  Pedestrians and drivers interact closely in a wide range of environments.
Autonomous vehicles (AVs) correspondingly face the need to predict pedestrians'
future trajectories in these same environments. Traditional model-based
prediction methods have been limited to making predictions in highly structured
scenes with signalized intersections, marked crosswalks, or curbs. Deep
learning methods have instead leveraged datasets to learn predictive features
that generalize across scenes, at the cost of model interpretability. This
paper aims to achieve both widely applicable and interpretable predictions by
proposing a risk-based attention mechanism to learn when pedestrians yield, and
a model of vehicle influence to learn how yielding affects motion. A novel
probabilistic method, Off the Sidewalk Predictions (OSP), uses these to achieve
accurate predictions in both shared spaces and traditional scenes. Experiments
on urban datasets demonstrate that the realtime method achieves
state-of-the-art performance.
