Robust service migration for autonomous vehicles leveraging deep learning and cooperative V2V protocols

Autonomous vehicles are essentially data centers on wheels. Between self-driving algorithms and high-definition mapping, the demand for instant processing at the network edge is exploding. But here is the snag: keeping those data streams alive while a car is tearing down the highway at 100 km/h is still a nightmare. Most current solutions are stuck playing catch-up. They rely on "reactive" methods that only track memory changes after they happen, and they use a blunt, one-size-fits-all compression strategy. That clumsy approach tends to fall apart the moment memory usage spikes or the signal gets choppy, leading to frustrating lags and dropped connections. We propose a different path. Instead of scrambling to fix problems after they occur, our framework anticipates them. We built a Deep Temporal Predictive Filtering mechanism powered by Long Short-Term Memory (LSTM) networks. This setup predicts which memory pages are about to change and proactively ignores them, saving precious bandwidth. We also realized that treating all data the same is a mistake. A dense binary file behaves differently than a sparse sensor log, so we shouldn't compress them the same way. Our Content-Aware Semantic Compression engine identifies exactly what it's looking at and switches between Hybrid Autoencoders and Z-standard strategies to maximize efficiency. To coordinate all this, a Deep Reinforcement Learning agent monitors the network's unpredictability and picks the precise microsecond to execute the migration, backed up by a cooperative vehicle-to-vehicle transfer protocol. The payoff is clear. In our simulations, this cognitive approach cut wasted data transmission by 28% and slashed service downtime by 34% compared to standard adaptive methods. It keeps the digital handover stable, even when the physical world is moving fast.