Trajectory prediction and intelligent RSU handover for connected vehicles using deep sequential and ensemble learning

Accurate trajectory prediction and proactive Road-Side Unit (RSU) handover are essential for maintaining seamless connectivity and low-latency Multi-Edge Computing (MEC) based services in Cooperative Adaptive Cruise Control (CACC) systems. Conventionally, mobility forecasting and migration decisions are treated as separate problems, neglecting their inherent dependence, thus leading to premature or delayed handovers. In this work an integrated learning-based pipeline is introduced that first predicts the vehicle mobility using deep sequential models and the leverages these predictions to trigger intelligent RSU migrations. We employ a LSTM network to learn temporal dynamics from the large-scale FHWA dataset and generate multi-step displacement predictions over short horizons. These predictions are then used to infer future coverage boundaries and connectivity risks. Further, we model RSU migration as a binary-class classification task and train Random Forest (RF) and Multilayer Perceptron (MLP) as classifiers on engineered mobility features, such as velocities deltas, cumulative drift and distance to RSU markers. To handle skewed migration labels, we incorporate fallback heuristics and class-balanced strategies. Experiments on the 500,000 real vehicular samples reveal that the RF-based migration model achieves up to 73% accuracy. Meanwhile LSTM maintains stable short-horizon displacement accuracy with competitive Average Displacement Error (ADE) and Final Displacement Error (FDE) scores. Together, the models enable anticipatory RSU handover decisions that outperform naive threshold-based methods.