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Towards Map-Based Validation of Semantic Segmentation Masks

Paper presented at 37th International Conference on Machine Learning, ICML 2020, Workshop on AI for Autonomous Driving, AIAD 2020, 12-18 July 2020, Vienna, Austria
 
: Rüden, Laura von; Wirtz, Tim; Hueger, Fabian; Schneider, Jan David; Bauckhage, Christian

:
Volltext urn:nbn:de:0011-n-6363230 (4.1 MByte PDF)
MD5 Fingerprint: 853f64cc4c6df4291548fb8421479f39
Erstellt am: 29.6.2021


2020, 5 S.
International Conference on Machine Learning (ICML) <37, 2020, Online>
Workshop on AI for Autonomous Driving (AIAD) <2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01-S18038A; ML2R
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not only with given ground truth labels, but also with additional a-priori knowledge. In particular, we suggest to validate the drivable area in semantic segmentation masks using given street map data. We present first results, which indicate that prediction errors can be uncovered by map-based validation.

: http://publica.fraunhofer.de/dokumente/N-636323.html