One of the fundamental challenges in the prediction of dynamic agents is robustness. Usually, most predictions are deterministic estimates of future states which are over-confident and prone to error. Recently, few works have addressed capturing uncertainty during forecasting of future states. However, these probabilistic estimation methods fail to account for the upstream noise in perception data during tracking. Sensors always have noise and state estimation becomes even more difficult under adverse weather conditions and occlusion. Traditionally, Bayes filters have been used to fuse information from noisy sensors to update states with associated belief. But, they fail to address non-linearities and long-term predictions. Therefore, we propose an end-to-end estimator that can take noisy sensor measurements and make robust future state predictions with uncertainty bounds while simultaneously taking into consideration the upstream perceptual uncertainty. For the current research, we consider an encoder-decoder based deep ensemble network for capturing both perception and predictive uncertainty simultaneously. We compared the current model to other approximate Bayesian inference methods. Overall, deep ensembles provided more robust predictions and the consideration of upstream uncertainty further increased the estimation accuracy for the model.

翻译：动态智能体预测的根本挑战之一在于鲁棒性。通常，大多数预测都是对未来状态的确定性估计，这种估计过度自信且容易出错。近年来，少数研究致力于捕捉未来状态预测过程中的不确定性。然而，这些概率估计方法未能考虑跟踪过程中感知数据的上游噪声。传感器始终存在噪声，而在恶劣天气条件和遮挡情况下，状态估计变得更为困难。传统上，贝叶斯滤波器被用于融合含噪传感器的信息，通过关联置信度更新状态。但这类方法难以处理非线性问题及长期预测。因此，我们提出了一种端到端估计器，该估计器能够接收含噪传感器测量数据，在考虑上游感知不确定性的同时，做出带有不确定性边界的鲁棒未来状态预测。在本研究中，我们采用基于编码器-解码器的深度集成网络，同时捕捉感知不确定性和预测不确定性。我们将当前模型与其他近似贝叶斯推断方法进行了比较。总体而言，深度集成提供了更鲁棒的预测，而考虑上游不确定性进一步提升了模型的估计精度。