  Pedestrians are particularly vulnerable road users in urban traffic. With the
arrival of autonomous driving, novel technologies can be developed specifically
to protect pedestrians. We propose a machine learning toolchain to train
artificial neural networks as models of pedestrian behavior. In a preliminary
study, we use synthetic data from simulations of a specific pedestrian crossing
scenario to train a variational autoencoder and a long short-term memory
network to predict a pedestrian's future visual perception. We can accurately
predict a pedestrian's future perceptions within relevant time horizons. By
iteratively feeding these predicted frames into these networks, they can be
used as simulations of pedestrians as indicated by our results. Such trained
networks can later be used to predict pedestrian behaviors even from the
perspective of the autonomous car. Another future extension will be to re-train
these networks with real-world video data.
