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.
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
arXiv:2608. 14746v1 Announce Type: new Abstract: The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety measures.
The paper introduces an explainable pedestrian routing method that estimates perceived safety by first generating a natural‑language caption from street‑level images and then deriving risk scores solely from structured features of that caption. Benchmarking nine captioning setups against a CLIP image‑embedding baseline shows comparable performance, and the system was deployed on over 650,000 images across 36 wards in Manchester and Huddersfield. Field validation with 3,669 ratings from 70 participants revealed a modest but statistically significant correlation (r = 0.262) with human judgments, while a stronger supervised benchmark did not translate into better real‑world performance.
arXiv:2610.26474v2 Announce Type: replace Abstract: Large language models (LLMs) designed to interact with users are typically trained to respond appropriately to harmful or toxic content. However, e...
The paper introduces Counterfactual Neighborhood Risk Fields (CNRF), an evidence‑grounded framework that improves safety for vision‑language models (VLMs). CNRF builds context‑matched benign‑harmful counterfactual pairs, uses their representation differences to define risk directions, and separates neighborhood support from risk estimation. This approach allows selective intervention rather than blanket rejection, achieving high macro defense success rates across multiple VLMs while preserving benign‑task utility.
The paper introduces an edge‑assisted crowdsensing framework that uses handlebar‑mounted cameras to capture street‑level video, a vision‑language model to extract structured traffic attributes, and an explainable boosting machine to combine these with OpenStreetMap data and on‑bike kinematics for a 1‑9 safety score. On 1,362 annotated frames from two London routes, the model achieves 72.2 % binary accuracy and a mean absolute error of 1.011, with feature attribution revealing the impact of lane separation, painted lanes, and construction sites on safety ratings. The system operates efficiently at the network edge, requiring only 2.49 s per frame for extraction and 0.4 ms for scoring, and supports continuous monitoring at a cost of 1.4 accuracy points for 1,000 riders over a 20‑second interval.