Abstract
The perception of the autonomous driving software of the FS223, a low-level sensor fusion of Lidar and Camera data requires the use of a neural network for image classification. To keep the neural network up to date with updates in the training data, we introduce a Continuous Integration (CI) pipeline to re-train the network. The network is then automatically validated and integrated into the code base of the autonomous system. The introduction of proper CI methods in these high-speed embedded software applications is an application of state-of-the-art MLOps techniques that aim to provide rapid generation of production-ready models. It further serves the purpose of professionalizing the otherwise script-based software production, which is re-done almost completely every year as the teams change from one year to the next.
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Notes
- 1.
SAE international is a standards developing organization for engineers, see: https://www.sae.org/.
- 2.
GET racing participates annually in the events since 2005, see https://www.get-racing.de/.
- 3.
GitLab is a DevOps platform that aims to assist software developers with project management, versioning, etc. See https://about.gitlab.com/company/.
- 4.
Coral offers hardware and software platforms for embedded systems. Our accelerator: https://coral.ai/products/pcie-accelerator.
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Steffen, B., Zohren, J., Pazarci, U., Kullmann, F., Weißenfels, H. (2024). Continuous Integration of Neural Networks in Autonomous Systems. In: Kofroň, J., Margaria, T., Seceleanu, C. (eds) Engineering of Computer-Based Systems. ECBS 2023. Lecture Notes in Computer Science, vol 14390. Springer, Cham. https://doi.org/10.1007/978-3-031-49252-5_21
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