  Towards safe autonomous driving (AD), we consider the problem of learning
models that accurately capture the diversity and tail quantiles of human driver
behavior probability distributions, in interaction with an AD vehicle. Such
models, which predict drivers' continuous actions from their states, are
particularly relevant for closing the gap between AD agent simulations and
reality. To this end, we adapt two flexible quantile learning frameworks for
this setting that avoid strong distributional assumptions: (1) quantile
regression (based on the titled absolute loss), and (2) autoregressive quantile
flows (a version of normalizing flows). Training happens in a behavior
cloning-fashion. We use the highD dataset consisting of driver trajectories on
several highways. We evaluate our approach in a one-step acceleration
prediction task, and in multi-step driver simulation rollouts. We report
quantitative results using the tilted absolute loss as metric, give qualitative
examples showing that realistic extremal behavior can be learned, and discuss
the main insights.
