{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:40:48Z","timestamp":1785512448830,"version":"3.56.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032050724","type":"print"},{"value":"9783032050731","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05073-1_11","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T07:37:33Z","timestamp":1758353853000},"page":"159-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CODIF: Counterfactual Data-Augmentations for\u00a0Estimating Perception Influencing Factors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6395-986X","authenticated-orcid":false,"given":"Christopher","family":"Meszaros","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6521-8406","authenticated-orcid":false,"given":"Roman","family":"Gansch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7839-5497","authenticated-orcid":false,"given":"Peter","family":"Liggesmeyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"11_CR1","unstructured":"Goodfellow, I.J., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http:\/\/www.deeplearningbook.org"},{"key":"11_CR2","unstructured":"Villalobos, P., Sevilla, J., Besiroglu, T., Heim, L., Ho, A., Hobbhahn, M.: Machine learning model sizes and the parameter gap (2022)"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Gr\u00fcnbaum, D., Stern, M.L., Lang, E.W.: Quantitative probing: validating causal models with quantitative domain knowledge. J. Causal Inference 11 (2023)","DOI":"10.1515\/jci-2022-0060"},{"key":"11_CR4","unstructured":"ADEE, M.S.A.: Model Based System Analysis Techniques to Determine Propagation Paths of Functional Insufficiencies in Software Intensive Systems. Ph.D. thesis, Technische Universit\u00e4t Kaiserslautern (2023)"},{"key":"11_CR5","unstructured":"Pearl, J., Glymour, M., Jewell, N.: Causal Inference in Statistics: A Primer. Wiley (2016)"},{"key":"11_CR6","volume-title":"Causal Inference: What If","author":"M Hernan","year":"2024","unstructured":"Hernan, M., Robins, J.: Causal Inference: What If. CRC Press, Chapman & Hall\/CRC Monographs on Statistics & Applied Probab (2024)"},{"key":"11_CR7","unstructured":"Xing, X., Jia, T., Chen, J., Xiong, L., Yu, Z.: An ontology-based method to identify triggering conditions for perception insufficiency of autonomous vehicles (2022)"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s42154-021-00172-y","volume":"5","author":"M Hoss","year":"2022","unstructured":"Hoss, M., Scholtes, M., Eckstein, L.: A review of testing object-based environment perception for safe automated driving. Autom. Innov. 5, 223\u2013250 (2022)","journal-title":"Autom. Innov."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Burton, S., Habli, I., Lawton, T., McDermid, J., Morgan, P., Porter, Z.: Mind the gaps: assuring the safety of autonomous systems from an engineering, ethical, and legal perspective. Artif. Intell. 279, 103201 (2019)","DOI":"10.1016\/j.artint.2019.103201"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuscenes: a multimodal dataset for autonomous driving (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"issue":"2","key":"11_CR11","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1109\/TITS.2023.3317475","volume":"25","author":"R Maier","year":"2024","unstructured":"Maier, R., Grabinger, L., Urlhart, D., Mottok, J.: Causal models to support scenario-based testing of adas. IEEE Trans. Intell. Transp. Syst. 25(2), 1815\u20131831 (2024)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Adee, A., Gansch, R., Liggesmeyer, P.: Systematic modeling approach for environmental perception limitations in automated driving. In: 2021 17th European Dependable Computing Conference (EDCC), pp. 103\u2013110. IEEE (2021)","DOI":"10.1109\/EDCC53658.2021.00022"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Adee, A., Gansch, R., Liggesmeyer, P., Glaeser, C., Drews, F.: Discovery of perception performance limiting triggering conditions in automated driving. In: 2021 5th International Conference on System Reliability and Safety (ICSRS), pp. 248\u2013257. IEEE (2021)","DOI":"10.1109\/ICSRS53853.2021.9660641"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Liu, J., Sun, P., Sang, M., Li, H., Pan, Y.: Generation of risky scenarios for testing automated driving visual perception based on causal analysis. In: IEEE Transactions on Intelligent Transportation Systems, pp. 1\u201314 (2024)","DOI":"10.1109\/TITS.2024.3421343"},{"key":"11_CR15","unstructured":"Ilse, M., Tomczak, J.M., Forr\u00e9, P.: Selecting data augmentation for simulating interventions (2020)"},{"issue":"12","key":"11_CR16","doi-asserted-by":"publisher","first-page":"4223","DOI":"10.1007\/s13042-023-01891-w","volume":"14","author":"M Rahul","year":"2023","unstructured":"Rahul, M., Chiddarwar, S.S.: A causality-inspired data augmentation approach to cross-domain burr detection using randomly weighted shallow networks. Int. J. Mach. Learn. Cybern. 14(12), 4223\u20134236 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"11_CR17","unstructured":"Reddy, A.G., Bachu, S., Dash, S., Sharma, C., Sharma, A., Balasubramanian, V.N.: On counterfactual data augmentation under confounding (2023)"},{"key":"11_CR18","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation (2017)"},{"key":"11_CR19","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout (2017)"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"11631","DOI":"10.1007\/s11042-020-10141-y","volume":"80","author":"C-Y Hsu","year":"2021","unstructured":"Hsu, C.-Y., Lin, L.-E., Lin, C.H.: Age and gender recognition with random occluded data augmentation on facial images. Multimedia Tools Appl. 80, 11631\u201311653 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"11_CR22","unstructured":"Roser, M., Appel, C., Ritchie, H.: Human height. Our World in Data (2021). https:\/\/ourworldindata.org\/human-height"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.aap.2015.03.009","volume":"79","author":"G Crocetta","year":"2015","unstructured":"Crocetta, G., Piantini, S., Pierini, M., Simms, C.: The influence of vehicle front-end design on pedestrian ground impact. Accident Anal. Prevent. 79, 56\u201369 (2015)","journal-title":"Accident Anal. Prevent."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Beckers, S., Halpern, J.Y.: Abstracting causal models (2019)","DOI":"10.1609\/aaai.v33i01.33012678"},{"key":"11_CR25","unstructured":"Chen, K., et al.: MMDetection: open mmLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"11_CR27","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018)"},{"issue":"2","key":"11_CR28","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Lecture Notes in Computer Science","Model-Based Safety and Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05073-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T07:37:41Z","timestamp":1758353861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05073-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032050724","9783032050731"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05073-1_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IMBSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Model-Based Safety and Assessment","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"imbsa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/imbsa-conference.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}