Identification of Driving Scenarios and Driving Styles Using Machine Learning
The data-based verification of autonomous driving functionalities requires the detection of driving scenarios and driving styles. Driving scenarios define the tasks that the functionalities have to master, whereas the driving style has an impact on the traffic and thus the recorded traffic scenarios. In this thesis we consider two machine learning models which extract driving scenarios and driving
