Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain

Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays realized using Machine Learning (ML) and thus need to be developed and deployed reliably and efficiently. One important aspect to achieve this is to use an MLOps process for tackling improved reproducibility, traceability, collaboration, and continuous adaptation of a driverless operation to changing conditions. MLOps mixes ML application development and operation (Ops) and enables high frequency software releases and continuous innovation based on the feedback from operations. In this paper, we outline a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain. It integrates system engineering, safety assurance, and the ML life-cycle in a comprehensive workflow. We present the individual stages of the process and their interactions. Moreover, we describe relevant challenges to automate the different stages of the safe MLOps process

Artificial Neural Networks for Automated Quality Control of Textile Seams

Bahlmann C, Heidemann G, Ritter H. Artificial Neural Networks for Automated Quality Control of Textile Seams. Pattern Recognition. 1999;32(6):1049-1060.We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower coals in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an optimized feature set carrying the information relevant for classification is described. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd, All rights reserved

Potenziale für industrieübergreifendes Flottenlernen – KI-Mobilitätsdatenplattform zur Risikominimierung des automatisierten Fahrens

Ob in Transport, Logistik, im Individualverkehr oder im öffentlichen Nahverkehr – Verkehrsträger erreichen dank Künstlicher Intelligenz immer höhere Automatisierungsgrade. Automatisiertes Fahren kann helfen, die Verkehrssicherheit zu erhöhen, Verkehrsflüsse zu optimieren und Schadstoffemissionen zu reduzieren. Durch immer leistungsfähigere Verfahren der KI und des Maschinellen Lernens wird die Technologie des automatisierten Fahrens zunehmend verbessert, sodass sie in mehr als 99 Prozent der Situationen in Real-Tests funktioniert. Ein Restrisiko für mögliches Fehlverhalten tritt im Zusammenhang mit sogenannten Edge und Corner Cases (Grenz- und Übergangsfälle) auf. Für diese selten auftretenden Sonderfälle sind KI-Systeme unter Umständen nicht ausreichend trainiert und getestet. Um die Potenziale des industrieübergreifenden Flottenlernens zu erschließen, schlagen die Expertinnen und Experten der Arbeitsgruppe Mobilität und intelligente Verkehrssysteme der Plattform Lernende Systeme daher die Gründung einer gemeinschaftlichen KI-Mobilitätsdatenplattform vor. Diese Plattform soll den Austausch von Mobilitätsdaten ermöglichen und zur Risikominimierung beim automatisierten Fahren beitragen

A system for traffic sign detection, tracking, and recognition using color, shape and motion information

Abstract — This paper describes a computer vision based system for real-time robust traffic sign detection, tracking, and recognition. Such a framework is of major interest for driver assistance in an intelligent automotive cockpit environment. The proposed approach consists of two components. First, signs are detected using a set of Haar wavelet features obtained from Ada-Boost training. Compared to previously published approaches, our solution offers a generic, joint modeling of color and shape information without the need of tuning free parameters. Once detected, objects are efficiently tracked within a temporal information propagation framework. Second, classification is performed using Bayesian generative modeling. Making use of the tracking information, hypotheses are fused over multiple frames. Experiments show high detection and recognition accuracy and a frame rate of approximately 10 frames per second on a standard PC. I

On-line Handwriting Recognition with Support Vector Machines - A Kernel Approach

In this' contribution we describe a novel classification approach for on-line handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this' kernel Gaussian DTW (GDTW) ker- nel. This kernel approach haw' a main advantage over common HMM techniques. It does not assume a model for the generarive class conditional densities. Instead, it directly addresses the problem of discrimination by creating class boundaries and thus is' less sensitive to modeling assumptions. By incorporating DTW in the kernel function, general classification problems with variable-sized sequential data can be handled. In this respect the proposed method can be straightforwardly applied to all classification problems, where DTW gives a reasonable distance measure, e.g. speech recognition or genome processing. We show experiments with this' kernel approach on the UNIPEN handwriting data, achieving results' comparable to an HMMbased technique