{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T17:06:39Z","timestamp":1758474399007,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064326"},{"type":"electronic","value":"9783031064333"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06433-3_8","type":"book-chapter","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T18:03:24Z","timestamp":1652551404000},"page":"87-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Case Study on\u00a0the\u00a0Use of\u00a0the\u00a0SafeML Approach in\u00a0Training Autonomous Driving Vehicles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2941-7948","authenticated-orcid":false,"given":"Matthias","family":"Bergler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7470-3767","authenticated-orcid":false,"given":"Ramin Tavakoli","family":"Kolagari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0904-3712","authenticated-orcid":false,"given":"Kristina","family":"Lundqvist","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"8_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-3-030-58920-2_13","volume-title":"Model-Based Safety and Assessment","author":"K Aslansefat","year":"2020","unstructured":"Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., Papadopoulos, Y.: SafeML: safety monitoring of machine learning classifiers through statistical difference measures. In: Zeller, M., H\u00f6fig, K. (eds.) IMBSA 2020. LNCS, vol. 12297, pp. 197\u2013211. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58920-2_13"},{"key":"8_CR2","unstructured":"Das, S.: Best practices for dealing with concept drift (2021). https:\/\/neptune.ai\/blog\/concept-drift-best-practices"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1002\/sam.10054","volume":"2","author":"A Dries","year":"2009","unstructured":"Dries, A., R\u00fcckert, U.: Adaptive concept drift detection. Stat. Anal. Data Min. 2, 311\u2013327 (2009). https:\/\/doi.org\/10.1002\/sam.10054","journal-title":"Stat. Anal. Data Min."},{"key":"8_CR4","unstructured":"Godwin,, C.: Tesla\u2019s autopilot \u2018tricked\u2019 to operate without driver (2021). https:\/\/www.bbc.com\/news\/technology-56854417"},{"key":"8_CR5","unstructured":"Greenberg, A.: Hackers remotely kill a jeep on the highway-with me in it (2015). https:\/\/www.wired.com\/2015\/07\/hackers-remotely-kill-jeep-highway\/"},{"key":"8_CR6","unstructured":"Greenberg, A.: The jeep hackers are back to prove car hacking can get much worse (2016). https:\/\/www.wired.com\/2016\/08\/jeep-hackers-return-high-speed-steering-acceleration-hacks\/"},{"key":"8_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23"},{"key":"8_CR8","unstructured":"Health, U.: 5 real-life medical devices inspired by science fiction (2020). https:\/\/www.usfhealthonline.com\/resources\/healthcare\/5-real-life-medical-devices-inspired-by-science-fiction\/"},{"key":"8_CR9","unstructured":"Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector machines. In: Proceedings of ICML, May 2000"},{"key":"8_CR10","unstructured":"Koorosh, A.: How to make your classifier safe (2020). https:\/\/towardsdatascience.com\/how-to-make-your-classifier-safe-46d55f39f1ad"},{"key":"8_CR11","unstructured":"Kovacs, E.: Tesla car hacked remotely from drone via zero-click exploit (2021). https:\/\/www.securityweek.com\/tesla-car-hacked-remotely-drone-zero-click-exploit"},{"key":"8_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"8_CR13","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-319-26989-4_4","volume-title":"Big Data Analysis: New Algorithms for a New Society","author":"I \u017dliobait\u0117","year":"2016","unstructured":"\u017dliobait\u0117, I., Pechenizkiy, M., Gama, J.: An overview of concept drift applications. In: Japkowicz, N., Stefanowski, J. (eds.) Big Data Analysis: New Algorithms for a New Society. SBD, vol. 16, pp. 91\u2013114. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-26989-4_4"},{"key":"8_CR14","unstructured":"NuScenes: Nuscenes by motional (2020). https:\/\/www.nuscenes.org\/"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.asoc.2016.12.023","volume":"52","author":"D Oreski","year":"2016","unstructured":"Oreski, D., Oreski, S., Klicek, B.: Effects of dataset characteristics on the performance of feature selection techniques. Appl. Soft Comput. 52, 109\u2013119 (2016). https:\/\/doi.org\/10.1016\/j.asoc.2016.12.023","journal-title":"Appl. Soft Comput."},{"key":"8_CR16","unstructured":"Templeton, B.: Tesla in Taiwan crashes directly into overturned truck, ignores pedestrian, with autopilot on (2020). https:\/\/www.forbes.com\/sites\/bradtempleton\/2020\/06\/02\/tesla-in-taiwan-crashes-directly-into-overturned-truck-ignores-pedestrian-with-autopilot-on\/?sh=3ec11c5758e5"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06433-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:08:53Z","timestamp":1709813333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06433-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064326","9783031064333"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06433-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}