A Comparison of AI-Based Throughput Prediction for Cellular Vehicle-To-Server Communication

Nowadays, on-board sensor data is primarily used to detect nascent threats during automated driving. Since the range of this data is locally restricted, centralized server architectures are taken into consideration to alleviate challenges caused by highly automated driving at higher speeds. Therefore, a server accumulates this sensor data and provides aggregated information about the traffic situation utilizing mobile network-based vehicle to server communication. To schedule communication traffic on this fluctuating channel reliably, various approaches on throughput prediction are conducted. On one hand there are models based on aggregation depending on the position, e.g. connectivity maps. On the other hand there are traditional machine learning approaches, i.a. Support Vector Regression. This work implements the latter including OSM-based feature engineering and conducts a comprehensive comparison on the performance of these models utilizing a uniform dataset.