{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:59:17Z","timestamp":1767085157373,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031460012"},{"type":"electronic","value":"9783031460029"}],"license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-46002-9_16","type":"book-chapter","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:02:36Z","timestamp":1702483356000},"page":"279-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Benchmarks: Semantic Segmentation Neural Network Verification and\u00a0Objection Detection Neural Network Verification in\u00a0Perceptions Tasks of\u00a0Autonomous Driving"],"prefix":"10.1007","author":[{"given":"Yonggang","family":"Luo","sequence":"first","affiliation":[]},{"given":"Jinyan","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Sanchu","family":"Han","sequence":"additional","affiliation":[]},{"given":"Lecheng","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, G., Pailoor, S., Dillig, I., Chaudhuri, S.: Optimization and abstraction: a synergistic approach for analyzing neural network robustness. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019, pp. 731\u2013744. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3314221.3314614"},{"key":"16_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-76384-8_2","volume-title":"NASA Formal Methods","author":"S Bak","year":"2021","unstructured":"Bak, S.: nnenum: verification of ReLU neural networks with optimized abstraction refinement. In: Dutle, A., Moscato, M.M., Titolo, L., Mu\u00f1oz, C.A., Perez, I. (eds.) NFM 2021. LNCS, vol. 12673, pp. 19\u201336. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-76384-8_2"},{"key":"16_CR3","unstructured":"Bak, S., Liu, C., Johnson, T.T.: The second international verification of neural networks competition (vnn-comp 2021): Summary and results. arXiv: 2109.00498 (2021)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Bijelic, M., et al.: Seeing through fog without seeing fog: deep multimodal sensor fusion in unseen adverse weather. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11679\u201311689 (2020)","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A., Misener, R.: Efficient verification of relu-based neural networks via dependency analysis. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i04.5729"},{"key":"16_CR6","unstructured":"Shaler, B.: DanGill, M.M.M.P.W.C.: Carvana image masking challenge (2017)"},{"key":"16_CR7","unstructured":"Brix, C., Noll, T.: Debona: decoupled boundary network analysis for tighter bounds and faster adversarial robustness proofs. arXiv: 2006.09040 (2020)"},{"key":"16_CR8","unstructured":"Brix, C., Muller, M.N., Bak, S., Johnson, T.T., Liu, C.: First three years of the international verification of neural networks competition (vnn-comp). ArXiv, abs\/ arXiv: 2301.05815 (2023)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuscenes: A multimodal dataset for autonomous driving. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11618\u201311628 (2019)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"16_CR10","unstructured":"Chahal, K.S., Dey, K.: A survey of modern object detection literature using deep learning. ArXiv, abs\/ arXiv: 1808.07256 (2018)"},{"key":"16_CR11","unstructured":"Chan, R., et al.: Segmentmeifyoucan: A benchmark for anomaly segmentation. ArXiv, abs\/ arXiv: 2104.14812 (2021)"},{"key":"16_CR12","unstructured":"Dathathri, S., et al.: Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 5318\u20135331. Curran Associates Inc. (2020)"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Diaz-Ruiz, C.A., et al.: Ithaca365: dataset and driving perception under repeated and challenging weather conditions. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21351\u201321360 (2022)","DOI":"10.1109\/CVPR52688.2022.02069"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Benchmarking robustness of 3d object detection to common corruptions in autonomous driving. ArXiv, abs\/ arXiv: 2303.11040 (2023)","DOI":"10.1109\/CVPR52729.2023.00105"},{"issue":"1","key":"16_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TAC.2020.3046193","volume":"67","author":"M Fazlyab","year":"2022","unstructured":"Fazlyab, M., Morari, M., Pappas, G.J.: Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming. IEEE Trans. Autom. Control 67(1), 1\u201315 (2022)","journal-title":"IEEE Trans. Autom. Control"},{"key":"16_CR16","unstructured":"Ferrari, C., Mueller, M.N., Jovanovi\u0107, N., Vechev, M.: Complete verification via multi-neuron relaxation guided branch-and-bound. In: International Conference on Learning Representations (2022)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"16_CR18","unstructured":"Hendrycks, D., Basart, S., Mazeika, M., Mostajabi, M., Steinhardt, J., Song, D.X.: Scaling out-of-distribution detection for real-world settings. In: International Conference on Machine Learning (2022)"},{"key":"16_CR19","unstructured":"Henriksen, P., Lomuscio, A.: Efficient neural network verification via adaptive refinement and adversarial search (2020)"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Henriksen, P., Lomuscio, A.: Deepsplit: an efficient splitting method for neural network verification via indirect effect analysis. In: Zhou, Z.-H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pp. 2549\u20132555. International Joint Conferences on Artificial Intelligence Organization, Main Track (Aug 2021)","DOI":"10.24963\/ijcai.2021\/351"},{"key":"16_CR21","unstructured":"Henriksen, P., Hammernik, K., Rueckert, D., Lomuscio, A.: Bias field robustness verification of large neural image classifiers. In: British Machine Vision Conference (2021)"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Huang, C., Fan, J., Li, W., Chen, X., Zhu, Q.: Reachnn: reachability analysis of neural-network controlled systems. ACM Trans. Embed. Comput. Syst. 18(5s) (2019)","DOI":"10.1145\/3358228"},{"key":"16_CR23","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, HSCC 2019, pp. 169\u2013178. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3302504.3311806"},{"key":"16_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-030-25540-4_26","volume-title":"Computer Aided Verification","author":"G Katz","year":"2019","unstructured":"Katz, G., et al.: The marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 443\u2013452. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_26"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Katz, S.M., Corso, A., Strong, C.A., Kochenderfer, M.J.: Verification of image-based neural network controllers using generative models. In: 2021 IEEE\/AIAA 40th Digital Avionics Systems Conference (DASC), pp. 1\u201310 (2021)","DOI":"10.1109\/DASC52595.2021.9594360"},{"key":"16_CR26","unstructured":"Khedr, H., Ferlez, J., Shoukry, Y.: Effective formal verification of neural networks using the geometry of linear regions. ArXiv, abs\/ arXiv: 2006.10864 (2020)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: A real-world road corner case dataset for object detection in autonomous driving. ArXiv, abs\/ arXiv: 2203.07724 (2022)","DOI":"10.1007\/978-3-031-19839-7_24"},{"key":"16_CR28","unstructured":"Liu, X., Yang, H., Liu, Z., Song, L., Chen, Y., Li, H.H.: Dpatch: an adversarial patch attack on object detectors. In: Computer Vision and Pattern Recognition (2018)"},{"key":"16_CR29","unstructured":"Ma, X.: dog-qiuqiu\/yolo-fastestv2: V0.2 (August 2021)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Mohapatra, J., Weng, T.-W., Chen, P.-Y., Liu, S., Daniel, L.: Towards verifying robustness of neural networks against a family of semantic perturbations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 241\u2013249 (2020)","DOI":"10.1109\/CVPR42600.2020.00032"},{"key":"16_CR31","unstructured":"Muller, M. N., Brix, C., Bak, S., Liu, C., Johnson, T.T.: The third international verification of neural networks competition (vnn-comp 2022): Summary and results (2023)"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Pinggera, P., Ramos, S., Gehrig, S., Franke, U., Rother, C., Mester, R.: Lost and found: detecting small road hazards for self-driving vehicles. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1099\u20131106 (2016)","DOI":"10.1109\/IROS.2016.7759186"},{"key":"16_CR33","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1177\/0278364920979368","volume":"40","author":"MA Pitropov","year":"2020","unstructured":"Pitropov, M.A., et al.: Canadian adverse driving conditions dataset. Inter. J. Robo. Res. 40, 681\u2013690 (2020)","journal-title":"Inter. J. Robo. Res."},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. ArXiv, abs\/ arXiv: 1505.04597 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., Kwiatkowska, M.: Global robustness evaluation of deep neural networks with provable guarantees for L0 norm. CoRR, abs\/ arXiv: 1804.05805 (2018)","DOI":"10.24963\/ijcai.2019\/824"},{"key":"16_CR36","first-page":"9835","volume":"32","author":"H Salman","year":"2019","unstructured":"Salman, H., Yang, G., Zhang, H., Hsieh, C.-J., Zhang, P.: A convex relaxation barrier to tight robustness verification of neural networks. Adv. Neural. Inf. Process. Syst. 32, 9835\u20139846 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR37","unstructured":"Shen, J.,et al.: Sok: on the semantic ai security in autonomous driving. ArXiv, abs\/ arXiv: 2203.05314 (2022)"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Singh, G., Gehr, T., P\u00fcschel, M., Vechev, M.: An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3(POPL) (2019)","DOI":"10.1145\/3290354"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2443\u20132451 (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"16_CR40","unstructured":"Thoma, M.: A survey of semantic segmentation. ArXiv, abs\/ arXiv: 1602.06541 (2016)"},{"key":"16_CR41","unstructured":"Tjeng, V., Xiao, K. Y., Tedrake, R.: Evaluating robustness of neural networks with mixed integer programming. In: International Conference on Learning Representations (2017)"},{"key":"16_CR42","doi-asserted-by":"crossref","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., 18(5s) (2019)","DOI":"10.1145\/3358230"},{"key":"16_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-53288-8_2","volume-title":"Computer Aided Verification","author":"H-D Tran","year":"2020","unstructured":"Tran, H.-D., Bak, S., Xiang, W., Johnson, T.T.: Verification of deep convolutional neural networks using imagestars. In: Lahiri, S.K., Wang, C. (eds.) CAV 2020. LNCS, vol. 12224, pp. 18\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53288-8_2"},{"key":"16_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-81685-8_12","volume-title":"Computer Aided Verification","author":"H-D Tran","year":"2021","unstructured":"Tran, H.-D., et al.: Robustness verification of semantic segmentation neural networks using relaxed reachability. In: Silva, A., Leino, K.R.M. (eds.) CAV 2021. LNCS, vol. 12759, pp. 263\u2013286. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81685-8_12"},{"key":"16_CR45","unstructured":"Wang, S., et al.: Beta-CROWN: efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification. In: Advances in Neural Information Processing Systems 34 (2021)"},{"key":"16_CR46","unstructured":"Xu, K., et al.: Automatic perturbation analysis for scalable certified robustness and beyond. In: Advances in Neural Information Processing Systems 33 (2020)"},{"key":"16_CR47","unstructured":"Xu, K., et al.: Fast and complete: enabling complete neural network verification with rapid and massively parallel incomplete verifiers. In: International Conference on Learning Representations (2021)"},{"key":"16_CR48","doi-asserted-by":"publisher","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","volume":"8","author":"E Yurtsever","year":"2019","unstructured":"Yurtsever, E., Lambert, J., Carballo, A., Takeda, K.: A survey of autonomous driving: common practices and emerging technologies. IEEE Access 8, 58443\u201358469 (2019)","journal-title":"IEEE Access"},{"key":"16_CR49","unstructured":"Zhang, H., Weng, T.-W., Chen, P.-Y., Hsieh, C.-J., Daniel, L.: Efficient neural network robustness certification with general activation functions. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 4944\u20134953. Curran Associates Inc., Red Hook (2018)"},{"key":"16_CR50","unstructured":"Zhang, H., et al.: General cutting planes for bound-propagation-based neural network verification. In: Advances in Neural Information Processing Systems (2022)"},{"key":"16_CR51","unstructured":"Zhang, H., et al.: A branch and bound framework for stronger adversarial attacks of ReLU networks. In: Proceedings of the 39th International Conference on Machine Learning, vol. 162, pp. 26591\u201326604 (2022)"},{"key":"16_CR52","first-page":"23","volume":"12","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zou, X., Kuang, L., Wang, J., Sherratt, R., Yu, X.: Cctsdb 2021: a more comprehensive traffic sign detection benchmark. Human-centric Comput. Inform. Sci. 12, 23 (2022)","journal-title":"Human-centric Comput. Inform. Sci."}],"container-title":["Lecture Notes in Computer Science","Bridging the Gap Between AI and Reality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46002-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T16:05:38Z","timestamp":1702483538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46002-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"ISBN":["9783031460012","9783031460029"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46002-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"14 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AISoLA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bridging the Gap between AI and Reality","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Crete","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aisola2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023-aisola.isola-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}