  Predicting the behavior of surrounding traffic participants is crucial for
advanced driver assistance systems and autonomous driving. Most researchers
however do not consider contextual knowledge when predicting vehicle motion.
Extending former studies, we investigate how predictions are affected by
external conditions. To do so, we categorize different kinds of contextual
information and provide a carefully chosen definition as well as examples for
external conditions. More precisely, we investigate how a state-of-the-art
approach for lateral motion prediction is influenced by one selected external
condition, namely the traffic density. Our investigations demonstrate that this
kind of information is highly relevant in order to improve the performance of
prediction algorithms. Therefore, this study constitutes the first step towards
the integration of such information into automated vehicles. Moreover, our
motion prediction approach is evaluated based on the public highD data set
showing a maneuver prediction performance with areas under the ROC curve above
97% and a median lateral prediction error of only 0.18m on a prediction horizon
of 5s.
