Detecting Edge Cases from Trajectory Datasets Using Deep Learning Based Outlier Detection

Marcel Sonntag, Lennart Vater, Roman Vuskov, Lutz Eckstein

2024

Abstract

The biggest challenge to overcome for automated vehicles is to prove their safety, as these vehicles are solely responsible for the passengers’ safety. The scenario-based testing approach promises an efficient safety validation procedure by only testing the safety in relevant scenarios. An open question is how to select the relevant scenarios for testing. So-called edge cases are frequently named in the automated driving domain to be important scenarios for testing automated vehicles. However, it is not an easy task to define what an edge case is and to find and validate them. In this work, we present a novel data-driven approach to finding edge cases in trajectory datasets using deep learning-based outlier detection. We develop a method that calculates embeddings for driving scenarios in a two-stage process. In the dimensionally reduced embedding space, outliers represent potential edge cases. We apply the approach to the exiD dataset and find potential edge cases. For validation, we present the found potential edge cases to a group of experts. The experts validate that the approach is capable of detecting edge cases in trajectory datasets.

Paper Citation

in Harvard Style

Sonntag M., Vater L., Vuskov R. and Eckstein L. (2024). Detecting Edge Cases from Trajectory Datasets Using Deep Learning Based Outlier Detection. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 31-39. DOI: 10.5220/0012551600003702

in Bibtex Style

@conference{vehits24,
author={Marcel Sonntag and Lennart Vater and Roman Vuskov and Lutz Eckstein},
title={Detecting Edge Cases from Trajectory Datasets Using Deep Learning Based Outlier Detection},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={31-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012551600003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Detecting Edge Cases from Trajectory Datasets Using Deep Learning Based Outlier Detection
SN - 978-989-758-703-0
AU - Sonntag M.
AU - Vater L.
AU - Vuskov R.
AU - Eckstein L.
PY - 2024
SP - 31
EP - 39
DO - 10.5220/0012551600003702
PB - SciTePress