The invention discloses a traffic situation perception prediction method and system based on a multi-modal traffic large model, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-modal fusion data; performing model training optimization on the traffic large model according to the multi-modal fusion data to obtain a trained traffic large model; performing traffic situation perception on the vehicle information in different traffic environments according to the trained traffic large model to obtain traffic situation perception information; predicting the traffic situation according to the traffic situation perception information to obtain traffic prediction information; and urban traffic is managed according to the traffic prediction information. According to the method, traffic data in various different modes are fused, a large traffic model is correspondingly formed, traffic situation perception information is acquired, feature description is performed on a traffic scene from multiple different dimensions, and the problem that complex traffic scene analysis cannot be realized due to the adoption of single data in a traditional method is avoided.
本发明公开一种基于多模态交通大模型的交通态势感知预测方法以及系统,涉及人工智能技术领域。方法包括:获取多模态融合数据;根据多模态融合数据对交通大模型进行模型训练优化,获得训练完成的交通大模型;根据训练完成的交通大模型对不同交通环境下的车辆信息进行交通态势感知,获得交通态势感知信息;根据交通态势感知信息对交通态势进行预测,获得交通预测信息;根据交通预测信息对城市交通进行管理。本方法,融合了多种不同模态下的交通数据,并对应形成交通大模型,进行交通态势感知信息的获取,从多个不同维度对交通场景进行了特征描述,避免了传统方法中采用单一数据导致的无法实现复杂交通场景分析的问题。
Traffic situation perception prediction method and system based on multi-modal traffic large model
基于多模态交通大模型的交通态势感知预测方法以及系统
2023-11-28
Patent
Electronic Resource
Chinese
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