Test and Training Data Generation for Object Recognition in the Railway Domain-Reference-Cited by-同舟云学术

Test and Training Data Generation for Object Recognition in the Railway Domain

Published:2023 Issue: Volume: Page:5-16
ISSN:0302-9743
Container-title:Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops
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Author:

Grossmann Jürgen^ORCID,Grube Nicolas^ORCID,Kharma Sami^ORCID,Knoblauch Dorian^ORCID,Krajewski Roman^ORCID,Kucheiko Mariia,Wiesbrock Hans-Werner^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-26236-4_1

Reference22 articles.

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2. Czarnecki, K.: Operational world model ontology for automated driving systems - part 1: road structure. Technical report, Waterloo Intelligent Systems Engineering (WISE) Lab, University of Waterloo (2018). https://doi.org/10.13140/RG.2.2.15521.30568

3. Czarnecki, K.: Operational world model ontology for automated driving systems - part 2: road users, animals, other obstacles, and environmental conditions. Technical report, Waterloo Intelligent Systems Engineering (WISE) Lab, University of Waterloo (2018). https://doi.org/10.13140/RG.2.2.11327.00165

4. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)

5. Fremont, D.J., et al.: Scenic: a language for scenario specification and data generation (2020). https://doi.org/10.48550/ARXIV.2010.06580

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