Open Questions in Testing of Learned Computer Vision Functions for Automated Driving

Abstract

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.