The introduction of Vehicular Ad-Hoc Networks (VANETs) enables great potential for improving road trafic ow and especially active safety applications such as cooperative adaptive cruise control (CACC). Such applications not only rely on continuous broadcast of vehicle state information (beacons) of all vehicles, but also have strict real-time requirements. Regarding automotive E/E architectures this continuous broadcasting adds heavy internal E/E data trafic that needs to be processed in real-time by Electronic Control Units (ECUs). In this work we address this issue by proposing a novel cluster-based message evaluation methodology to significantly reduce internal E/E network trafic by discarding irrelevant messages. The approach is only depending on information received over beacons. It combines a vehicle clustering strategy as well as network and vehicle state monitoring capabilities in order to correctly evaluate messages under real-time constraints. The proposed methodology is modeled inside an abstract ECU. It is evaluated by simulating a model-based CACC application under different trafic scenarios. It is shown that a significant reduction of messages is achievable, while still guaranteeing accident-free behavior of CACC.