Zusammenfassung
Currently, neural networks can only be used to a limited extent in safety-critical applications, because their quality and safety properties are difficult or impossible to prove due to their inherent complexity and their black-box character. The project CertML aims to improve and verify the safety of machine learning based systems.
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References
Bilanz zur Verkehrssicherheitslage 2021in Berlin (Feb 2022). URL: https://www.berlin.de/polizei/polizeimeldungen/2022/pressemitteilung.1178821.php
Abbiegeassistenten ab sofort Pflicht, Bundesregierung (Jul 2022). URL: https://www.bundesregierung.de/breg-de/themen/europa/ abbiegeassistenten-pflicht-2060086
F. Machida: N-version machine learning models for safety critical systems, In: 2019, 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSNW), 2019, pp. 48–51. https://doi.org/10.1109/DSN-W.2019.00017.
T. Michalke, C. Wüst, D. Feng, M. Dolgov, C. Gläser, F. Timm: Where can i drive? a system approach: Deep ego corridor estimation for robust automated driving, In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), IEEE, 2021, pp. 1565–1571.
D. Gupta: Image segmentation keras: Implementation of segnet, fcn, unet, pspnet and other models in keras, https://github.com/divamgupta/image-segmentation-keras (2022).
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Jass, P., Abukhashab, H., Thomas, C. et al. CertML: Initial Steps Towards Using N-Version Neural Networks for Improving AI Safety. Datenschutz Datensich 47, 483–486 (2023). https://doi.org/10.1007/s11623-023-1803-z
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DOI: https://doi.org/10.1007/s11623-023-1803-z