arXiv AI June 2, 2026 By Xu Li, Zedong Fu, Xinyi Li, Xun Han

TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

arXiv:2606. 01737v1 Announce Type: new Abstract: Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 7

Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.

By Ryosei Tamura, Andrew Shin

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Sep 16

Crash Narrative-Guided Countermeasure Recommendation Using Large Language Models: A Retrieval-Augmented Generation Framework for Intersection Safety

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

llms rag safety

arXiv AI

Aug 17

Fine-grained Claim-level RAG Benchmark for Law

arXiv:2605. 21071v4 Announce Type: replace-cross Abstract: The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs generate responses.

By Souvick Das, Sallam Abualhaija, Domenico Bianculli

llms rag benchmarks safety

arXiv AI

Jun 12

Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis

arXiv:2606. 13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive.

By Seongjin Kim, Sungil Kim

llms rag safety

arXiv Computer Vision

Aug 21

CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios

arXiv:2608. 19380v1 Announce Type: new Abstract: While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents.

By Sparsh Garg, Yi-Wen Chen, Vijay Kumar B G, Abhishek Aich

llms agents computer-vision robotics multimodal benchmarks safety

arXiv Computer Vision

Sep 22

PRISM-RAG: Multimodal Hypergraph Retrieval-Augmented Generation for Tobacco Product and Legislative Policy Reasoning

arXiv:2609.23769v1 Announce Type: new Abstract: The disambiguation of semantically similar statutory text across jurisdictions is a retrieval problem that existing methods do not solve. This inter-co...

By Manuel Serna-Aguilera, Raegan Anderes, Page Dobbs, Khoa Luu

llms rag multimodal benchmarks

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Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

Crash Narrative-Guided Countermeasure Recommendation Using Large Language Models: A Retrieval-Augmented Generation Framework for Intersection Safety

Fine-grained Claim-level RAG Benchmark for Law

Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis

CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios

PRISM-RAG: Multimodal Hypergraph Retrieval-Augmented Generation for Tobacco Product and Legislative Policy Reasoning