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STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation
The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes.
The missing link: Developing a safety case for perception components in automated driving
In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components.
Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems
We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.
Engineering problems in machine learning systems
The key to using a machine learning model in a deductively engineered system is decomposing the data-driven training of machine learning models into requirement, design, and verification, particularly for machine learning models used in safety-critical systems.
Improving Transparency of Deep Neural Inference Process
However, the inference process of deep learning is black-box, and not very suitable to safety-critical systems which must exhibit high transparency.
Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems
To establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions.
Network Analysis for Explanation
Safety critical systems strongly require the quality aspects of artificial intelligence including explainability.
