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Street-Map Based Validation of Semantic Segmentation in Autonomous Driving

 
: Rüden, Laura von; Wirtz, Tim; Hueger, Fabian; Schneider, Jan David; Piatkowski, Nico; Bauckhage, Christian

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Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings : 10-15 January 2021, Milan, Italy, Virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-8809-6
ISBN: 978-1-7281-8808-9
S.10203-10210
International Conference on Pattern Recognition (ICPR) <25, 2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01-S18038B; ML2R
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()
measurement; roads; semantics; production; manual; prediction algorithms; robustness

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.

: http://publica.fraunhofer.de/dokumente/N-636314.html