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Authors: Christian Reichenbächer 1 ; 2 ; Maximilian Rasch 3 ; 2 ; Zafer Kayatas 3 ; 2 ; Florian Wirthmüller 4 ; 2 ; Jochen Hipp 2 ; Thao Dang 5 and Oliver Bringmann 1

Affiliations: 1 Department of Computer Science, University of Tübingen, Tübingen, Germany ; 2 Mercedes-Benz AG, Sindelfingen, Germany ; 3 Institute of Technical Mechanics and Vehicle Dynamics, Brandenburg University of Technology, Cottbus, Germany ; 4 Institute of Databases and Information Systems (DBIS), Ulm University, Ulm, Germany ; 5 Faculty Computer Science and Engineering, Esslingen University of Applied Sciences, Esslingen, Germany

Keyword(s): Autonomous Vehicles and Automated Driving, Analytics for Intelligent Transportation, Traffic and Vehicle Data Collection and Processing, Vehicle Environment Perception, Pattern Recognition for Vehicles.

Abstract: Scenario-based approaches for the validation of highly automated driving functions are based on the search for safety-critical characteristics of driving scenarios using software-in-the-loop simulations. This search requires information about the shape and probability of scenarios in real-world traffic. The scope of this work is to develop a method that identifies predefined logical driving scenarios in field data, so that this information can be derived subsequently. More precisely, a suitable approach is developed, implemented and validated using a traffic scenario as an example. The presented methodology is based on qualitative modelling of scenarios, which can be detected in abstracted field data. The abstraction is achieved by using universal elements of an ontology represented by a domain model. Already published approaches for such an abstraction are discussed and concretised with regard to the given application. By examining a first set of test data, it is shown that the deve loped method is a suitable approach for the identification of further driving scenarios. (More)

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Paper citation in several formats:
Reichenbächer, C.; Rasch, M.; Kayatas, Z.; Wirthmüller, F.; Hipp, J.; Dang, T. and Bringmann, O. (2022). Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-573-9; ISSN 2184-495X, SciTePress, pages 134-142. DOI: 10.5220/0011081500003191

@conference{vehits22,
author={Christian Reichenbächer. and Maximilian Rasch. and Zafer Kayatas. and Florian Wirthmüller. and Jochen Hipp. and Thao Dang. and Oliver Bringmann.},
title={Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2022},
pages={134-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011081500003191},
isbn={978-989-758-573-9},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems
SN - 978-989-758-573-9
IS - 2184-495X
AU - Reichenbächer, C.
AU - Rasch, M.
AU - Kayatas, Z.
AU - Wirthmüller, F.
AU - Hipp, J.
AU - Dang, T.
AU - Bringmann, O.
PY - 2022
SP - 134
EP - 142
DO - 10.5220/0011081500003191
PB - SciTePress