{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:06:15Z","timestamp":1789031175344,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pedestrian-detection research and pre-crash safety assessment predominantly represent upright pedestrians, although prone, supine, lateral, seated, crouched, kneeling, partially collapsed, and fall-transition states alter target geometry, visibility, sensor signatures, and intervention time. Cross-study comparison is further limited by inconsistent posture labels, data provenance, latency boundaries, uncertainty reporting, and vehicle-response assumptions. We developed NUP-REPORT 1.0 as a provisional reporting and benchmarking framework through a structured narrative synthesis of a 45-source derivation corpus covering epidemiology, sensing benchmarks, uncertainty and assurance methods, reporting-guideline methodology, and public safety protocols. A reconstructed decision ledger documented 46 candidate concepts: 30 were retained as checklist items, 10 were assigned to an extended descriptor set, and six were merged. Each retained item was mapped to supporting evidence and classified as universal core (n = 19), component-contingent core (n = 4), or conditional (n = 7). The framework comprises six domains, a scenario-coverage matrix, five non-overlapping event timestamps, detection-referenced stopping equations, and a 30-item checklist. A purposive feasibility audit of 20 publications, including the adjacent pedestrian-detection literature not designed specifically for non-upright evaluation, illustrated checklist use. Within this sample, target orientation and static-versus-transition state were each reported explicitly in five of 20 publications (25%); none of the 17 applicable papers reported both time-to-first-detection and detection distance, none evaluated confidence calibration, and none of the 20 reported independent-unit uncertainty intervals. Vehicle-response items were non-applicable to papers making no intervention claim. These observations are sample-specific and do not estimate field-wide reporting prevalence. NUP-REPORT is not a consensus standard, certification procedure, or safety score; it is a traceable Version 1.0 proposal for study design, retrospective audit, and stakeholder refinement.<\/jats:p>","DOI":"10.3390\/s26185710","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T09:24:02Z","timestamp":1788945842000},"page":"5710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NUP-REPORT 1.0: A Proposed Reporting and Benchmarking Framework for Non-Upright Pedestrian Detection and Pre-Crash Safety Evaluation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4641-0112","authenticated-orcid":false,"given":"Nick","family":"Barua","sequence":"first","affiliation":[{"name":"Department of Legal Medicine, Shiga University of Medical Science, Otsu 520-2192, Shiga, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5243-8766","authenticated-orcid":false,"given":"Masahito","family":"Hitosugi","sequence":"additional","affiliation":[{"name":"Department of Legal Medicine, Shiga University of Medical Science, Otsu 520-2192, Shiga, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). Global Status Report on Road Safety 2023, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.cjtee.2020.11.008","article-title":"Hit-and-runs more common with pedestrians lying on the road: Analysis of a nationwide database in Japan","volume":"24","author":"Hitosugi","year":"2021","journal-title":"Chin. J. Traumatol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Koh, M., Hitosugi, M., Kagesawa, E., Narikawa, T., and Takashima, K. (2021). Factors influencing fatalities or severe injuries to pedestrians lying on the road in Japan: Nationwide police database study. Healthcare, 9.","DOI":"10.3390\/healthcare9111433"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian detection: An evaluation of the state of the art","volume":"34","author":"Wojek","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","first-page":"886","article-title":"Histograms of oriented gradients for human detection","volume":"Volume 1","author":"Dalal","year":"2005","journal-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2017). CityPersons: A diverse dataset for pedestrian detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TPAMI.2019.2897684","article-title":"EuroCity Persons: A novel benchmark for person detection in traffic scenes","volume":"41","author":"Braun","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and Kweon, I.S. (2015). Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016). The Cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020). Scalability in perception for autonomous driving: Waymo Open Dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., and Tsotsos, J.K. (2017). Are they going to cross? A benchmark dataset and baseline for pedestrian crosswalk behavior. Proceedings of the IEEE International Conference on Computer Vision Workshops, IEEE.","DOI":"10.1109\/ICCVW.2017.33"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119242","DOI":"10.1016\/j.eswa.2022.119242","article-title":"Fallen person detection for autonomous driving","volume":"213","author":"Lee","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Barua, N., and Hitosugi, M. (2025). Advanced multi-modal sensor fusion system for detecting falling humans: Quantitative evaluation for enhanced vehicle safety. Vehicles, 7.","DOI":"10.2139\/ssrn.5873082"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Barua, N., and Hitosugi, M. (2026). A physics-grounded multi-modal sensor fusion framework for pedestrian impact kinematic reconstruction under uncertainty: Phase 1 design and theoretical evaluation. Sensors, 26.","DOI":"10.3390\/s26113387"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Barua, N., and Hitosugi, M. (2026). A multi-modal AI system for detecting pedestrians lying on the road: Simulation-based safety and injury risk analysis. Vehicles, 8.","DOI":"10.3390\/vehicles8060136"},{"key":"ref_18","unstructured":"Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.Q. (2017, January 6\u201311). On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_19","unstructured":"Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J. (2019, January 8\u201314). Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_20","unstructured":"Hendrycks, D., and Gimpel, K. (2017, January 24\u201326). A baseline for detecting misclassified and out-of-distribution examples in neural networks. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. (2020). Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_22","unstructured":"Kendall, A., and Gal, Y. (2017, January 4\u20139). What uncertainties do we need in Bayesian deep learning for computer vision?. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_23","first-page":"111","article-title":"Assuring the machine learning lifecycle: Desiderata, methods, and challenges","volume":"54","author":"Ashmore","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Menzel, T., Bagschik, G., and Maurer, M. (2018). Scenarios for development, test and validation of automated vehicles. 2018 IEEE Intelligent Vehicles Symposium, IEEE.","DOI":"10.1109\/IVS.2018.8500406"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bagschik, G., Menzel, T., and Maurer, M. (2018). Ontology based scene creation for the development of automated vehicles. 2018 IEEE Intelligent Vehicles Symposium, IEEE.","DOI":"10.1109\/IVS.2018.8500632"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Kunic, T., and Tsotsos, J.K. (2019). PIE: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00636"},{"key":"ref_27","unstructured":"Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017, January 4\u20139). Simple and scalable predictive uncertainty estimation using deep ensembles. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4271\/2016-01-0128","article-title":"Challenges in autonomous vehicle testing and validation","volume":"4","author":"Koopman","year":"2016","journal-title":"SAE Int. J. Transp. Saf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"18016","DOI":"10.1109\/ACCESS.2021.3053159","article-title":"Criticality analysis for the verification and validation of automated vehicles","volume":"9","author":"Neurohr","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","unstructured":"(2018). Road Vehicles\u2014Functional Safety (Standard No. ISO 26262:2018)."},{"key":"ref_31","unstructured":"(2022). Road Vehicles\u2014Safety of the Intended Functionality (Standard No. ISO 21448:2022)."},{"key":"ref_32","unstructured":"(2022). Road Vehicles\u2014Test Scenarios for Automated Driving Systems\u2014Vocabulary (Standard No. ISO 34501:2022)."},{"key":"ref_33","unstructured":"(2022). Road Vehicles\u2014Test Scenarios for Automated Driving Systems\u2014Scenario-Based Safety Evaluation Framework (Standard No. ISO 34502:2022)."},{"key":"ref_34","unstructured":"(2018). Road Vehicles\u2014Test Devices for Target Vehicles, Vulnerable Road Users and Other Objects, for Assessment of Active Safety Functions\u2014Part 2: Requirements for Pedestrian Targets (Standard No. ISO 19206-2:2018)."},{"key":"ref_35","unstructured":"European New Car Assessment Programme (2026). Car Safety Protocols 2026\u20132028, Euro NCAP."},{"key":"ref_36","unstructured":"European New Car Assessment Programme (AEB\/LSS VRU Systems Test Protocol, 2024). AEB\/LSS VRU Systems Test Protocol, Version 4.5.1."},{"key":"ref_37","unstructured":"UNECE (2020). Uniform Provisions Concerning the Approval of Motor Vehicles with Regard to the Advanced Emergency Braking System for M1 and N1 Vehicles, UNECE. UN Regulation No. 152."},{"key":"ref_38","unstructured":"National Highway Traffic Safety Administration (2024). Federal Motor Vehicle Safety Standards. Automatic Emergency Braking Systems for Light Vehicles, as amended."},{"key":"ref_39","unstructured":"(2021). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (Standard No. J3016_202104)."},{"key":"ref_40","unstructured":"Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., and Dennison, D. (2015, January 7\u201312). Hidden technical debt in machine learning systems. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e240300","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Moher, D., Schulz, K.F., Simera, I., and Altman, D.G. (2010). Guidance for developers of health research reporting guidelines. PLoS Med., 7.","DOI":"10.1371\/journal.pmed.1000217"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2016). How far are we from solving pedestrian detection?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.141"},{"key":"ref_47","unstructured":"Liu, J., Zhang, S., Wang, S., and Metaxas, D.N. (2016, January 19\u201322). Multispectral deep neural networks for pedestrian detection. Proceedings of the British Machine Vision Conference, York, UK."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, D., Ouyang, W., Ricci, E., Wang, X., and Sebe, N. (2017). Learning cross-modal deep representations for robust pedestrian detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.451"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"K\u00f6nig, D., Adam, M., Jarvers, C., Layher, G., Neumann, H., and Teutsch, M. (2017). Fully convolutional region proposal networks for multispectral person detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE.","DOI":"10.1109\/CVPRW.2017.36"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhu, X., Chen, X., Yang, X., Lei, Z., and Liu, Z. (2019). Weakly aligned cross-modal learning for multispectral pedestrian detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00523"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wolpert, A., Teutsch, M., Sarfraz, M.S., and Stiefelhagen, R. (2020, January 7\u201310). Anchor-free small-scale multispectral pedestrian detection. Proceedings of the British Machine Vision Conference, Virtual.","DOI":"10.5244\/C.34.131"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hasan, I., Liao, S., Li, J., Akram, S.U., and Shao, L. (2021). Generalizable pedestrian detection: The elephant in the room. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.01117"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:10:08Z","timestamp":1789027808000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":52,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185710"],"URL":"https:\/\/doi.org\/10.3390\/s26185710","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}