Towards Map-Based Validation of Semantic Segmentation Masks

2020/11/03 by Laura von Rueden, von Rueden, Laura, Tim Wirtz +7 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.AI #cs.CV

paper · pdf · doi:10.48550/arxiv.2011.08008

arxiv created 2020/11/26 · arxiv updated 2020/11/30

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.

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