Making the Most of Predictions: Data Offloading for Autonomous Driving

Abstract

Autonomous driving struggles to anticipate events due to the limitations of first-person perception. By offloading sensor data, environmental awareness can be shared between road users and greatly improve perception of obstacles, leading to safer and more versatile trips. To this end, vehicular networks require stable, high-performance connectivity across heterogeneous Radio Access Technologies (RAT), yet existing selection mechanisms react to degradation rather than anticipating it. We present a predictive quality-of-service (QoS) framework that jointly forecasts latency and packet delivery rate using dual-output recurrent neural networks, enabling proactive RAT selection across 5G NR, C-V2X, and DSRC. Where most frameworks tend to stop at link continuity, our proposal operates as a hierarchical closed-loop system that also maximizes throughput: a model-based selector chooses the optimal RAT, then a discrete-time Markov chain adapts packet size to channel conditions, then a queue manager enforces per-RAT capacity constraints, leading to new QoS measurements and online retraining, closing the loop. To study scalability, we introduce per-RAT contention models grounded in each RAT’s scheduling standard, and evaluate a two-pass contention-aware selection policy under increasing population sizes. Field trial data collected on an urban platform using V2X hardware across three simultaneous RATs validates the approach. The predictive QoS scheme achieves 75.2% of transmissions above 99% PDR versus 63.7% for the reactive baseline, with 40–60% fewer RAT handovers. Upscaled simulations show that contention-aware selection yields measurable reliability gains at medium and high densities, where shared-medium RATs face meaningful resource pressure. Dynamic packet sizing further improves per-vehicle throughput by up to 12.6% by exploiting high-reliability windows to increase payload.