QoS-Aware Intelligent Model Selection for Autonomous Vehicles under Dynamic Network and Resource Conditions

Abstract

Autonomous-vehicle perception systems increasingly operate across heterogeneous compute and communication environments, where the most accurate model is not necessarily the model that provides the best end-to-end service quality. A high-capacity perception model may improve recognition quality while increasing inference time, CPU consumption, memory demand, and sensitivity to network conditions. This paper proposes a Quality-of-Service (QoS)-Aware Intelligent Model Selection framework that predicts the expected QoS of candidate perception models from model, network, and hardware context and dynamically selects a feasible model according to application priorities. The framework extends a network-aware autonomous-vehicle benchmarking foundation with a QoS prediction layer, multi-objective utility function, Pareto filtering, and a hysteresis-based switching controller. Three tabular prediction methods—linear regression, random forest, and gradient boosting—are compared. A synthetic dataset of 1,800 observations is generated from six candidate model profiles, five network conditions, three hardware conditions, and repeated measurements. The synthetic evaluation indicates that nonlinear predictors outperform the linear baseline and that QoS-aware selection can reduce mean response time by approximately 31.8–42.1% relative to an accuracy-only baseline in the modeled scenarios. Pareto and ablation analyses further show that latency and resource terms materially influence the selected model. Because no real AV testbed experiment has yet been conducted, these numerical results are explicitly treated as synthetic validation rather than empirical evidence. The paper therefore provides a reproducible research design and a publication-oriented methodology whose final claims should be revalidated with measured AV executions.