The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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:2608. 14767v1 Announce Type: cross Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust.
The study shows that while large language model (LLM) annotations of stakeholder consultation submissions are highly reproducible (intraclass correlations > 0.99), they do not reliably capture the intended construct measured by structured survey responses. Divergence between LLM-inferred and survey measures varies by stakeholder group, with business associations expressing more AI risk concern in text than in surveys, and spatial autocorrelation indicates neighboring European countries share similar text-based stances. Despite these divergences, survey-reported concerns remain strongly linked to support for explainability across all levels of divergence.
EG-ARSA introduces an Expert‑Grounded Distillation (EGD) framework that transfers institutional road safety expertise into a compact vision‑language model for visual road safety auditing. The method calibrates a teacher model against authoritative field audits, achieving a Cohen’s kappa of 0.74 before generating structured supervision for an 8‑billion‑parameter student model via Low‑Rank Adaptation. The authors also release Bangladesh Road Safety Audit (BD‑ARSA), an open dataset of 21,947 image‑audit records, and demonstrate that the student model outperforms both its larger teacher and Gemini‑2.5‑Flash in ordinal risk assessment and expert evaluation.
arXiv:2608. 06378v1 Announce Type: cross Abstract: Driver emotions can affect risk perception, decision-making, and vehicle control under complex road conditions.