Street-Map Based Validation of Semantic Segmentation in Autonomous Driving

TL;DR

This paper introduces a street-map based validation method for semantic segmentation in autonomous driving, reducing reliance on ground truth data and improving safety validation through map-based metrics and GPS correction.

Contribution

It presents a novel, model-agnostic validation approach using street maps like OpenStreetMap, including new metrics and GPS correction for more accurate validation.

Findings

Validation metrics effectively identify false positives and negatives.

GPS correction improves localization accuracy for validation.

Quantitative results demonstrate error detection in semantic segmentation.

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.