  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.
