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However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.<\/jats:p>","DOI":"10.1145\/3450356","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T21:36:34Z","timestamp":1632346594000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Testing Deep Learning-based Visual Perception for Automated Driving"],"prefix":"10.1145","volume":"5","author":[{"given":"Stephanie","family":"Abrecht","sequence":"first","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]},{"given":"Lydia","family":"Gauerhof","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]},{"given":"Christoph","family":"Gladisch","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]},{"given":"Konrad","family":"Groh","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]},{"given":"Christian","family":"Heinzemann","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]},{"given":"Matthias","family":"Woehrle","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Renningen"}]}],"member":"320","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Konrad Groh, Christian Heinzemann, Sebastian Houben, and Matthias Woehrle.","author":"Abrecht Stephanie","year":"2020","unstructured":"Stephanie Abrecht , Maram Akila , Sujan Sai Gannamaneni , Konrad Groh, Christian Heinzemann, Sebastian Houben, and Matthias Woehrle. 2020 . 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Retrieved May 29, 2020 from https:\/\/wimlworkshop.org\/2018\/program\/."},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00176"},{"key":"e_1_2_1_100_1","unstructured":"Nick Webb Dan Smith Christopher Ludwick Trent Victor Qi Hommes Francesca Favaro George Ivanov and Tom Daniel. 2020. Waymo's Safety Methodologies and Safety Readiness Determinations. arXiv preprint arXiv:2011.00054.  Nick Webb Dan Smith Christopher Ludwick Trent Victor Qi Hommes Francesca Favaro George Ivanov and Tom Daniel. 2020. Waymo's Safety Methodologies and Safety Readiness Determinations. arXiv preprint arXiv:2011.00054."},{"key":"e_1_2_1_101_1","volume-title":"Computer Safety, Reliability, and Security: SAFECOMP Workshops","author":"Willers Oliver","unstructured":"Oliver Willers , Sebastian Sudholt , Shervin Raafatnia , and Stephanie Abrecht . 2020. Safety concerns and mitigation approaches regarding the use of deep learning in safety-critical perception tasks . 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Retrieved from https:\/\/arxiv.org\/abs\/1805.04025. Alan L. Yuille and Chenxi Liu. 2018. Deep Nets: What have they ever done for Vision?arXiv:1805.04025. Retrieved from https:\/\/arxiv.org\/abs\/1805.04025."},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.706"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_25"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.239"},{"key":"e_1_2_1_109_1","volume-title":"Machine learning testing: Survey, landscapes and horizons","author":"Zhang Jie M.","year":"2020","unstructured":"Jie M. Zhang , Mark Harman , Lei Ma , and Yang Liu . 2020. Machine learning testing: Survey, landscapes and horizons . IEEE Trans. Softw. Eng. ( 2020 ). PrePrint . Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu. 2020. Machine learning testing: Survey, landscapes and horizons. IEEE Trans. Softw. Eng. (2020). 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