Classification Of Driver Intentions At Roundabouts

Classification of other drivers' intentions is an important requirement for automated driving. We present two methods to estimate whether a driver leaves a roundabout. The first, like many other approaches to this problem, requires training data of the specific roundabout to extract typical behavior patterns. Afterwards, these patterns are used for classification of other drivers' intentions. The second approach generates typical behavior patterns from a precise map. Consequently, no training data is required and classification can be performed on arbitrary roundabouts as long as a map is available. Experimental evaluation on a real world dataset of 266 trajectories shows that the performance of the map-based approach is comparable to the data-driven approach. The classification result can be used in a later stage for behavior planning of automated vehicles or driver assistance systems.