The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations

التفاصيل البيبلوغرافية
العنوان: The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations
المؤلفون: Padusinski, Hubert, Steinhauser, Christian, Braun, Thilo, Ries, Lennart, Sax, Eric
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Software Engineering, Electrical Engineering and Systems Science - Image and Video Processing
الوصف: 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.
Comment: Submitted at IEEE ITSC 2024
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2401.14831
رقم الأكسشن: edsarx.2401.14831
قاعدة البيانات: arXiv