  Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems using machine-learning and deep-learning models, such
as automated-driving vehicles. Quality assurance frameworks are required for
such machine learning systems, but there are no widely accepted and established
quality-assurance concepts and techniques. At the same time, open problems and
the relevant technical fields are not organized. 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. In
the present study, we identify, classify, and explore the open problems in
quality assurance of safety-critical machine-learning systems, and their
relevant corresponding industry and technological trends, using
automated-driving vehicles as an example. Our results show that addressing
these open problems requires incorporating knowledge from several different
technological and industrial fields, including the automobile industry,
statistics, software engineering, and machine learning.
