Vehicle Edge Computing

Vehicle Edge Computing (VEC) focuses on processing data from vehicles at the network edge, near the source, to reduce latency and bandwidth demands in applications like autonomous driving and intelligent transportation systems. Current research emphasizes efficient task offloading strategies, often employing deep reinforcement learning and federated learning to optimize resource allocation, personalize services (e.g., route planning), and ensure data privacy while mitigating challenges like network dynamics and Byzantine attacks. This field is crucial for enabling real-time responsiveness and safety-critical applications in connected and autonomous vehicles, impacting both the development of advanced AI algorithms and the design of future transportation infrastructure.