Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model

Abstract

Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment. *This research is accomplished within the project "UNICARagil" (FKZ 16EMO0289). We acknowledge the financial support for the project by the Federal Ministry of Education and Research of Germany (BMBF).

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“…Furthermore, the mentioned collaborative world model through V2X (Sec. VIII-B2) can also serve as a means for uncertainty tolerance if participating cars can reduce their world model uncertainties through, e.g., a cloud-based fusion [176].…”

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