ProFusion2 — towards a Modular, Robust and Reliable Fusion Architecture for Automotive Environment Perception
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This publication focuses on a modular architecture for sensor data fusion regarding to research work of common interest related to sensors and sensor data fusion. This architecture will be based on an extended environment model and representation, consisting of a set of common data structures for sensor, object and situation refinement data and algorithms as well as the corresponding models. The aim of such research is to contribute to a measurable enhancement of the output performance provided by multi-sensor systems in terms of actual availability, reliability, accuracy and precision of the perception results. In this connection, investigations towards fusion concepts and paradigms, such as ‘redundant’ and ‘complementary’, as well as ‘early’ and track-based sensor data fusion approaches, are conducted, in order to significantly enhance the overall performance of the perception system.
Keywordssensor data fusion environment perception fusion framework environment modeling early fusion multi level fusion grid-based fusion track-based fusion fusion feedback ProFusion2
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