CrashSight is a new infrastructure-focused benchmark showing that state-of-the-art vision-language models can describe crash scenes but fail at temporal and causal reasoning.
Dada-2000: Can driving accident be pre- dicted by driver attentionƒ analyzed by a benchmark
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
AITP is a new multimodal large language model that uses multimodal chain-of-thought and retrieval-augmented generation of legal knowledge to achieve state-of-the-art results on traffic accident responsibility allocation and related tasks, supported by the DecaTARA benchmark of 67,941 videos.
citing papers explorer
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CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning
CrashSight is a new infrastructure-focused benchmark showing that state-of-the-art vision-language models can describe crash scenes but fail at temporal and causal reasoning.
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AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models
AITP is a new multimodal large language model that uses multimodal chain-of-thought and retrieval-augmented generation of legal knowledge to achieve state-of-the-art results on traffic accident responsibility allocation and related tasks, supported by the DecaTARA benchmark of 67,941 videos.