Comparing the Machine Readability of Traffic Sign Pictograms in Austria and Germany

TL;DR

This study evaluates how well machine learning models can recognize traffic sign pictograms from Austria and Germany, highlighting challenges in generalization and implications for ADAS system design.

Contribution

It provides a comparative analysis of pictogram recognition across countries and introduces insights into the impact of design differences on machine readability.

Findings

Models perform poorly on unseen pictogram designs.

Design differences significantly affect recognition accuracy.

New pictograms designed for better human readability pose recognition challenges.

Abstract

We compare the machine readability of pictograms found on Austrian and German traffic signs. To that end, we train classification models on synthetic data sets and evaluate their classification accuracy in a controlled setting. In particular, we focus on differences between currently deployed pictograms in the two countries, and a set of new pictograms designed to increase human readability. Besides other results, we find that machine-learning models generalize poorly to data sets with pictogram designs they have not been trained on. We conclude that manufacturers of advanced driver-assistance systems (ADAS) must take special care to properly address small visual differences between current and newly designed traffic sign pictograms, as well as between pictograms from different countries.