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This is a Validation Study study.

November 8, 2025Open Access

Multimodal Large Language Model Framework for Safe and Interpretable Grid-Integrated EVs

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Real-world data validated effective alerts generated for urban driving scenarios, ensuring safety and comprehension.
Alerts enhanced using object detection and semantic segmentation based on multimodal sensor data integration.
Assessment of driver interactions through visual perception and CAN bus enables informed decision-making in critical situations.
Highlights opportunities in smart grid applications with the potential for scalable fleet coordination and EV load forecasting.

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Framework demonstrates effective alerts for electric vehicles in urban driving, indicating advances in multimodal interaction with the smart grid.

Carvalho et al. (2025) studied this question.

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Framework demonstrates effective alerts for electric vehicles in urban driving, indicating advances in multimodal interaction with the smart grid.