Towards Map-Based Validation of Semantic Segmentation Masks
This addresses safety and robustness issues in autonomous driving by providing a validation method, though it appears incremental as it builds on existing validation techniques.
The paper tackles the problem of validating semantic segmentation masks for autonomous driving by using street map data as additional a-priori knowledge, with initial results showing that prediction errors can be uncovered through this map-based approach.
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.