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

arXiv: https://arxiv.org/abs/2607.03591 · HTML: https://mlsanity.com/paper/2607.03591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Driving VLMs and open-vocabulary detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Driving VLMs and open-vocabulary detection](https://mlsanity.com/mllm/driving-vlms-and-open-vocabulary-detection/)

## Builds on (most cited first, 5 of 5)

- [Abductive Ego-View Accident Video Understanding for Safe Driving Perception](https://mlsanity.com/paper/2403.00436/) (2024)
- [Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference](https://mlsanity.com/paper/2408.04652/) (2024)
- [Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses](https://mlsanity.com/paper/2406.10789/) (2024)
- [CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis](https://mlsanity.com/paper/2505.07853/) (2025)
- [CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution](https://mlsanity.com/paper/2511.11715/) (2025)

## Abstract (arXiv)

Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records. However, such data sources are costly to deploy and maintain at large scale, and they cannot objectively capture what the driver was actually able to observe before the accident. In contrast, ego-view accident videos directly represent the driver's visual perspective, making them suitable for reasoning about avoidability and driver responsibility. In this paper, we introduce responsibility distribution estimation for ego-view traffic accident videos, a new task in which a model predicts the percentage of responsibility assigned to each involved agent. We construct an LLM-assisted responsibility annotation pipeline and fine-tune multimodal large language models under multiple input settings, including raw frames, segmentation-enhanced input, and textual descriptions. Experimental results establish a strong initial benchmark, demonstrating that multimodal LLMs can effectively perform this nuanced, constraint-based reasoning task. Our findings suggest that ego-centric accident videos provide a promising foundation for socially and legally meaningful multimodal reasoning beyond conventional accident classification and explanation tasks.