Time Series Segmentation for Driving Scenario Detection with Fully Convolutional Networks

Philip Elspas, Yannick Klose, Simon Isele, Johannes Bach, Eric Sax

2021

Abstract

Leveraging measurement data for Advanced Driver Assistant Systems and Automated Driving Systems requires reliable meta information about covered driving scenarios. With domain expertise, rule-based detectors can be a scalable way to detect scenarios in large amounts of recorded data. However, rules might struggle with noisy data, large number of variations or corner cases and might miss valuable scenarios of interest. Finding missing scenarios manually is challenging and hardly scalable. Therefore we suggest to complement rule-based scenario detection with a data-driven approach. In this work rule-based detections are used as labels to train Fully Convolutional Networks (FCN) in a weakly supervised setup. Experiments show, that FCNs generalize well and identify additional scenarios of interest. The main contribution of this paper is twofold: First, the scenario detection is formulated as a time series segmentation problem and the capability to learn a meaningful scenario detection is demonstrated. Secondly, we show how the disagreement between the rule-based method and the learned detection method can be analyzed to find wrong or missing detections. We conclude, that the FCNs provide a scalable way to assess the quality of a rule based scenario detection without the need of large amounts of ground truth infromation.

Paper Citation

in Harvard Style

Elspas P., Klose Y., Isele S., Bach J. and Sax E. (2021). Time Series Segmentation for Driving Scenario Detection with Fully Convolutional Networks. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 56-64. DOI: 10.5220/0010404700560064

in Bibtex Style

@conference{vehits21,
author={Philip Elspas and Yannick Klose and Simon Isele and Johannes Bach and Eric Sax},
title={Time Series Segmentation for Driving Scenario Detection with Fully Convolutional Networks},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={56-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010404700560064},
isbn={978-989-758-513-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Time Series Segmentation for Driving Scenario Detection with Fully Convolutional Networks
SN - 978-989-758-513-5
AU - Elspas P.
AU - Klose Y.
AU - Isele S.
AU - Bach J.
AU - Sax E.
PY - 2021
SP - 56
EP - 64
DO - 10.5220/0010404700560064