The paper presents a decision‑aware framework for predicting dementia‑related crash severity that emphasizes auditability and selective deferral. Using 4,781 Texas crash records, the authors evaluate several models—including structured, narrative, fusion, calibrated fusion, BERT‑family, and local large‑language‑model baselines—under a stratified 70/15/15 split. The leakage‑controlled Gemma model achieves the highest macro‑F1 of 0.545, while a calibrated fusion model reaches 0.522 macro‑F1 with an expected calibration error of 0.033; selective deferral further improves performance, raising macro‑F1 to 0.573 at 70% coverage and reducing severity cost to 0.577.
By Gaurab Chhetri, Anika Baitullah, Subasish Das
arXiv:2609.15997v1 Announce Type: cross
Abstract: Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process tradition...
By Abu Saif Md Nasim Uddin, Mohamed Abdel-Aty, Zubayer Islam, Parvez Anowar, Chenzhu Wang
arXiv:2609.24052v1 Announce Type: new
Abstract: Crash datasets that carry an investigator narrative hold information the coded fields omit. Coding those narratives at scale has been blocked by three...
By Amir Rafe, Subasish Das
arXiv:2609.16267v1 Announce Type: new
Abstract: Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select on...
By Hisham Ihshaish, Peter Mayhew, Tasnim M. A. Zayet, Ana Del Amo
The study investigates whether a model trained on construction‑sector occupational accident narratives can accurately classify accident‑process roles in other sectors and reporting environments. Using 42,244 factual units from 6,040 construction narratives, the authors compared TF‑IDF, frozen pretrained representations, and task‑adapted pretrained models, achieving up to 85.7% balanced accuracy without retraining. The models performed consistently across metallurgy, chemistry‑plastics, and an independent company corpus, though performance varied more on the latter due to differing reporting practices.
By Aho Yapi, Pierre Latouche, Arnaud Guillin, Yan Bailly
Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select one of these records before model comparison begins...
arXiv:2606. 26541v1 Announce Type: new Abstract: Data from affected populations are crucial for informing humanitarian response, but their value depends on timely and consistent interpretation of nuanced accounts of need.
By Jerome Marston, Tino Kreutzer, Salom\'e Garnier, Ella Boone, Phuong N Pham, Patrick Vinck
arXiv:2607. 20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data.
By Faizan Iqbal
The paper introduces Expert-Committee Audit Screening (ECAS), a framework that evaluates the credibility of synthetic minority samples for predicting crash injury severity in automated driving systems. Using real incident data from the NHTSA, ECAS filters generated samples based on label support, boundary separation, committee agreement, and local plausibility, then selects accepted samples via within‑class percentile normalization and Pareto non‑dominated sorting. The best ECAS configuration, combined with normalizing flow augmentation and a TabPFN classifier, outperformed other evidence settings in balanced accuracy, macro‑F1, and minor‑injury recall, and analysis showed ECAS‑accepted samples were better supported by nearby real crashes.
By Zewei Li, Qiaoqiao Ren, Hang Yang, S. C. Wong, Stergios-Aristoteles Mitoulis, Yun Ye
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
arXiv:2606. 00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions common in clinical practice.
By Andrei Marian Feier, Veysel Kocaman, Yigit Gul, Ahmet Korkmaz, Alexander Thomas, Aleksei Zakharov, Jay Gil, Mehmet Butgul, David Talby
SAFARI is the first industrial benchmark for evaluating large language models (LLMs) in automotive hazard analysis and risk assessment (HARA) under ISO 26262. It comprises 3,000 de‑identified HARA cases and tests two tasks: open‑ended hazard generation and standards‑grounded risk classification, using a novel reference‑anchored LLM‑as‑a‑judge protocol. Experiments with nine state‑of‑the‑art LLMs show that while hazard narratives are often plausible, risk classification remains weak (best ASIL macro‑F1 = 0.261), with errors mainly due to missing scenario context and misjudged controllability.
"whyItMatters":"The benchmark highlights the current limitations of LLMs in safety‑critical engineering workflows, guiding future research and expert oversight in automotive safety analysis."
By Chenxi Wu, Zimu Wang, Haiyang Zhang, Wei Wang, Zhijie Xu