# VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding

arXiv: https://arxiv.org/abs/2507.09815 · HTML: https://mlsanity.com/paper/2507.09815/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 57 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 30 of 32)

- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [Ferret: Refer and Ground Anything Anywhere at Any Granularity](https://mlsanity.com/paper/2310.07704/) (2023)
- [NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario](https://mlsanity.com/paper/2305.14836/) (2023)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding](https://mlsanity.com/paper/2409.14485/) (2024)
- [LingoQA: Visual Question Answering for Autonomous Driving](https://mlsanity.com/paper/2312.14115/) (2023)
- [DRAMA: Joint Risk Localization and Captioning in Driving](https://mlsanity.com/paper/2209.10767/) (2022)
- [Dolphins: Multimodal Language Model for Driving](https://mlsanity.com/paper/2312.00438/) (2023)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events](https://mlsanity.com/paper/2103.15538/) (2021)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://mlsanity.com/paper/1903.00618/) (2019)
- [Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning](https://mlsanity.com/paper/2309.06597/) (2023)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving](https://mlsanity.com/paper/2403.19838/) (2024)
- [Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding](https://mlsanity.com/paper/2503.18478/) (2025)
- [Abductive Ego-View Accident Video Understanding for Safe Driving Perception](https://mlsanity.com/paper/2403.00436/) (2024)
- [TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes](https://mlsanity.com/paper/2502.02449/) (2025)
- [Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework](https://mlsanity.com/paper/2412.06681/) (2024)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic](https://mlsanity.com/paper/2404.08561/) (2024)
- [Traffic-Domain Video Question Answering with Automatic Captioning](https://mlsanity.com/paper/2307.09636/) (2023)
- [Aligning LLM with human travel choices: a persona-based embedding learning approach](https://mlsanity.com/paper/2505.19003/) (2025)

## Built on by (most cited first, 20 of 20)

- [WaymoQA: A Multi-View Visual Question Answering Dataset for Safety-Critical Reasoning in Autonomous Driving](https://mlsanity.com/paper/2511.20022/) (2025)
- [Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset and Benchmark for Biometric Safety](https://mlsanity.com/paper/2509.00192/) (2025)
- [VideoPerceiver: Enhancing Fine-Grained Temporal Perception in Video Multimodal Large Language Models](https://mlsanity.com/paper/2511.18823/) (2025)
- [More than the Sum: Panorama-Language Models for Adverse Omni-Scenes](https://mlsanity.com/paper/2603.09573/) (2026)
- [Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning](https://mlsanity.com/paper/2607.01667/) (2026)
- [Understanding Real-World Traffic Safety through RoadSafe365 Benchmark](https://mlsanity.com/paper/2602.07212/) (2026)
- [LFS: Learnable Frame Selector for Event-Aware and Temporally Diverse Video Captioning](https://mlsanity.com/paper/2601.14594/) (2026)
- [COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection](https://mlsanity.com/paper/2605.15325/) (2026)
- [TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination](https://mlsanity.com/paper/2606.01737/) (2026)
- [Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation](https://mlsanity.com/paper/2606.29805/) (2026)
- [From Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA](https://mlsanity.com/paper/2606.30220/) (2026)
- [Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method](https://mlsanity.com/paper/2601.10165/) (2026)
- [GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic](https://mlsanity.com/paper/2605.10386/) (2026)
- [Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding](https://mlsanity.com/paper/2606.12047/) (2026)
- [AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring](https://mlsanity.com/paper/2607.05859/) (2026)
- [Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies](https://mlsanity.com/paper/2607.18325/) (2026)
- [DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video](https://mlsanity.com/paper/2607.23132/) (2026)
- [Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges](https://mlsanity.com/paper/2608.08184/) (2026)
- [CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios](https://mlsanity.com/paper/2608.19380/) (2026)
- [dKFD: Phase-Structured Evidence Allocation for Fixed-Budget Localized Event Understanding](https://mlsanity.com/paper/2609.33083/) (2026)

## Abstract (arXiv)

Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, is a critical challenge for autonomous driving systems, as crashes involving VRUs often result in severe or fatal consequences. While multimodal large language models (MLLMs) have shown promise in enhancing scene understanding and decision making in autonomous vehicles, there is currently no standardized benchmark to quantitatively evaluate their reasoning abilities in complex, safety-critical scenarios involving VRUs. To address this gap, we present VRU-Accident, a large-scale vision-language benchmark designed to evaluate MLLMs in high-risk traffic scenarios involving VRUs. VRU-Accident comprises 1K real-world dashcam accident videos, annotated with 6K multiple-choice question-answer pairs across six safety-critical categories (with 24K candidate options and 3.4K unique answer choices), as well as 1K dense scene descriptions. Unlike prior works, our benchmark focuses explicitly on VRU-vehicle accidents, providing rich, fine-grained annotations that capture both spatial-temporal dynamics and causal semantics of accidents. To assess the current landscape of MLLMs, we conduct a comprehensive evaluation of 17 state-of-the-art models on the multiple-choice VQA task and on the dense captioning task. Our findings reveal that while MLLMs perform reasonably well on visually grounded attributes, they face significant challenges in reasoning and describing accident causes, types, and preventability.