Classification of Driver Intentions at Roundabouts

Moritz Sackmann, Henrik Bey, Ulrich Hofmann, Jörn Thielecke

Abstract

Classification of other drivers’ intentions is an important requirement for automated driving. We present two methods to estimate whether a driver leaves a roundabout. The first, like many other approaches to this problem, requires training data of the specific roundabout to extract typical behavior patterns. Afterwards, these patterns are used for classification of other drivers’ intentions. The second approach generates typical behavior patterns from a precise map. Consequently, no training data is required and classification can be performed on arbitrary roundabouts as long as a map is available. Experimental evaluation on a real world dataset of 266 trajectories shows that the performance of the map-based approach is comparable to the data-driven approach. The classification result can be used in a later stage for behavior planning of automated vehicles or driver assistance systems.

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Paper Citation


in Harvard Style

Sackmann M., Bey H., Hofmann U. and Thielecke J. (2020). Classification of Driver Intentions at Roundabouts.In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-419-0, pages 301-311. DOI: 10.5220/0009344603010311


in Bibtex Style

@conference{vehits20,
author={Moritz Sackmann and Henrik Bey and Ulrich Hofmann and Jörn Thielecke},
title={Classification of Driver Intentions at Roundabouts},
booktitle={Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2020},
pages={301-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009344603010311},
isbn={978-989-758-419-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Classification of Driver Intentions at Roundabouts
SN - 978-989-758-419-0
AU - Sackmann M.
AU - Bey H.
AU - Hofmann U.
AU - Thielecke J.
PY - 2020
SP - 301
EP - 311
DO - 10.5220/0009344603010311