@article{PereiraThomas, author = {Pereira, Ana and Thomas, Carsten}, title = {Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems}, series = {Machine Learning and Knowledge Extraction}, volume = {2}, journal = {Machine Learning and Knowledge Extraction}, number = {4}, publisher = {MDPI}, issn = {2504-4990}, doi = {10.3390/make2040031}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-14570}, abstract = {Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.}, language = {en} } @article{JassThomas, author = {Jaß, Philipp and Thomas, Carsten}, title = {Using N-Version Architectures for Railway Segmentation with Deep Neural Networks}, series = {Machine Learning and Knowledge Extraction}, volume = {7}, journal = {Machine Learning and Knowledge Extraction}, number = {2}, editor = {Javaheri, Danial}, publisher = {MDPI}, issn = {2504-4990}, doi = {10.3390/make7020049}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-20867}, abstract = {Autonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. This paper investigates the application of N-version architectures to rail track detection using Deep Neural Networks (DNNs) as a means to improve the safety of machine learning (ML)-enabled perception systems. We combine three different neural network architectures (WCID, VGG16-UNet, MobileNet-SegNet) in a 3M1I configuration. In this configuration, we apply two fusion methods to increase accuracy and to enable error detection: Maximum Confidence Voting (MCV), combining the DNN predictions at the image level, and Pixel Majority Voting (PMV), a novel approach for combining the predictions at the pixel level. In addition, we implement a new method for evaluating and combining prediction confidence values in the N-version architecture during runtime. We adjust the overall prediction confidence according to the conformity of all individual predictions, which is not possible with an individual network. Our results show that the N-version architecture not only enables a detection of erroneous predictions by utilizing those adjusted confidence values, but it can also partially improve the predictions by using the PMV combination algorithm. This work emphasizes the importance of model diversity and appropriate thresholds for an accurate assessment of prediction safety. These approaches can significantly improve the practical applicability of ML-based systems in safety-critical domains such as rail transportation.}, subject = {Machine learning}, language = {en} }