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. 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 responsibil
The proliferation of ego-view camera data (dashcams, personal devices) combined with rapidly advancing multimodal large language models is enabling new applications for AI in accident analysis.
Accurate and objective responsibility distribution in accidents has significant implications for insurance, legal systems, and the development of self-driving car ethics, making this a critical area of AI research.
The ability to use AI to objectively assess driver responsibility from their direct visual perspective fundamentally changes how accident investigations can be conducted and potentially streamlines claims processes.
- · Insurance companies
- · Automotive safety researchers
- · Legal tech platforms
- · Multimodal AI developers
- · Fraudulent accident claimants
- · Traditional accident reconstruction firms
Refined capabilities for AI to interpret complex visual and contextual information in real-world scenarios.
Potential for reduced insurance premiums due to more accurate risk assessment and fraud detection, and faster claims processing.
Ethical debates intensify regarding AI's role in determining human 'responsibility' and 'avoidability,' especially as autonomous vehicle integration increases.
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Read at arXiv cs.AI