Communication between vehicles and internet services is ubiquitous: traffic jam news can be received via the mobile phone network; the parking meter can be activated via smartphone; and workshops can access service messages remotely. It is foreseeable that more and more in-vehicle services will rely on an internet connection and connect to cloud services. Autonomous driving in particular, which depends on up-to-date information on the environment, presents new challenges for communication. This is because good and comprehensive mobile radio coverage is still not guaranteed in rural areas, underground carparks and tunnels in larger cities. This doctoral project, therefore, develops approaches to optimise the communication between vehicles and internet services as well as vehicle-to-vehicle communication. A key research question addresses the prioritisation of data traffic, which enables the transmission of important or safety-related data such as black ice warnings even in weak mobile radio networks. A second core area of this doctoral project approaches methods for predicting mobile radio network coverage. Using machine learning methods, the connection quality will be predicted with consideration for traffic density, weather and building development. In combination with the method of prioritising data traffic, necessary data can thus be exchanged even before connection termination.

Stefan Herrnleben