  The use of learning-based methods for vehicle behavior prediction is a
promising research topic. However, many publicly available data sets suffer
from class distribution skews which limits learning performance if not
addressed. This paper proposes an interaction-aware prediction model consisting
of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning
technique, the multiclass balancing ensemble is proposed. Evaluations show that
the method enhances model performance, resulting in improved classification
accuracy. Good generalization properties of learned models are important and
therefore a generalization study is done where models are evaluated on unseen
traffic data with dissimilar traffic behavior stemming from different road
configurations. This is realized by using two distinct highway traffic
recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover,
methods for encoding structural and static features into the learning process
for improved generalization are evaluated. The resulting methods show
substantial improvements in classification as well as generalization
performance.
