2021/04/15 by Laura von Rueden, Tim Wirtz, von Rueden, Laura +10
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #Data Management and Algorithms #FOS: Computer and information sciences #Traffic Prediction and Management Techniques #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2104.07538
Final version accepted at the International Conference on Pattern Recognition (ICPR). arXiv admin note: substantial text overlap with arXiv:2011.08008
arxiv created 2021/04/15 · openalex publication_date 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.