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Towards Map-Based Validation of Semantic Segmentation Masks
Fraunhofer-Gesellschaft
2020
,
Rüden, Laura von
,
Wirtz, Tim
,
Hueger, Fabian
,
Schneider, Jan David
,
Bauckhage, Christian
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.