分位点回帰とフローによる運転行動分布の裾分位点の学習
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
自動運転車との相互作用における人間ドライバーの行動分布の多様性と裾分位点を捉えるため、分位点回帰と自己回帰分位点フローを行動クローニングで訓練し、高速道路の軌跡データで評価した。
著者: Jia Yu Tee, Oliver De Candido, Wolfgang Utschick, Philipp Geiger
分類: cs.LG, cs.RO
原文アブストラクト
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.
