AccidentBench: 2,000 Videos and 19,000 QA Pairs for Vehicle Accident Understanding

AccidentBench is a multimodal benchmark containing approximately 2,000 videos and 19,000 human-annotated question-answer pairs focused on vehicle accidents. The dataset was created by Open-Space-Reasoning and was last updated on the platform in October 2025. It covers a wide range of reasoning tasks related to understanding events in videos.

Use Cases

• Benchmarking multimodal reasoning models based on video and question-answer pairs.
• Training models for visual question answering in complex, real-world driving scenarios.
• Developing systems for automated accident analysis and understanding based on video content.
• Studying AI capabilities for temporal and causal reasoning in dynamic visual scenes.

Strengths

• Approximately 2,000 videos provide a substantial visual corpus for analysis.
• 19,000 human-annotated question-answer pairs offer a structured benchmark for evaluation.
• Covers a wide range of reasoning tasks, as indicated in the description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific model training needs.
• Description metadata is limited; actual data quality and annotation consistency require manual inspection.

Provenance

Source

Open-Space-Reasoning

Collection Method

Likely collected and annotated by the authoring organization; specific methodology is not detailed in the provided input.

Freshness

Last updated 2025-10-09 17:38:49; freshness should be verified.