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.
Abstract. The growing complexity of systems and the continuing pressure on reductions in development time and cost can only be tackled if an effective and tool-supported requirement management process is established. In our view, requirements management is not just part of requirements engineering. Instead, in principle, it is part of all phases of the entire system life-cycle, ranging from market analysis and top-level customer requirements to system maintenance, final shutdown, and removal. In this paper, we report on some methodological experiences gained from introducing a tool-supported requirements management process at DaimlerChrysler Aerospace Airbus. First, we will describe our view of the main process steps in requirements management and the typical roles that people play in a tool-supported requirements management process. Then, we will introduce the basic parts of a common requirements management information model and discuss how a general common information model helps to support the main process steps in requirements management.
MOTIVATION
Multi-robot systems are often static and pre-configured during the design time of their software. Emerging cooperation between unknown robots is still rare and limited. Such cooperation might be basic like sharing sensor data or complex like conjoined motion planning and acting. Robots should be able to detect other robots and their abilities during runtime. When cooperation seems to be possible and beneficial, it should be initiated autonomously. A centralized cloud control shall be avoided. Using software patterns belonging to service-oriented architectures, the robots are able to discover other robots and their abilities during runtime. These abilities are implemented as services and described by their interfaces. Composition of services can be done easily and flexibly. The software patterns originally belonging to cloud computing could be successfully adopted to decentralized multi-robot systems. The developed concept allows autonomous systems to cooperate flexibly and to compose multi-robot systems during runtime.
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