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

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 i...

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Bibliographic Details
Published in:arXiv.org 2020-05
Main Authors: Lampe, Bastian, Raphael van Kempen, Woopen, Timo, Kampmann, Alexandru, Alrifaee, Bassam, Eckstein, Lutz
Format: Article
Language:English
Subjects:
Automation
Cloud computing
Entropy (Information theory)
Environment models
Occupancy
Perception
Uncertainty
Vehicles
Online Access:Get full text
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Summary: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.
ISSN:2331-8422