  Autonomous driving models should ideally be evaluated by deploying them on a
fleet of physical vehicles in the real world. Unfortunately, this approach is
not practical for the vast majority of researchers. An attractive alternative
is to evaluate models offline, on a pre-collected validation dataset with
ground truth annotation. In this paper, we investigate the relation between
various online and offline metrics for evaluation of autonomous driving models.
We find that offline prediction error is not necessarily correlated with
driving quality, and two models with identical prediction error can differ
dramatically in their driving performance. We show that the correlation of
offline evaluation with driving quality can be significantly improved by
selecting an appropriate validation dataset and suitable offline metrics. The
supplementary video can be viewed at
https://www.youtube.com/watch?v=P8K8Z-iF0cY
