  Predicting the future motion of vehicles has been studied using various
techniques, including stochastic policies, generative models, and regression.
Recent work has shown that classification over a trajectory set, which
approximates possible motions, achieves state-of-the-art performance and avoids
issues like mode collapse. However, map information and the physical
relationships between nearby trajectories is not fully exploited in this
formulation. We build on classification-based approaches to motion prediction
by adding an auxiliary loss that penalizes off-road predictions. This auxiliary
loss can easily be pretrained using only map information (e.g., off-road area),
which significantly improves performance on small datasets. We also investigate
weighted cross-entropy losses to capture spatial-temporal relationships among
trajectories. Our final contribution is a detailed comparison of classification
and ordinal regression on two public self-driving datasets.
