Padusinski, Hubert (FZI Research Center for Information Technology), Steinhauser, Christian (FZI Research Center for Information Technology), Braun, Thilo (FZI Forschungszentrum Informatik), Ries, Lennart (FZI Research Center for Information Technology), Sax, Eric (FZI Research Center for Information Technology)

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

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception I" (WeAT2), Wednesday, September 25, 2024, 12:10−12:30, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

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.