On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
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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 simulation and reality. To this end, we adapt two flexible 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 evaluate our approach in a one-step prediction, as well as in multi-step simulation rollouts. We use the highD dataset consisting of driver trajectories on several highways. 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.
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