  Predicting future behavior of other traffic participants is an essential task
that needs to be solved by automated vehicles and human drivers alike to
achieve safe and situationaware driving. Modern approaches to vehicles
trajectory prediction typically rely on data-driven models like neural
networks, in particular LSTMs (Long Short-Term Memorys), achieving promising
results. However, the question of optimal composition of the underlying
training data has received less attention. In this paper, we expand on previous
work on vehicle trajectory prediction based on neural network models employing
distributed representations to encode automotive scenes in a semantic vector
substrate. We analyze the influence of variations in the training data on the
performance of our prediction models. Thereby, we show that the models
employing our semantic vector representation outperform the numerical model
when trained on an adequate data set and thereby, that the composition of
training data in vehicle trajectory prediction is crucial for successful
training. We conduct our analysis on challenging real-world driving data.
