Vision-assisted graph neural network for collaborative control in intelligent transportation systems

Due to the challenges faced by current deep learning models in training, such as incomplete data coverage and difficulty in fully reflecting all actual scenarios, this study explores innovative approaches to data collection and annotation strategies. The aim is to fundamentally solve the problem of performance degradation of models in unknown scenarios by improving data diversity and quality. A refined data collection framework has been designed, combined with feature extraction and representation methods in dynamic scenes, effectively enhancing the adaptability and robustness of the model. In order to further verify the effectiveness of the strategy, this study introduces the Visual Auxiliary Graph Neural Network (VA-GNN) and constructs an innovative model for collaborative control of intelligent transportation systems. The experimental results show that with the increase of training iterations, the VA-GNN model and collaborative control strategy have achieved significant results in reducing the average waiting time of vehicles and the number of vehicles in the same lane queue, which is a qualitative leap compared to traditional methods.