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.

翻译：从地理图像和文本中提取信息对于自动驾驶汽车提前确定其未来路径上最佳基站连接点至关重要。多种人工神经网络模型可应对这一挑战，但目前尚缺乏针对此类用例选择合适模型的明确指导。因此，我们实验了两种架构来解决该任务：第一种是链式模型架构，其中链中每个模型处理任务的子任务；第二种是单一模型的组合架构，直接处理完整任务。结果表明，两种架构实现了同等性能水平，均方根误差（RMSE）分别为0.055和0.056。研究进一步揭示，当任务可分解为子任务时，链式架构的训练速度比组合模型快十二倍。然而，组合模型显著减轻了数据标注的负担。