Learning-Based Quality of Service Prediction in Cellular Vehicle Communication
Redner: Josef Schmid
Termin: 14:00, 19 April 2021
At the moment, nearly all automotive manufactures as well as a lot of newcomers like Google and Tesla are working in the area of automated driving. Since today’s automated driving solutions are based on onboard sensor technologies like radar, laser or camera systems, their observing range is limited to about 250m in front of the vehicle. In the case of a need for transfer of the driving task from automated mode to the driver, drivers need some time to react. Therefore, the vehicle needs an extended observing area that can be achieved by communication. A common approach for such a communication is to use a mobile network connection. But due to temporary lack of radio coverage, the mobile networks link e.g. LTE or 5G are not as stable as needed. To improve the quality of service of the mobile network, it is a key objective to analyse the behaviour of the mobile network in certain driving scenarios. This presentation introduces a method for how to record, collect and analyse such a communication. In addition, two different approaches to predict the state of the mobile connection are shown. The first is a geo-based solution using connectivity maps. The second uses different machine learning regression models to achieve this goal.
Josef Schmid received his B. Eng. in 2014 and his M. Sc. in Applied Research in Engineering Sciences in March 2016 at the OTH Amberg-Weiden. During his master studies, he started working as research associate at the Faculty of Electrical, Information and Media at the OTH Amberg-Weiden. Since Mai 2016 his research focus is on mobile network based vehicle communication for cooperative highly automated driving. His main research interests are vehicle to X communication (V2X) as well as quality of service for mobile network and machine learning methods.