# Street-Map Based Validation of Semantic Segmentation in Autonomous Driving

Source: [Street-Map Based Validation of Semantic Segmentation in Autonomous Driving](<https://www.sota2.com/research/paper/68fa69ce1e209131cea24323>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:08:38.583Z

Authors: Laura von Rueden, Tim Wirtz, Fabian Hueger, Jan David Schneider, Nico Piatkowski, Christian Bauckhage

Published: 2021-04-15T15:48:11.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.07538>)

[PDF](<https://arxiv.org/pdf/2104.07538.pdf>)