  This paper presents a novel approach to pedestrian trajectory prediction for
on-board camera systems, which utilizes behavioral features of pedestrians that
can be inferred from visual observations. Our proposed method, called
Behavior-Aware Pedestrian Trajectory Prediction (BA-PTP), processes multiple
input modalities, i.e. bounding boxes, body and head orientation of pedestrians
as well as their pose, with independent encoding streams. The encodings of each
stream are fused using a modality attention mechanism, resulting in a final
embedding that is used to predict future bounding boxes in the image.
  In experiments on two datasets for pedestrian behavior prediction, we
demonstrate the benefit of using behavioral features for pedestrian trajectory
prediction and evaluate the effectiveness of the proposed encoding strategy.
Additionally, we investigate the relevance of different behavioral features on
the prediction performance based on an ablation study.
