Optimizing Real-Time Cooperative Perception with Adaptive Model Pruning and Bandwidth Allocation

摘要

Cooperative perception (CP) is promising to enhance environmental awareness by sharing complementary information among connected vehicles. However, the challenges of resource constraints and edge heterogeneity in vehicular networks often lead to synchronization bottlenecks and degrade CP accuracy under strict delay requirements. This paper presents RT-Cooper, a real-time CP framework that jointly orchestrates computation and communication to optimize perception accuracy with delay guarantees in heterogeneous vehicular networks. We first introduce adaptive model pruning and bandwidth allocation into the CP pipeline to enable fine-grained resource coordination. Then, we formulate the Real-time Cooperative Perception (RCP) problem, which captures coupled computation-communication delays and system-wide synchronization bottlenecks, aiming to maximize perception accuracy under hard deadlines and resource constraints. To handle the closed-box and non-convex nature of the RCP objective, we construct an interpretable feature quality model derived from pruning analysis as a surrogate for accuracy. On this basis, we develop the Two-stage Alternating Resource Optimization (TARO) algorithm, which alternates between closed-form feasibility enforcement and heterogeneity-aware feature quality refinement with provable convergence. Extensive experiments on open-source benchmark and a real-world prototype validate the effectiveness of RT-Cooper in improving perception accuracy while achieving real-time guarantees.