The paper presents a system that combines coded police crash records with officer narratives to estimate uncoded crash factors in Texas from 2017‑2025. It uses an in‑context tabular foundation model (Kumo Tabular) to process coded data, a calibrated System One model (Jev) to read sampled narratives, and human judgments to recalibrate probabilities, producing validated population estimates for factors such as hydroplaning, medical episodes, fatigue, animals, and phone use. The approach demonstrates that narrative analysis uncovers more injury crashes than coded fields and that a single additional round of human review can significantly tighten estimate precision. "whyItMatters":"The system provides safety offices with more accurate counts, a discordance map, a validated re‑read list, and a reading budget, enabling better resource allocation for road‑safety interventions."
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...
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.
arXiv:2607. 29064v1 Announce Type: cross Abstract: Police crash narratives contain information that may supplement structured crash databases, but manual review is labor-intensive and it remains unclear how well large language models (LLMs) reproduce official crash coding.
arXiv:2609.15997v1 Announce Type: cross Abstract: Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process tradition...
arXiv:2609.11592v2 Announce Type: replace Abstract: Transportation agencies increasingly predict crash-injury severity with statistical and machine-learning models, but these models do not state how...
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
arXiv:2609.22582v1 Announce Type: new Abstract: End-to-end and vision-language-action (VLA) driving policies are compared by leaderboard rank, but a rank reports an outcome, not the behaviour behind...
arXiv:2607. 20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data.
arXiv:2603.11413v4 Announce Type: replace-cross Abstract: A recent Nature Medicine study reported that ChatGPT Health under-triages 51.6% of emergencies and concluded that consumer-facing AI triage p...
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.
arXiv:2603.11413v5 Announce Type: replace-cross Abstract: A recent Nature Medicine study reported that ChatGPT Health under-triages 51.6% of emergencies and concluded that consumer-facing AI triage p...
The paper introduces a hybrid framework that combines text analysis of aviation accident reports with physics-based models to extract and quantify causal chains of failure. Using a large language model, the authors build a causation knowledge graph from 56,202 reports and achieve 90.8% recovery of investigator-flagged causes on a 2,000-report test set. The framework calibrates occurrence models for physical factors and employs a leaky noisy-OR to estimate failure mode probabilities, demonstrating interpretability gains and modest predictive improvements for specific scenarios such as carburetor icing.