On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 22 May 2023.
Builds on
17 of 34 references.
References · 17
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,317
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- 2017Masked Autoregressive Flow for Density Estimationcited by 680
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- 2019Human Motion Trajectory Prediction: A Surveycited by 231
- 2018Neural Autoregressive Flowscited by 207
- 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 193
- 2019Latent Normalizing Flows for Discrete Sequencescited by 66
- 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
- 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 43
- 2019Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policiescited by 37
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- 2020Solving inverse problems using conditional invertible neural networkscited by 37
- 2019Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMscited by 34
- 2015Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankingscited by 28
- 2021Autoregressive Quantile Flows for Predictive Uncertainty Estimationcited by 15
- 2022Fail-Safe Adversarial Generative Imitation Learningcited by 2
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