arXiv (OAI Expanded)
open access
Crash Narrative-Guided Countermeasure Recommendation Using Large Language Models: A Retrieval-Augmented Generation Framework for Intersection Safety
Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process traditionally relies on expert judgment, making it labor-intensive, difficult to scale, and dependent on the availability of experienced traffic safety engineer
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models#139128
- Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG#925810
- Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models#178920
- WordPolo: Evaluating Language Models Through Iterative Semantic Feedback#1013219
- Rewarding Novel Deductions: Solver-guided Process Rewards for Logical Reasoning#1128919
Record · ID 1110395
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive