  Automated vehicles require a comprehensive understanding of traffic
situations to ensure safe and comfortable driving. In this context, the
prediction of pedestrians is particularly challenging as pedestrian behavior
can be influenced by multiple factors. In this paper, we thoroughly analyze the
requirements on pedestrian behavior prediction for automated driving via a
system-level approach: to this end we investigate real-world pedestrian-vehicle
interactions with human drivers. Based on human driving behavior we then derive
appropriate reaction patterns of an automated vehicle. Finally, requirements
for the prediction of pedestrians are determined. This also includes a novel
metric tailored to measure prediction performance from a system-level
perspective. Furthermore, we present a pedestrian prediction model based on a
Conditional Variational Auto-Encoder (CVAE) which incorporates multiple
contextual cues to achieve accurate long-term prediction. The CVAE shows
superior performance over a baseline prediction model, where prediction
performance was evaluated on a large-scale data set comprising thousands of
real-world pedestrian-vehicle-interactions. Finally, we investigate the impact
of different contextual cues on prediction performance via an ablation study
whose results can guide future research on the perception of relevant
pedestrian attributes.
