The \ac{CVAE} is one of the most widely-used models in trajectory prediction for \ac{AD}. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance. We find that unscented sampling, which draws samples from any learned distribution in a deterministic manner, can naturally be better suited to trajectory prediction than potentially dangerous random sampling. We go further and offer additional improvements, including a more structured mixture latent space, as well as a novel, potentially more expressive way to do inference with CVAEs. We show wide applicability of our models by evaluating them on the INTERACTION prediction dataset, outperforming the state of the art, as well as at the task of image modeling on the CelebA dataset, outperforming the baseline vanilla CVAE. Code is available at https://github.com/boschresearch/cuae-prediction.

翻译：条件变分自编码器（CVAE）是自动驾驶轨迹预测任务中最广泛使用的模型之一。该模型将驾驶环境与其真实未来轨迹之间的相互作用编码到概率隐空间中，并利用该隐空间生成预测结果。本文对CVAE的核心组成部分提出质疑。我们利用变分自编码器（CVAE的基石）领域的最新进展，证明采样过程的简单改进即可显著提升性能。研究发现，无迹采样——以确定性方式从任意已学习分布中抽取样本——相较于具有潜在风险的随机采样，天然更适合轨迹预测任务。我们进一步提出多项改进，包括更具结构化的混合隐空间，以及一种新颖且可能更具表达力的CVAE推理方法。通过在INTERACTION预测数据集上超越当前最优方法，以及在CelebA数据集上通过图像建模任务超越基线标准CVAE，我们验证了所提模型的广泛适用性。代码见https://github.com/boschresearch/cuae-prediction。