Medical Cyber Physical Systems of Systems (MCPSoS) refer to a set of systems that flexibly collaborate at runtime in order to render higher level functionality. Most systems in a MCPSoS offer a generic piece of functionality so that they can contribute to many totally different collaboration scenarios. Consequently, it is unknown at design time which systems will how collaborate at runtime. This unpredictability leads to new challenges for the assurance of safety, because established approaches always build on the assumption that systems and their environments are completely known. We believe that the safety research community has to pull together in order to tackle the challenge of unpredictability and that this requires an appropriate taxonomy in order to establish a common understanding of the challenge and related solutions. To this end, we propose enhancements based on a widely accepted taxonomy for dependable computing with respect to the system-of-systems aspect. Further, we will use the taxonomy to reflect on the new challenge of unpredictability and related solutions from the state-of-the-art, namely, safety contracts and dynamic risk assessment. Finally, we motivate an integration of the safety contracts and dynamic risk assessment and present some ideas on this integration. Throughout the paper, we use a real-world example to exemplify our proposed taxonomy and our thoughts
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.
Future automotive systems will be highly automated and they will cooperate to optimize important system qualities and performance. Established safety assurance approaches and standards have been designed with manually controlled stand-alone systems in mind and are thus not fit to ensure safety of this next generation of systems. We argue that, given frequent dynamic changes and unknown contexts, systems need to be enabled to dynamically assess and manage their risks. In doing so, systems become resilient from a safety perspective, i.e. they are able to maintain a state of acceptable risk even when facing changes. This work presents a Dynamic Risk Assessment architecture that implements the concepts of context-awareness, confidence-disclosure and fail-operational. In particular, we demonstrate the utilization of these concepts for the calculation of automotive collision risk metrics, which are at the heart of our architecture.
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