# Towards Map-Based Validation of Semantic Segmentation Masks

arXiv: https://arxiv.org/abs/2011.08008 · HTML: https://mlsanity.com/paper/2011.08008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Street-Map Based Validation of Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2104.07538/) (2021)

## Abstract (arXiv)

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.