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  4. Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
 
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2021
Conference Paper
Title

Street-Map Based Validation of Semantic Segmentation in Autonomous Driving

Abstract
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current validation approaches mostly require ground truth data and are thus both cost-intensive and limited in their applicability. We propose to overcome these limitations by a model agnostic validation using a-priori knowledge from street maps. In particular, we show how to validate semantic segmentation masks and demonstrate the potential of our approach using OpenStreetMap. We introduce validation metrics that indicate false positive or negative road segments. Besides the validation approach, we present a method to correct the vehicle's GPS position so that a more accurate localization can be used for the street-map based validation. Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.
Author(s)
Rüden, Laura von  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Wirtz, Tim  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Hueger, Fabian
Volkswagen Group Automation
Schneider, Jan David
Volkswagen Group Automation
Piatkowski, Nico  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Bauckhage, Christian  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Mainwork
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings  
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Conference on Pattern Recognition (ICPR) 2021  
Open Access
DOI
10.1109/ICPR48806.2021.9413292
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • measurement

  • roads

  • semantics

  • production

  • manual

  • prediction algorithms

  • robustness

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