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Generalized Learning Vector Quantization methods are a powerful and robust approach for classification tasks. They compare incoming samples with representative prototypes for each target class. While prototypes are physically interpretable, they do not account for changes in the environment. We propose a novel framework for the incorporation of context information into prototype generation. We can model dependencies in a modular way ranging from polynomials to neural networks. Evaluations on artificial and real-world datasets show an increase in performance and meaningful prototype adaptations.

This paper focuses on the actuation strategy of an active driving simulator and its validation using experimental driving data. The simulator combines the simulation of both the road characteristics and the vehicle dynamics into a single architecture. The goal is to combine actual road excitation signals with imposed vehicle movements to create a realistic driving experience.

This paper describes the application of machine learning techniques and an associated assurance case for a safety-relevant chassis control system. The method applied during the assurance process is described including the sources of evidence and deviations from previous ISO 26262 based approaches. The paper highlights how the choice of machine learning approach supports the assurance case, especially regarding the inherent explainability of the algorithm and its robustness to minor input changes. In addition, the challenges that arise if applying more complex machine learning technique, for example in the domain of automated driving, are also discussed. The main contribution of the paper is the demonstration of an assurance approach for machine learning for a comparatively simple function. This allowed the authors to develop a convincing assurance case, whilst identifying pragmatic considerations in the application of machine learning for safety-relevant functions.