Extracting information from geographic images and text is crucial for autonomous vehicles to determine in advance the best cell stations to connect to along their future path. Multiple artificial neural network models can address this challenge; however, there is no definitive guidance on the selection of an appropriate model for such use cases. Therefore, we experimented two architectures to solve such a task: a first architecture with chained models where each model in the chain addresses a sub-task of the task; and a second architecture with a single model that addresses the whole task. Our results showed that these two architectures achieved the same level performance with a root mean square error (RMSE) of 0.055 and 0.056; The findings further revealed that when the task can be decomposed into sub-tasks, the chain architecture exhibits a twelve-fold increase in training speed compared to the composite model. Nevertheless, the composite model significantly alleviates the burden of data labeling.

本篇论文对于如何从地理图像与文本中提取信息，以协助自动驾驶车辆在路径上提前确定最佳的基站连接进行了研究，实验表明，使用串联模型能够获得较快的训练速度，而使用单一的综合模型可显著缓解数据标记的负担。

比较组合模型与链式模型的性能，以定位最近的视觉对象