Kinematic priors have shown to be helpful in boosting generalization and performance in prior work on trajectory forecasting. Specifically, kinematic priors have been applied such that models predict a set of actions instead of future output trajectories. By unrolling predicted trajectories via time integration and models of kinematic dynamics, predicted trajectories are not only kinematically feasible on average but also relate uncertainty from one timestep to the next. With benchmarks supporting prediction of multiple trajectory predictions, deterministic kinematic priors are less and less applicable to current models. We propose a method for integrating probabilistic kinematic priors into modern probabilistic trajectory forecasting architectures. The primary difference between our work and previous techniques is the analytical quantification of variance, or uncertainty, in predicted trajectories. With negligible additional computational overhead, our method can be generalized and easily implemented with any modern probabilistic method that models candidate trajectories as Gaussian distributions. In particular, our method works especially well in unoptimal settings, such as with small datasets or in the presence of noise. Our method achieves up to a 50% performance boost in small dataset settings and up to an 8% performance boost in large-scale learning compared to previous kinematic prediction methods on SOTA trajectory forecasting architectures out-of-the-box, with minimal fine-tuning. In this paper, we show four analytical formulations of probabilistic kinematic priors which can be used for any Gaussian Mixture Model (GMM)-based deep learning models, quantify the error bound on linear approximations applied during trajectory unrolling, and show results to evaluate each formulation in trajectory forecasting.

翻译：运动学先验已被证明能够提升轨迹预测领域的泛化能力和性能。具体而言，运动学先验的应用使得模型能够预测一组动作而非直接输出未来轨迹。通过时间积分和运动学动力学模型对预测轨迹进行展开，所得轨迹不仅在平均意义上满足运动学可行性，还能将不确定性在时间步之间传递。随着基准测试日益支持多轨迹预测，确定性运动学先验对当前模型的适用性逐渐降低。本文提出一种将概率化运动学先验整合到现代概率轨迹预测架构中的方法。本研究与先前技术的主要区别在于对预测轨迹方差（即不确定性）的解析量化。该方法在可忽略的额外计算开销下，可泛化并轻松应用于任何将候选轨迹建模为高斯分布的现代概率方法。特别地，该方法在非理想场景（如小数据集或存在噪声时）表现尤为突出。相较于开箱即用的先进轨迹预测架构中既往运动学预测方法，本方法在小数据集场景中可实现高达50%的性能提升，在大规模学习中亦可实现高达8%的性能提升，且仅需极少量微调。本文提出四种可用于任何基于高斯混合模型（GMM）的深度学习模型的概率运动学先验解析形式，量化了轨迹展开过程中线性近似的误差边界，并通过轨迹预测实验评估了每种形式的性能。