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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.
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.
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.
Runtime Monitoring Neuron Activation Patterns
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training.
Towards Dependability Metrics for Neural Networks
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality.
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems
Systematically testing models learned from neural networks remains a crucial unsolved barrier to successfully justify safety for autonomous vehicles engineered using data-driven approach.
