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：尽管在自动驾驶轨迹预测方面已有大量研究工作，但鲜有研究关注预测可靠性评估。为弥补这一局限，我们提出了一种覆盖两种误差来源的方法：通过分布外（OOD）检测应对新异情境，以及通过不确定性估计处理分布内（ID）情境的复杂性。我们在用于轨迹预测的编码器-解码器网络旁引入了两个模块。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，随后用于检测特征空间中似然值较低区域的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，在Shifts鲁棒轨迹预测数据集上，这两个模块的组合在OOD检测和不确定性估计方面分别超越现有技术2.8%和10.1%。代码已公开。