Adaptive Uncertainty Quantification for Trajectory Prediction Under Distributional Shift

• URL: http://arxiv.org/abs/2406.12100v1
• Date: Mon, 17 Jun 2024 21:25:36 GMT
• Title: Adaptive Uncertainty Quantification for Trajectory Prediction Under Distributional Shift
• Authors: Huiqun Huang, Sihong He, Fei Miao,
• Abstract summary: Trajectory prediction models can infer both finite future trajectories and their associated uncertainties in an online setting. We propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories.
• Score: 6.029850098632435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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