Ensuring validation for highly automated driving poses significant obstacles to the widespread adoption of highly automated vehicles. Scenario-based testing offers a potential solution by reducing the homologation effort required for these systems. However, a crucial prerequisite, yet unresolved, is the definition and reduction of the test space to a finite number of scenarios. To tackle this challenge, we propose an extension to a contrastive learning approach utilizing graphs to construct a meaningful embedding space. Our approach demonstrates the continuous mapping of scenes using scene-specific features and the formation of thematically similar clusters based on the resulting embeddings. Based on the found clusters, similar scenes could be identified in the subsequent test process, which can lead to a reduction in redundant test runs.
@article{arxiv.2309.09720,
title = {Traffic Scene Similarity: a Graph-based Contrastive Learning Approach},
author = {Maximilian Zipfl and Moritz Jarosch and J. Marius Zöllner},
journal= {arXiv preprint arXiv:2309.09720},
year = {2023}
}