August 17, 2024Open Access

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

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JYJunwei YouUniversity of Wisconsin–Madison HSHaotian ShiChina Three Gorges Corporation (China)ZJZhuoyu JiangUniversity of Wisconsin–Madison

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Implication

Benchmark evaluation demonstrates superior trajectory planning in cooperative autonomous driving, indicating enhanced safety through vehicle-infrastructure multimodal fusion.

Key Points

Cooperative autonomous driving achieves improved situational awareness and precise trajectory planning across complex road scenarios through integrated vision-language models.
Benchmark evaluation on the DAIR-V2X dataset shows that multimodal data fusion outperforms existing state-of-the-art baselines in cooperative autonomous driving tasks.
Vehicle-infrastructure cooperative autonomous driving highlights how end-to-end systems can improve decision-making and safety across complex, dynamic transportation networks.

Cite This Study

You et al. (2024) studied this question.

synapsesocial.com/papers/68e5be6db6db643587555cb8 https://doi.org/10.48550/arxiv.2408.09251

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Implication

Benchmark evaluation demonstrates superior trajectory planning in cooperative autonomous driving, indicating enhanced safety through vehicle-infrastructure multimodal fusion.

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Discussion

Member takes

Implication

Key Points

Cite This Study

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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