  End-to-end driving systems have recently made rapid progress, in particular
on CARLA. Independent of their major contribution, they introduce changes to
minor system components. Consequently, the source of improvements is unclear.
We identify two biases that recur in nearly all state-of-the-art methods and
are critical for the observed progress on CARLA: (1) lateral recovery via a
strong inductive bias towards target point following, and (2) longitudinal
averaging of multimodal waypoint predictions for slowing down. We investigate
the drawbacks of these biases and identify principled alternatives. By
incorporating our insights, we develop TF++, a simple end-to-end method that
ranks first on the Longest6 and LAV benchmarks, gaining 11 driving score over
the best prior work on Longest6.
