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Cognitive automobiles consist of a set of algorithms that cover a wide range of processing levels: from low-level image acquisition and feature extraction up to situation assessment and decision making. The modules implementing them are naturally characterized by decreasing data rates at higher levels, because raw data is discarded after evaluation, and increasing processing intervals, as knowledge based levels require longer computation times. The architecture presented in this papers offers a method to interchange information with different temporal resolutions liberally among modules with distinct cycle times and realtime demands. It allows effortless buffering of raw data for subsequent data fusion and verification, facilitating innovative processing structures. The paper is completed by measurements demonstrating the achieved real-time capabilities on our selected hardware architecture.