  By observing their environment as well as other traffic participants, humans
are enabled to drive road vehicles safely. Vehicle passengers, however,
perceive a notable difference between non-experienced and experienced drivers.
In particular, they may get the impression that the latter ones anticipate what
will happen in the next few moments and consider these foresights in their
driving behavior. To make the driving style of automated vehicles comparable to
the one of human drivers with respect to comfort and perceived safety, the
aforementioned anticipation skills need to become a built-in feature of
self-driving vehicles. This article provides a systematic comparison of methods
and strategies to generate this intention for self-driving cars using machine
learning techniques. To implement and test these algorithms we use a large data
set collected over more than 30000 km of highway driving and containing
approximately 40000 real-world driving situations. We further show that it is
possible to classify driving maneuvers upcoming within the next 5 s with an
Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes.
This enables us to predict the lateral position with a prediction horizon of 5
s with a median lateral error of less than 0.21 m.
