Selected aspects important from an applied point of view to the fusion of collective vehicle data

It can be considered as a current advancement observed within the automotive industry that vehicles are more and more tightly interconnected. By utilizing the data provided by the manifold onboard sensors the vehicles can exchange considerable amounts of perceived environmental data with each other or a common fusion center. This way comprehensive, richly detailed, and by now unmatched up-to-date maps can be deduced by aggregating the received data. Subsequently, these maps can be utilized as an additional, virtual, ultra-longrange sensor for supporting next generation driver assistance or piloted driving functions. The focus of the research on this topic has usually concerned the key challenge, data aggregation; this means how to fuse multiple sensor readings afflicted with imperfections and uncertainties. However, to achieve generalized, reliable, precise, and computationally feasible aggregates the full data acquisition and processing chain needs to be considered holistically as affirmed by our research. At this, highly interesting, however, usually neglected challenges arise which this paper is dedicated to. We illuminate the for a real-world automotive application crucial aspects of sensor data fusion, such as coping with the temporal decay of measurements, the precise vehicle localization by utilizing commercially viable sensors, the generalized storage of different types of sensor data, and the definition of generalized acquisition and processing chains to provide a fast adaptation to new kinds of input data. Furthermore, we present how these aspects lead to an adaptable, efficient, and accurate data fusion of collective vehicle data concerning both areal and point-shaped/complex data.