  Self-driving vehicles plan around both static and dynamic objects, applying
predictive models of behavior to estimate future locations of the objects in
the environment. However, future behavior is inherently uncertain, and models
of motion that produce deterministic outputs are limited to short timescales.
Particularly difficult is the prediction of human behavior. In this work, we
propose the discrete residual flow network (DRF-Net), a convolutional neural
network for human motion prediction that captures the uncertainty inherent in
long-range motion forecasting. In particular, our learned network effectively
captures multimodal posteriors over future human motion by predicting and
updating a discretized distribution over spatial locations. We compare our
model against several strong competitors and show that our model outperforms
all baselines.
