CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Prediction

TL;DR

This paper introduces CUQDS, a framework that enhances trajectory prediction models by providing reliable uncertainty quantification under distribution shifts, combining Gaussian process regression and conformal calibration for safer autonomous vehicle navigation.

Contribution

The paper proposes CUQDS, a novel framework that improves uncertainty quantification and prediction accuracy of existing models under distribution shifts in real-time scenarios.

Findings

Effective uncertainty reduction during training.

Reliable uncertainty calibration under distribution shift.

Compatible with existing trajectory prediction models.

Abstract

Trajectory prediction models that can infer both finite future trajectories and their associated uncertainties of the target vehicles in an online setting (e.g., real-world application scenarios) is crucial for ensuring the safe and robust navigation and path planning of autonomous vehicle motion. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories of existing trajectory prediction models under potential data distribution shift, while considering improving the prediction accuracy of the models and reducing the estimated uncertainty during the training stage. Specifically, CUQDS includes 1) a learning-based Gaussian process regression module that models the output distribution of the base model (any existing trajectory prediction or time series forecasting neural networks) and reduces the estimated uncertainty by additional loss term, and 2) a statistical-based Conformal P control module to calibrate the estimated uncertainty from the Gaussian process regression module in an online setting under potential distribution shift between training and testing data.