Reliable uncertainty quantification in deep neural networks is very crucial
in safety-critical applications such as automated driving for trustworthy and
informed decision-making. Assessing the quality of uncertainty estimates is
challenging as ground truth for uncertainty estimates is not available.
Ideally, in a well-calibrated model, uncertainty estimates should perfectly
correlate with model error. We propose a novel error aligned uncertainty
optimization method and introduce a trainable loss function to guide the models
to yield good quality uncertainty estimates aligning with the model error. Our
approach targets continuous structured prediction and regression tasks, and is
evaluated on multiple datasets including a large-scale vehicle motion
prediction task involving real-world distributional shifts. We demonstrate that
our method improves average displacement error by 1.69% and 4.69%, and the
uncertainty correlation with model error by 17.22% and 19.13% as quantified by
Pearson correlation coefficient on two state-of-the-art baselines.

通过提出基于误差对齐的不确定性优化方法和引入可训练的损失函数来估算深度神经网络的不确定性，以取得与模型误差相关性强的不确定性预测。该方法适用于连续结构化预测和回归任务，并在包括大规模车辆运动预测任务的多个数据集上进行评估，通过 Pearson 相关系数表明我们的方法提高了平均位移误差和不确定性与模型误差之间的相关性。