  In this paper, we present an end-to-end future-prediction model that focuses
on pedestrian safety. Specifically, our model uses previous video frames,
recorded from the perspective of the vehicle, to predict if a pedestrian will
cross in front of the vehicle. The long term goal of this work is to design a
fully autonomous system that acts and reacts as a defensive human driver would
--- predicting future events and reacting to mitigate risk. We focus on
pedestrian-vehicle interactions because of the high risk of harm to the
pedestrian if their actions are miss-predicted. Our end-to-end model consists
of two stages: the first stage is an encoder/decoder network that learns to
predict future video frames. The second stage is a deep spatio-temporal network
that utilizes the predicted frames of the first stage to predict the
pedestrian's future action. Our system achieves state-of-the-art accuracy on
pedestrian behavior prediction and future frames prediction on the Joint
Attention for Autonomous Driving (JAAD) dataset.
