What question did this study set out to answer?

To improve traffic accident anticipation in autonomous driving by overcoming the limitations of vision-only models through multimodal reasoning and human-inspired attention mechanisms.

August 25, 2025IEEE Transactions on Intelligent Transportation Systems

Chain-of-Thought Guided Multimodal Large Language Models for Scene-Aware Accident Anticipation in Autonomous Driving

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To improve traffic accident anticipation in autonomous driving by overcoming the limitations of vision-only models through multimodal reasoning and human-inspired attention mechanisms.
Designed the MLTA framework integrating multimodal learning with a hypergraph attention network to hierarchically model cross-modal interactions.
Incorporated LLaVA-NeXT guided by Chain-of-Thought prompting alongside a human-inspired attention module mimicking experienced driver priorities.
Evaluated anticipation performance across four public driving datasets: DAD, A3D, CCD, and DADA-2000.
The MLTA model consistently outperformed state-of-the-art baseline approaches across all four benchmark datasets.
The integrated architecture substantially improved accident prediction precision, timeliness, and adaptability in complex, dynamic driving environments.

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Implication

Algorithm evaluation demonstrates improved accident anticipation across benchmark driving datasets, indicating enhanced safety and scene awareness in autonomous vehicles.

Cite This Study

Liao et al. (2025) studied this question.

synapsesocial.com/papers/6a16b6e887c007abbc051f3f https://doi.org/10.1109/tits.2025.3597411

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Implication

Algorithm evaluation demonstrates improved accident anticipation across benchmark driving datasets, indicating enhanced safety and scene awareness in autonomous vehicles.

Chain-of-Thought Guided Multimodal Large Language Models for Scene-Aware Accident Anticipation in Autonomous Driving

Key Points

Discussion

Implication

Cite This Study

Chain-of-Thought Guided Multimodal Large Language Models for Scene-Aware Accident Anticipation in Autonomous Driving

Key Points

Discussion

Implication

Cite This Study

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