Multi-classification of Driver Intentions in Yielding Scenarios

Predictions of the future motion of other vehicles in the vicinity of an autonomous vehicle is required for safe operation on trafficked roads. An important step in order to use proper behavioral models for trajectory prediction is correctly classifying the intentions of drivers. This paper focuses on recognizing the intention of drivers without priority in yielding scenarios at intersections - where the behavior of the driver depends on interaction with other drivers with priority. In these scenarios the behavior can be divided into multiple classes for which we have compared three common classification algorithms: k-nearest neighbors, random forests and support vector machines. Evaluation on a data set of tracked vehicles recorded at an unsignalized intersection show that multiple intentions can be learned and that the support vector machine algorithm exhibits superior classification performance.