{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:00:07Z","timestamp":1742994007334,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030555825"},{"type":"electronic","value":"9783030555832"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-55583-2_6","type":"book-chapter","created":{"date-parts":[[2020,8,21]],"date-time":"2020-08-21T06:02:53Z","timestamp":1597989773000},"page":"82-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assurance Case Patterns for Cyber-Physical Systems with Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Ramneet","family":"Kaur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radoslav","family":"Ivanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Cleaveland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleg","family":"Sokolsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Insup","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"6_CR1","unstructured":"Adelard: ASCAD - the Adelard Safety Case Development (ASCAD) Manual (1998)"},{"key":"6_CR2","unstructured":"F1tenth. http:\/\/f1tenth.org\/"},{"key":"6_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-642-28891-3_14","volume-title":"NASA Formal Methods","author":"A Ayoub","year":"2012","unstructured":"Ayoub, A., Kim, B.G., Lee, I., Sokolsky, O.: A safety case pattern for model-based development approach. In: Goodloe, A.E., Person, S. (eds.) NFM 2012. LNCS, vol. 7226, pp. 141\u2013146. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28891-3_14"},{"issue":"9","key":"6_CR4","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MC.2019.2914775","volume":"52","author":"R Bloomfield","year":"2019","unstructured":"Bloomfield, R., Khlaaf, H., Conmy, P.R., Fletcher, G.: Disruptive innovations and disruptive assurance: assuring machine learning and autonomy. Computer 52(9), 82\u201389 (2019)","journal-title":"Computer"},{"key":"6_CR5","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Boopathy, A., Weng, T.W., Chen, P.Y., Liu, S., Daniel, L.: CNN-Cert: an efficient framework for certifying robustness of convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3240\u20133247 (2019)","DOI":"10.1609\/aaai.v33i01.33013240"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-319-66284-8_1","volume-title":"Computer Safety, Reliability, and Security","author":"S Burton","year":"2017","unstructured":"Burton, S., Gauerhof, L., Heinzemann, C.: Making the case for safety of machine learning in highly automated driving. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10489, pp. 5\u201316. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66284-8_1"},{"key":"6_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1007\/978-3-642-39799-8_18","volume-title":"Computer Aided Verification","author":"X Chen","year":"2013","unstructured":"Chen, X., \u00c1brah\u00e1m, E., Sankaranarayanan, S.: Flow*: an analyzer for non-linear hybrid systems. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 258\u2013263. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39799-8_18"},{"key":"6_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-642-53956-5_7","volume-title":"Foundations of Health Information Engineering and Systems","author":"Y Chen","year":"2014","unstructured":"Chen, Y., Lawford, M., Wang, H., Wassyng, A.: Insulin pump software certification. In: Gibbons, J., MacCaull, W. (eds.) FHIES 2013. LNCS, vol. 8315, pp. 87\u2013106. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-642-53956-5_7"},{"issue":"9","key":"6_CR10","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342\u20131350 (2018)","journal-title":"Nat. Med."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Denney, E., Pai, G.: Safety considerations for UAS ground-based detect and avoid. In: 2016 IEEE\/AIAA 35th Digital Avionics Systems Conference, pp. 1\u201310 (2016)","DOI":"10.1109\/DASC.2016.7778077"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Denney, E., Pai, G., Habli, I.: Towards measurement of confidence in safety cases. In: 2011 International Symposium on Empirical Software Engineering and Measurement, pp. 380\u2013383. IEEE (2011)","DOI":"10.1109\/ESEM.2011.53"},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/978-3-642-14295-6_17","volume-title":"Computer Aided Verification","author":"A Donz\u00e9","year":"2010","unstructured":"Donz\u00e9, A.: Breach, a toolbox for verification and parameter synthesis of hybrid systems. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 167\u2013170. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14295-6_17"},{"key":"6_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/978-3-319-57288-8_26","volume-title":"NASA Formal Methods","author":"T Dreossi","year":"2017","unstructured":"Dreossi, T., Donz\u00e9, A., Seshia, S.A.: Compositional falsification of cyber-physical systems with machine learning components. In: Barrett, C., Davies, M., Kahsai, T. (eds.) NFM 2017. LNCS, vol. 10227, pp. 357\u2013372. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-57288-8_26"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Dutta, S., Chen, X., Jha, S., Sankaranarayanan, S., Tiwari, A.: Sherlock-a tool for verification of neural network feedback systems: demo abstract. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 262\u2013263 (2019)","DOI":"10.1145\/3302504.3313351"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Fainekos, G.E., Sankaranarayanan, S., Ueda, K., Yazarel, H.: Verification of automotive control applications using S-TaLiRo. In: 2012 American Control Conference (ACC), pp. 3567\u20133572. IEEE (2012)","DOI":"10.1109\/ACC.2012.6315384"},{"key":"6_CR17","unstructured":"Fazlyab, M., Robey, A., Hassani, H., Morari, M., Pappas, G.: Efficient and accurate estimation of Lipschitz constants for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 11423\u201311434 (2019)"},{"key":"6_CR18","unstructured":"Group, A.C.W., et al.: Goal structuring notation community standard (2018)"},{"key":"6_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63387-9_1","volume-title":"Computer Aided Verification","author":"X Huang","year":"2017","unstructured":"Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3\u201329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_1"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Ivanov, R., Carpenter, T.J., Weimer, J., Alur, R., Pappas, G.J., Lee, I.: Case study: verifying the safety of an autonomous racing car with a neural network controller. arXiv preprint arXiv:1910.11309 (2019)","DOI":"10.1145\/3365365.3382216"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Ivanov, R., Weimer, J., Alur, R., Pappas, G.J., Lee, I.: Verisig: verifying safety properties of hybrid systems with neural network controllers. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 169\u2013178. ACM (2019)","DOI":"10.1145\/3302504.3311806"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Julian, K.D., Kochenderfer, M.J.: Neural network guidance for UAVs. In: AIAA Guidance, Navigation, and Control Conference, p. 1743 (2017)","DOI":"10.2514\/6.2017-1743"},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-63387-9_5","volume-title":"Computer Aided Verification","author":"G Katz","year":"2017","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97\u2013117. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_5"},{"key":"6_CR24","unstructured":"Ko, C.Y., Lyu, Z., Weng, T.W., Daniel, L., Wong, N., Lin, D.: POPQORN: quantifying robustness of recurrent neural networks. arXiv preprint:1905.07387 (2019)"},{"issue":"1","key":"6_CR25","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s00521-006-0039-9","volume":"16","author":"Z Kurd","year":"2007","unstructured":"Kurd, Z., Kelly, T., Austin, J.: Developing artificial neural networks for safety critical systems. Neural Comput. Appl. 16(1), 11\u201319 (2007)","journal-title":"Neural Comput. Appl."},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Lin, C.L., Shen, W.: Applying safety case pattern to generate assurance cases for safety-critical systems. In: 2015 IEEE 16th International Symposium on High Assurance Systems Engineering, pp. 255\u2013262. IEEE (2015)","DOI":"10.1109\/HASE.2015.44"},{"key":"6_CR27","doi-asserted-by":"publisher","DOI":"10.1201\/9781420067859","volume-title":"Model-Based Design for Embedded Systems","author":"G Nicolescu","year":"2009","unstructured":"Nicolescu, G., Mosterman, P.J.: Model-Based Design for Embedded Systems. CRC Press, Boca Raton (2009)"},{"key":"6_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-030-26601-1_12","volume-title":"Computer Safety, Reliability, and Security","author":"C Picardi","year":"2019","unstructured":"Picardi, C., Hawkins, R., Paterson, C., Habli, I.: A pattern for arguing the assurance of machine learning in medical diagnosis systems. In: Romanovsky, A., Troubitsyna, E., Bitsch, F. (eds.) SAFECOMP 2019. LNCS, vol. 11698, pp. 165\u2013179. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26601-1_12"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Polack, P., Altch\u00e9, F., d\u2019Andr\u00e9a Novel, B., de La Fortelle, A.: The kinematic bicycle model: a consistent model for planning feasible trajectories for autonomous vehicles? In: Intelligent Vehicles Symposium (IV), pp. 812\u2013818. IEEE (2017)","DOI":"10.1109\/IVS.2017.7995816"},{"key":"6_CR30","unstructured":"Rushby, J.: The interpretation and evaluation of assurance cases. Comp. Science Laboratory, SRI International, Technical report, SRI-CSL-15-01 (2015)"},{"key":"6_CR31","unstructured":"Taeyoung, L., Kyongsu, Y., Jangseop, K., Jaewan, L.: Development and evaluations of advanced emergency braking system algorithm for the commercial vehicle. In: Enhanced Safety of Vehicles Conference, ESV, pp. 11\u20130290 (2011)"},{"issue":"5s","key":"6_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3358230","volume":"18","author":"HD Tran","year":"2019","unstructured":"Tran, H.D., Cai, F., Diego, M.L., Musau, P., Johnson, T.T., Koutsoukos, X.: Safety verification of cyber-physical systems with reinforcement learning control. ACM Trans. Embed. Comput. Syst. (TECS) 18(5s), 1\u201322 (2019)","journal-title":"ACM Trans. Embed. Comput. Syst. (TECS)"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Tuncali, C.E., Fainekos, G., Ito, H., Kapinski, J.: Simulation-based adversarial test generation for autonomous vehicles with machine learning components. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1555\u20131562. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500421"},{"key":"6_CR34","unstructured":"Wang, Y.S., Weng, T.W., Daniel, L.: Verification of neural network control policy under persistent adversarial perturbation. arXiv preprint arXiv:1908.06353 (2019)"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Weimer, J., Sokolsky, O., Bezzo, N., Lee, I.: Towards assurance cases for resilient control systems. In: 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 1\u20136. IEEE (2014)","DOI":"10.1109\/CPSNA.2014.19"}],"container-title":["Lecture Notes in Computer Science","Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55583-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T03:20:34Z","timestamp":1619234434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-55583-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030555825","9783030555832"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55583-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAFECOMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Safety, Reliability, and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"39","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"safecomp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/safecomp2020.di.fc.ul.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","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":"27","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":"2","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":"23% - 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":"6.2","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}