{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:09:02Z","timestamp":1759385342798,"version":"3.41.0"},"publisher-location":"Cham","reference-count":83,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031917660","type":"print"},{"value":"9783031917677","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-91767-7_15","type":"book-chapter","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T17:44:51Z","timestamp":1748281491000},"page":"206-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AnoVox: A Benchmark for\u00a0Multimodal Anomaly Detection in\u00a0Autonomous Driving"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0432-4937","authenticated-orcid":false,"given":"Daniel","family":"Bogdoll","sequence":"first","affiliation":[]},{"given":"Iramm","family":"Hamdard","sequence":"additional","affiliation":[]},{"given":"Lukas Namgyu","family":"R\u00f6\u00dfler","sequence":"additional","affiliation":[]},{"given":"Felix","family":"Geisler","sequence":"additional","affiliation":[]},{"given":"Muhammed","family":"Bayram","sequence":"additional","affiliation":[]},{"given":"Felix","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Imhof","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"de Campos","sequence":"additional","affiliation":[]},{"given":"Anushervon","family":"Tabarov","sequence":"additional","affiliation":[]},{"given":"Yitian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Gontscharow","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2167-2028","authenticated-orcid":false,"given":"Hanno","family":"Gottschalk","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6190-7202","authenticated-orcid":false,"given":"J. Marius","family":"Z\u00f6llner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"15_CR1","unstructured":"Ackermann, J., Sakaridis, C., Yu, F.: Maskomaly: zero-shot mask anomaly segmentation. In: BMVC (2023)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Aliakbarian, M.S., Saleh, F.S., Salzmann, M., Fernando, B., Petersson, L., Andersson, L.: Viena2: a driving anticipation dataset. In: ACCV (2018)","DOI":"10.1007\/978-3-030-20887-5_28"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Bansal, M., Krizhevsky, A., Ogale, A.: Chauffeurnet: learning to drive by imitating the best and synthesizing the worst. In: RSS (2019)","DOI":"10.15607\/RSS.2019.XV.031"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Besnier, V., Bursuc, A., Picard, D., Briot, A.: Triggering failures: out-of-distribution detection by learning from local adversarial attacks in semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01541"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Bevandi\u0107, P., Kre\u0161o, I., Or\u0161i\u0107, M., \u0160egvi\u0107, S.: Simultaneous semantic segmentation and outlier detection in presence of domain shift. In: GCPR (2019)","DOI":"10.1007\/978-3-030-33676-9_3"},{"key":"15_CR7","unstructured":"Bhat, S.F., Birkl, R., Wofk, D., Wonka, P., M\u00fcller, M.: ZoeDepth: zero-shot transfer by combining relative and metric depth. arXiv:2302.12288 (2023)"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Blum, H., Sarlin, P.E., Nieto, J., Siegwart, R., Cadena, C.: Fishyscapes: a benchmark for safe semantic segmentation in autonomous driving. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00294"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Blum, H., Sarlin, P.E., Nieto, J., Siegwart, R., Cadena, C.: The fishyscapes benchmark: measuring blind spots in semantic segmentation. IJCV 129 (2021)","DOI":"10.1007\/s11263-021-01511-6"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., et al.: Description of corner cases in automated driving: goals and challenges. In: ICCVW (2021)","DOI":"10.1109\/ICCVW54120.2021.00119"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Guneshka, S., Z\u00f6llner, J.M.: One ontology to rule them all: corner case scenarios for autonomous driving. In: ECCVW (2022)","DOI":"10.1007\/978-3-031-25072-9_29"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Nekolla, M., Joseph, T., Z\u00f6llner, J.M.: Quantification of actual road user behavior on the basis of given traffic rules. In: IEEE IV (2022)","DOI":"10.1109\/IV51971.2022.9827082"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Nitsche, M., Z\u00f6llner, J.M.: Anomaly detection in autonomous driving: a survey. In: CVPRW (2022)","DOI":"10.1109\/CVPRW56347.2022.00495"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Schreyer, F., Z\u00f6llner, J.M.: AD-datasets: a meta-collection of data sets for autonomous driving. In: VEHITS (2022)","DOI":"10.5220\/0011001900003191"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Uhlemeyer, S., Kowol, K., Z\u00f6llner, J.M.: Perception datasets for anomaly detection in autonomous driving: a survey. In: IEEE IV (2023)","DOI":"10.1109\/IV55152.2023.10186609"},{"key":"15_CR16","unstructured":"Bogdoll, D., Yang, Y., Z\u00f6llner, J.M.: MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. arXiv:2311.11762 (2023)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Breitenstein, J., Termohlen, J.A., Lipinski, D., Fingscheidt, T.: Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches. arXiv:2102.05897 (2021)","DOI":"10.1109\/IV47402.2020.9304789"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Breitenstein, J., Term\u00f6hlen, J.A., Lipinski, D., Fingscheidt, T.: Systematization of corner cases for visual perception in automated driving. In: IEEE IV (2020)","DOI":"10.1109\/IV47402.2020.9304789"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Bu, T., Zhang, X., Mertz, C., Dolan, J.M.: CARLA simulated data for rare road object detection. In: IEEE ITSC (2021)","DOI":"10.1109\/ITSC48978.2021.9564932"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Cen, J., et al.: Open-world semantic segmentation for LIDAR point clouds. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19839-7_19"},{"key":"15_CR23","unstructured":"Chan, R., et al.: SegmentMeIfYouCan: a benchmark for anomaly segmentation. In: NeurIPS (2021)"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Chan, R., Rottmann, M., Gottschalk, H.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00508"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Chan, R., Uhlemeyer, S., Rottmann, M., Gottschalk, H.: Deep Neural Networks and Data for Automated Driving, chap. Detecting and Learning the Unknown in Semantic Segmentation. Springer (2022)","DOI":"10.1007\/978-3-031-01233-4_10"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Deng, W., Huang, K., Yu, Q., Lu, H., Zheng, Z., Chen, X.: ElC-OIS: ellipsoidal clustering for open-world instance segmentation on LiDAR data. In: IROS (2023)","DOI":"10.1109\/IROS55552.2023.10342356"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Di\u00a0Biase, G., Blum, H., Siegwart, R., Cadena, C.: Pixel-wise anomaly detection in complex driving scenes. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01664"},{"key":"15_CR30","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., L\u00f3pez, A.M., Koltun, V.: Carla: an open urban driving simulator. In: CoRL (2017)"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Du, X., Wang, X., Gozum, G., Li, Y.: Unknown-aware object detection: learning what you don\u2019t know from videos in the wild. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01331"},{"key":"15_CR32","unstructured":"Franchi, G., et al.: Muad: multiple uncertainties for autonomous driving benchmark for multiple uncertainty types and tasks. In: BMVC (2022)"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"Galesso, S., Argus, M., Brox, T.: Far away in the deep space: dense nearest-neighbor-based out-of-distribution detection. In: ICCVW (2023)","DOI":"10.1109\/ICCVW60793.2023.00482"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Gong, L., Zhang, Y., Xia, Y., Zhang, Y., Ji, J.: SDAC: a multimodal synthetic dataset for anomaly and corner case detection in autonomous driving. AAAI 38(3) (2024)","DOI":"10.1609\/aaai.v38i3.27961"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Grci\u0107, M., Bevandi\u0107, P., Kalafati\u0107, Z., \u0160egvi\u0107, S.: Dense out-of-distribution detection by robust learning on synthetic negative data. Sensors 24 (2024)","DOI":"10.3390\/s24041248"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Grci\u0107., M., Bevandi\u0107., P., Segvi\u0107., S.: Dense open-set recognition with synthetic outliers generated by real NVP. In: International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2021)","DOI":"10.5220\/0010260701330143"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Grci\u0107, M., \u0160ari\u0107, J., \u0160egvi\u0107, S.: On advantages of mask-level recognition for outlier-aware segmentation. In: CVPRW (2023)","DOI":"10.1109\/CVPRW59228.2023.00295"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Hanselmann, N., Renz, K., Chitta, K., Bhattacharyya, A., Geiger, A.: KING: generating safety-critical driving scenarios for robust imitation via kinematics gradients. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19839-7_20"},{"key":"15_CR39","doi-asserted-by":"crossref","unstructured":"Heidecker, F., Bieshaar, M., Sick, B.: Corner cases in machine learning processes. AI Perspect. Adv. 6(1) (2024)","DOI":"10.1186\/s42467-023-00015-y"},{"key":"15_CR40","doi-asserted-by":"crossref","unstructured":"Heidecker, F., et al.: An application-driven conceptualization of corner cases for perception in highly automated driving. In: IEEE IV (2021)","DOI":"10.1109\/IV48863.2021.9575933"},{"key":"15_CR41","unstructured":"Hendrycks, D., et al.: Scaling out-of-distribution detection for real-world settings. In: ICML (2022)"},{"key":"15_CR42","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2016)"},{"key":"15_CR43","doi-asserted-by":"crossref","unstructured":"Hess, G., Jaxing, J., Svensson, E., Hagerman, D., Petersson, C., Svensson, L.: Masked autoencoder for self-supervised pre-training on lidar point clouds. In: IEEE Winter Conference on Applications of Computer Vision (2023)","DOI":"10.1109\/WACVW58289.2023.00039"},{"key":"15_CR44","unstructured":"Hu, A., et al.: Model-based imitation learning for urban driving. In: NeurIPS (2022)"},{"key":"15_CR45","unstructured":"Hu, A., et al.: GAIA-1: A Generative World Model for Autonomous Driving. arXiv:2309.17080 (2023)"},{"key":"15_CR46","doi-asserted-by":"crossref","unstructured":"Kim, H., Lee, K., Hwang, G., Suh, C.: Crash to not crash: learn to identify dangerous vehicles using a simulator. AAAI 33 (2019)","DOI":"10.1609\/aaai.v33i01.3301978"},{"key":"15_CR47","unstructured":"Kim, S.: Multiple issues in the behavior agent (2022). https:\/\/github.com\/carla-simulator\/carla\/issues\/5398. Accessed 07 Mar 2024"},{"key":"15_CR48","doi-asserted-by":"crossref","unstructured":"K\u00f6sel, M., Schreiber, M., Ulrich, M., Gl\u00e4ser, C., Dietmayer, K.: Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection. arXiv:2404.15879 (2024)","DOI":"10.1109\/IV55156.2024.10588849"},{"key":"15_CR49","unstructured":"Li, H., et al.: Open-sourced data ecosystem in autonomous driving: the present and future. arXiv:2312.03408 (2024)"},{"key":"15_CR50","doi-asserted-by":"crossref","unstructured":"Li, J., Dong, Q.: Open-set semantic segmentation for point clouds via adversarial prototype framework. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00909"},{"key":"15_CR51","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: CODA: a real-world road corner case dataset for object detection in autonomous driving. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19839-7_24"},{"key":"15_CR52","unstructured":"Li, Y., et al.: Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases. arXiv:2404.10595 (2024)"},{"key":"15_CR53","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: A survey on autonomous driving datasets: data statistic, annotation, and outlook. arXiv:2401.01454 (2024)","DOI":"10.1109\/TIV.2024.3394735"},{"key":"15_CR54","doi-asserted-by":"crossref","unstructured":"Maag, K., Chan, R., Uhlemeyer, S., Kowol, K., Gottschalk, H.: Two video data sets for tracking and retrieval of out of distribution objects. In: ACCV (2023)","DOI":"10.1007\/978-3-031-26348-4_28"},{"key":"15_CR55","doi-asserted-by":"crossref","unstructured":"Mohan, R., Kumaraswamy, K., Hurtado, J.V., Petek, K., Valada, A.: Panoptic out-of-distribution segmentation. arXiv:2310.11797 (2023)","DOI":"10.1109\/LRA.2024.3375122"},{"key":"15_CR56","doi-asserted-by":"crossref","unstructured":"Najibi, M., Ji, J., Zhou, Y., Qi, C.R., Yan, X., Ettinger, S., Anguelov, D.: Motion inspired unsupervised perception and prediction in autonomous driving. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19839-7_25"},{"key":"15_CR57","doi-asserted-by":"crossref","unstructured":"Nayal, N., Yavuz, M., Henriques, J.F., G\u00fcney, F.: RbA: segmenting unknown regions rejected by all. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00072"},{"key":"15_CR58","doi-asserted-by":"crossref","unstructured":"Nunes, I., Laranjeira, C., Oliveira, H., dos Santos, J.A.: A systematic review on open-set segmentation. Comput. Graph. 115 (2023)","DOI":"10.2139\/ssrn.4374389"},{"key":"15_CR59","doi-asserted-by":"crossref","unstructured":"Nunes, L., et al.: Unsupervised class-agnostic instance segmentation of 3D LiDAR data for autonomous vehicles. IEEE Rob. Auto. Lett. 7(4) (2022)","DOI":"10.1109\/LRA.2022.3187872"},{"key":"15_CR60","unstructured":"Oquab, M., et al.: DINOv2: learning robust visual features without supervision. Trans. Mach. Learn. Res. (2023)"},{"key":"15_CR61","doi-asserted-by":"crossref","unstructured":"Pfeil, J., Wieland, J., Michalke, T., Theissler, A.: On why the system makes the corner case: AI-based holistic anomaly detection for autonomous driving. In: IEEE IV (2022)","DOI":"10.1109\/IV51971.2022.9827078"},{"key":"15_CR62","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: IROS (2016)","DOI":"10.1109\/IROS.2016.7759186"},{"key":"15_CR63","doi-asserted-by":"crossref","unstructured":"Piroli, A., Dallabetta, V., Kopp, J., Walessa, M., Meissner, D., Dietmayer, K.: LS-VOS: identifying outliers in 3D object detections using latent space virtual outlier synthesis. IEEE ITSC (2023)","DOI":"10.1109\/ITSC57777.2023.10421856"},{"key":"15_CR64","doi-asserted-by":"crossref","unstructured":"Rai, S.N., Cermelli, F., Fontanel, D., Masone, C., Caputo, B.: Unmasking anomalies in road-scene segmentation. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00373"},{"key":"15_CR65","doi-asserted-by":"crossref","unstructured":"Rempe, D., Philion, J., Guibas, L.J., Fidler, S., Litany, O.: Generating useful accident-prone driving scenarios via a learned traffic prior. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01679"},{"key":"15_CR66","doi-asserted-by":"crossref","unstructured":"R\u00f6sch, K., et al.: Space, time, and interaction: a taxonomy of corner cases in trajectory datasets for automated driving. In: Symposium Series on Computational Intelligence (2022)","DOI":"10.1109\/SSCI51031.2022.10022241"},{"key":"15_CR67","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van\u00a0Gool, L.: ACDC: the adverse conditions dataset with correspondences for semantic driving scene understanding. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"15_CR68","doi-asserted-by":"crossref","unstructured":"Sena\u00a0Ferreira, R., Gu\u00e9rin, J., Guiochet, J., Waeselynck, H.: SiMOOD: evolutionary testing simulation with out-of-distribution images. In: Pacific Rim International Symposium on Dependable Computing (2022)","DOI":"10.1109\/PRDC55274.2022.00021"},{"key":"15_CR69","unstructured":"Tian, B., et al.: Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics. arXiv:2401.04942 (2024)"},{"key":"15_CR70","doi-asserted-by":"crossref","unstructured":"Tremblay, M., Halder, S.S., de Charette, R., Lalonde, J.F.: Rain rendering for evaluating and improving robustness to bad weather. IJCV 129 (2021)","DOI":"10.1007\/s11263-020-01366-3"},{"key":"15_CR71","unstructured":"Urtasun, R.: Interpretable neural motion planner (2021). https:\/\/youtube.com\/watch?v=PSZ2Px9PrHg. Accessed 01 Mar 2024"},{"key":"15_CR72","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: DeepAccident: a motion and accident prediction benchmark for V2X autonomous driving. In: AAAI (2024)","DOI":"10.1609\/aaai.v38i6.28370"},{"key":"15_CR73","unstructured":"Waymo: Waymo One: The next step on our self-driving journey (2018). https:\/\/waymo.com\/blog\/2018\/10\/where-next-10-million-miles-will-take-us\/. Accessed 22 Feb 2024"},{"key":"15_CR74","unstructured":"Waymo: Advice Letter 0002 (Tier 2) (2024). https:\/\/cpuc.ca.gov\/-\/media\/cpuc-website\/divisions\/consumer-protection-and-enforcement-division\/documents\/tlab\/av-programs\/waymo-llc-cpuc-advice-letter-0002-tier-2--january-2024-passenger-safety-plan-update-january-192024.pdf. Accessed 22 Feb 2024"},{"key":"15_CR75","unstructured":"Waymo: Waymo One (2024). https:\/\/waymo.com\/waymo-one\/. Accessed 22 Feb 2024"},{"key":"15_CR76","doi-asserted-by":"crossref","unstructured":"Wiederer, J., Schmidt, J., Kressel, U., Dietmayer, K., Belagiannis, V.: A benchmark for unsupervised anomaly detection in multi-agent trajectories. In: IEEE ITSC (2022)","DOI":"10.1109\/ITSC55140.2022.9922440"},{"key":"15_CR77","unstructured":"Xu, S., Gilpin, L.H.: DANGER: a framework of danger-aware novel dataset generator extension for robustness test of machine learning. In: BayLearn Machine Learning Symposium (2022)"},{"key":"15_CR78","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: Unipad: a universal pre-training paradigm for autonomous driving. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01443"},{"key":"15_CR79","doi-asserted-by":"crossref","unstructured":"Zendel, O., Honauer, K., Murschitz, M., Steininger, D., Dominguez, G.F.: WildDash - creating hazard-aware benchmarks. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01231-1_25"},{"key":"15_CR80","unstructured":"Zhang, L., Xiong, Y., Yang, Z., Casas, S., Hu, R., Urtasun, R.: Learning unsupervised world models for autonomous driving via discrete diffusion. In: ICLR (2024)"},{"key":"15_CR81","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liniger, A., Dai, D., Yu, F., Van\u00a0Gool, L.: End-to-end urban driving by imitating a reinforcement learning coach. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01494"},{"key":"15_CR82","doi-asserted-by":"crossref","unstructured":"Zhou, J., Beyerer, J.: Corner cases in data-driven automated driving: definitions, properties and solutions. In: IEEE IV (2023)","DOI":"10.1109\/IV55152.2023.10186558"},{"key":"15_CR83","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00981"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91767-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T17:45:06Z","timestamp":1748281506000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91767-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031917660","9783031917677"],"references-count":83,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91767-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}