Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we propose an approach that covers two sources of error, namely novel situations with out-of-distribution (OOD) detection and the complexity in in-distribution (ID) situations with uncertainty estimation. We introduce two modules next to an encoder-decoder network for trajectory prediction. Firstly, a Gaussian mixture model learns the probability density function of the ID encoder features during training, and then it is used to detect the OOD samples in regions of the feature space with low likelihood. Secondly, an error regression network is applied to the encoder, which learns to estimate the trajectory prediction error in supervised training. During inference, the estimated prediction error is used as the uncertainty. In our experiments, the combination of both modules outperforms the prior work in OOD detection and uncertainty estimation, on the Shifts robust trajectory prediction dataset by $2.8 \%$ and $10.1 \%$, respectively. The code is publicly available.

翻译：尽管自动驾驶轨迹预测已有大量研究工作，但针对预测可靠性的评估仍十分有限。为解决这一问题，我们提出了一种涵盖两种误差来源的方法：通过分布外（OOD）检测识别新异场景，以及通过不确定性估计量化分布内（ID）场景的复杂性。我们在编码器-解码器轨迹预测网络的基础上引入两个模块：首先，高斯混合模型在训练阶段学习ID编码器特征的概率密度函数，用于检测特征空间中似然较低区域的OOD样本；其次，在编码器上应用误差回归网络，通过监督训练学习估计轨迹预测误差。在推理阶段，将估计的预测误差作为不确定性指标。实验表明，两个模块的组合在Shifts鲁棒轨迹预测数据集上，OOD检测与不确定性估计性能分别较现有方法提升2.8%和10.1%。相关代码已公开。