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

Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction