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Authors: Eugenia Rykova 1 ; 2 ; Juri Golanov 3 ; Jonas Vogt 3 ; Daniel Rau 3 and Horst Wieker 3

Affiliations: 1 University of Applied Sciences TH Wildau, Wildau, Germany ; 2 University of Eastern Finland, Joensuu, Finland ; 3 ITS Research Group FGVT htw saar, Saarbrücken, Germany

Keyword(s): Connected, Automated Driving, Traffic Data Evaluation, Automated Driving Handover.

Abstract: At the current stage of automated vehicle development, the control handover from the system to a human driver (and back) is inevitable. It is essential to distinguish between situations in which the handover is possible and in which it could be dangerous and is therefore highly undesirable. We evaluated traffic situations based on two modalities: own vehicle state and traffic objects. To assess the former, supervised machine learning was applied, reaching an accuracy of 80.3% and specificity of 77.8% with Multilayer perceptron Classification. Traffic objects data were subject to different clustering techniques. The final grouping was done according to manually elaborated rules, resulting in a range of situation complexity scores. Improving the discriminative power of vehicle state classification, including driver’s state and weather information, and predicting situation complexity are to be addressed in future research.

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Paper citation in several formats:
Rykova, E.; Golanov, J.; Vogt, J.; Rau, D. and Wieker, H. (2023). Traffic Data Evaluation for Automated Driving Handover Scenarios. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 125-134. DOI: 10.5220/0011599900003479

@conference{vehits23,
author={Eugenia Rykova. and Juri Golanov. and Jonas Vogt. and Daniel Rau. and Horst Wieker.},
title={Traffic Data Evaluation for Automated Driving Handover Scenarios},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={125-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011599900003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Traffic Data Evaluation for Automated Driving Handover Scenarios
SN - 978-989-758-652-1
IS - 2184-495X
AU - Rykova, E.
AU - Golanov, J.
AU - Vogt, J.
AU - Rau, D.
AU - Wieker, H.
PY - 2023
SP - 125
EP - 134
DO - 10.5220/0011599900003479
PB - SciTePress

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