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.

翻译：在在线设置（例如实际应用场景）中能够推断目标车辆有限未来轨迹及其相关不确定性的轨迹预测模型，对于确保自动驾驶车辆运动的安全鲁棒导航与路径规划至关重要。然而，现有大多数轨迹预测模型既未在训练阶段将降低不确定性作为优化目标，也未能在潜在分布偏移下的推理阶段提供可靠的不确定性量化。为此，本文提出分布偏移下的保形不确定性量化框架CUQDS，旨在量化现有轨迹预测模型在潜在数据分布偏移下预测轨迹的不确定性，同时考虑提升模型预测精度并在训练阶段降低估计不确定性。具体而言，CUQDS包含：1）基于学习的高斯过程回归模块，该模块对基础模型（任何现有轨迹预测或时间序列预测神经网络）的输出分布进行建模，并通过附加损失项降低估计不确定性；2）基于统计的保形P控制模块，用于在训练与测试数据间存在潜在分布偏移的在线场景下，校准来自高斯过程回归模块的估计不确定性。