November 20, 2025Open Access

Smart Traffic Orchestration with Vision-Language Fusion Models

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Traffic accidents can be classified using a visual graph representation, enhancing automated response systems.
A balanced accuracy score of 57.77% was achieved when incorporating visual graph information into the classifier.
The proposed vision-language fusion model uses multimodal deep learning to enhance traffic orchestration efficiency.
Results suggest that reinforcement learning can transform traditional traffic management systems, improving safety and flow.

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Implication

This analysis demonstrates improved signal timing and accident classification in traffic management, indicating reinforcement learning's potential for enhanced Autonomous Driving.

Cite This Study

Sayma Nasrin Shompa (2025) studied this question.

synapsesocial.com/papers/6924f08cc0ce034ddc350736 https://doi.org/10.5281/zenodo.17661642

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Implication

This analysis demonstrates improved signal timing and accident classification in traffic management, indicating reinforcement learning's potential for enhanced Autonomous Driving.

Smart Traffic Orchestration with Vision-Language Fusion Models

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Implication

Cite This Study

Smart Traffic Orchestration with Vision-Language Fusion Models

Key Points

Discussion

Implication

Cite This Study

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