  ML-based motion planning is a promising approach to produce agents that
exhibit complex behaviors, and automatically adapt to novel environments. In
the context of autonomous driving, it is common to treat all available training
data equally. However, this approach produces agents that do not perform
robustly in safety-critical settings, an issue that cannot be addressed by
simply adding more data to the training set - we show that an agent trained
using only a 10% subset of the data performs just as well as an agent trained
on the entire dataset. We present a method to predict the inherent difficulty
of a driving situation given data collected from a fleet of autonomous vehicles
deployed on public roads. We then demonstrate that this difficulty score can be
used in a zero-shot transfer to generate curricula for an imitation-learning
based planning agent. Compared to training on the entire unbiased training
dataset, we show that prioritizing difficult driving scenarios both reduces
collisions by 15% and increases route adherence by 14% in closed-loop
evaluation, all while using only 10% of the training data.
