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

IEEE Intelligent Vehicles Symposium, pp. 837–843

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.

Authors 6

1. Bastian Lampe Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074

2. Raphael van Kempen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074

3. Timo Woopen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074

4. Alexandru Kampmann Aachen Informatik 11 – Embedded Software

   RWTH Aachen University

   Affiliation as printed

   Informatik 11 - Embedded Software (i11), RWTH Aachen University, Aachen, Germany

   Informatik 11 - Embedded Software (i11), RWTH Aachen University,Aachen,Germany,52074

5. Bassam Alrifaee Aachen Informatik 11 – Embedded Software

   RWTH Aachen University

   Affiliation as printed

   Informatik 11 - Embedded Software (i11), RWTH Aachen University, Aachen, Germany

   Informatik 11 - Embedded Software (i11), RWTH Aachen University,Aachen,Germany,52074

6. Lutz Eckstein Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074