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... [ view full abstract ]

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.