Open Questions In Testing Of Learned Computer Vision Functions For Automated Driving
COMPUTER SAFETY, RELIABILITY, AND SECURITY, SAFECOMP 2019(2019)
摘要
Vision is an important sensing modality in automated driving. Deep learning-based approaches have gained popularity for different computer vision (CV) tasks such as semantic segmentation and object detection. However, the black-box nature of deep neural nets (DNN) is a challenge for practical software verification. With this paper, we want to initiate a discussion in the academic community about research questions w.r.t. software testing of DNNs for safety-critical CV tasks. To this end, we provide an overview of related work from various domains, including software testing, machine learning and computer vision and derive a set of open research questions to start discussion between the fields.
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