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Veh."},{"issue":"12","key":"10.1016\/j.neucom.2026.134102_bib67","doi-asserted-by":"crossref","first-page":"14111","DOI":"10.1109\/TITS.2023.3296567","article-title":"A probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections","volume":"24","author":"Li","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.134102_bib68","doi-asserted-by":"crossref","unstructured":"J. Hariyono, L. Kurnianggoro, K.-H. Jo, Analysis of pedestrian collision risk using fuzzy inference model, in: Proceedings of the 2016 16th International Conference on Control, Automation and Systems (ICCAS). 2016, pp. 696\u2013700.","DOI":"10.1109\/ICCAS.2016.7832394"},{"key":"10.1016\/j.neucom.2026.134102_bib69","doi-asserted-by":"crossref","unstructured":"J. Hariyono, et al., Estimation of collision risk for improving driver's safety, in: Proceedings of the 2016-42nd Annual Conference of the IEEE Industrial Electronics Society (IECON), 2016, pp. 901\u2013906.","DOI":"10.1109\/IECON.2016.7793743"},{"key":"10.1016\/j.neucom.2026.134102_bib70","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106528","article-title":"Vulnerable road user safety evaluation using probe vehicle data with collision warning information","volume":"165","author":"Matsuo","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"10.1016\/j.neucom.2026.134102_bib71","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2025.130584","article-title":"The connected vehicle microscopic behavior modeling base on risk field theory: theoretical developments, methodological overview and future trends","author":"Ma","year":"2025","journal-title":"Phys. A"},{"issue":"4","key":"10.1016\/j.neucom.2026.134102_bib72","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TITS.2015.2401837","article-title":"The driving safety field based on driver-vehicle-road interactions","volume":"16","author":"Wang","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"10.1016\/j.neucom.2026.134102_bib73","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ijtst.2020.05.005","article-title":"A longitudinal car-following risk assessment model based on risk field theory for autonomous vehicles","volume":"10","author":"Wu","year":"2021","journal-title":"Int. J. Transp. Sci. Technol."},{"issue":"8","key":"10.1016\/j.neucom.2026.134102_bib74","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1177\/0361198121995511","article-title":"Optimizing right-turn signals to benefit pedestrian-vehicle interactions","volume":"2675","author":"Wang","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.neucom.2026.134102_bib75","doi-asserted-by":"crossref","unstructured":"J. Wang, J. Wu, Y. Li, K. Li, The concept and modeling of driving safety field based on driver-vehicle-road interactions, in: Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014, pp. 974\u2013981.","DOI":"10.1109\/ITSC.2014.6957815"},{"issue":"9","key":"10.1016\/j.neucom.2026.134102_bib76","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1080\/00423114.2018.1497185","article-title":"An optimal hierarchical framework of the trajectory following by convex optimisation for highly automated driving vehicles","volume":"57","author":"Cao","year":"2019","journal-title":"Veh. Syst. Dyn."},{"key":"10.1016\/j.neucom.2026.134102_bib77","doi-asserted-by":"crossref","unstructured":"S. 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Netw."},{"issue":"12","key":"10.1016\/j.neucom.2026.134102_bib98","doi-asserted-by":"crossref","first-page":"4864","DOI":"10.3390\/su12124864","article-title":"Neural-network-based dynamic distribution model of parking space under sharing and non-sharing modes","volume":"12","author":"Zhao","year":"2020","journal-title":"Sustainability"},{"key":"10.1016\/j.neucom.2026.134102_bib99","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102942","article-title":"Adaptive risk tendency in uncertainty-aware motion planning using risk-sensitive Reinforcement Learning","volume":"63","author":"Wang","year":"2025","journal-title":"Adv. Eng. Inf."},{"issue":"12","key":"10.1016\/j.neucom.2026.134102_bib100","doi-asserted-by":"crossref","first-page":"10589","DOI":"10.1109\/TNNLS.2022.3169488","article-title":"Synchronous spatiotemporal graph transformer: A new framework for traffic data prediction","volume":"34","author":"Wang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. 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Gou, Dynamic attention-enhanced spatio-temporal network for pedestrian collision risk assessment. in: Proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 2024, pp. 207\u2013221.","DOI":"10.1007\/978-981-97-8792-0_15"},{"issue":"3","key":"10.1016\/j.neucom.2026.134102_bib106","doi-asserted-by":"crossref","DOI":"10.1016\/j.geits.2023.100092","article-title":"A review of occluded objects detection in real complex scenarios for autonomous driving","volume":"2","author":"Ruan","year":"2023","journal-title":"Green. Energy Intell. Transp."},{"key":"10.1016\/j.neucom.2026.134102_bib107","article-title":"HVAE-DC: a hierarchical variational autoencoder-based deep clustering model for multi-level driving behavior","volume":"305","author":"Ma","year":"2025","journal-title":"Expert. Sys. 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Prev."},{"key":"10.1016\/j.neucom.2026.134102_bib111","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2026.108756","article-title":"DADiffNet: Delay-aware diffusion networks with adaptive subgraphs for large scale traffic forecasting","volume":"200","author":"Fan","year":"2026","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.134102_bib112","doi-asserted-by":"crossref","unstructured":"A. Rasouli, I. Kotseruba, J.K. Tsotsos, PIE: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction, in: Proceedings of the IEEE\/CVF international conference on computer vision (CVF), 2019, pp. 6262-6271.","DOI":"10.1109\/ICCV.2019.00636"},{"key":"10.1016\/j.neucom.2026.134102_bib113","doi-asserted-by":"crossref","unstructured":"A. Rasouli, I. Kotseruba, J.K. Tsotsos, Are they going to cross? 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