The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations
arxiv(2024)
摘要
Machine Vision (MV) is essential for solving driving automation. This paper
examines potential shortcomings in current MV testing strategies for highly
automated driving (HAD) systems. We argue for a more comprehensive
understanding of the performance factors that must be considered during the MV
evaluation process, noting that neglecting these factors can lead to
significant risks. This is not only relevant to MV component testing, but also
to integration testing. To illustrate this point, we draw an analogy to a ship
navigating towards an iceberg to show potential hidden challenges in current MV
testing strategies. The main contribution is a novel framework for black-box
testing which observes environmental relations. This means it is designed to
enhance MV assessments by considering the attributes and surroundings of
relevant individual objects. The framework provides the identification of seven
general concerns about the object recognition of MV, which are not addressed
adequately in established test processes. To detect these deficits based on
their performance factors, we propose the use of a taxonomy called "granularity
orders" along with a graphical representation. This allows an identification of
MV uncertainties across a range of driving scenarios. This approach aims to
advance the precision, efficiency, and completeness of testing procedures for
MV.
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