Improving intelligent perception and decision optimization of pedestrian crossing scenarios in autonomous driving environments through large visual language models
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Key Points
Achieved a perception accuracy of 93.05%, demonstrating effective processing of pedestrian crossing scenarios.
The system utilized visual language models to analyze scenario data, predicting collision risks accurately, while offering actionable safety recommendations.
Through standardized prompts and structured data, the model enhances automated decision-making for improved roadway safety.
This innovative framework outperforms traditional methods, integrating visual perception with reasoning for complex traffic environments.
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Implication
This approach improves decision optimization and risk assessment in pedestrian crossing scenarios, indicating safer autonomous vehicle interactions.