-Simulated Digital Twin with AI Traffic Signal Control for Real-Time Traffic Optimization in SUMO. [PDF]
Sensors (Basel)Ceapă VCD +7 more
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