The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler, to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is dependent on the previously drawn samples. Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value. This acquisition function applies the original distribution as prior and encourages exploring paths in the long-tail region. This sampling method can be integrated with existing stochastic predictive models without retraining. Experimental results on various baseline methods demonstrate the effectiveness of our method.

翻译：人类运动的不确定性要求轨迹预测系统采用概率模型来表述多模态现象，并推断出有限的未来轨迹集合。然而，由于预测分布的长尾效应，现有方法大多依赖蒙特卡洛随机采样进行推断，难以通过有限样本覆盖真实路径。为提升随机预测的采样过程，我们提出一种名为BOsampler的新方法，以无监督方式通过贝叶斯优化自适应挖掘潜在路径，这是一种顺序设计策略，其中新的预测依赖于先前抽取的样本。具体而言，我们将轨迹采样建模为高斯过程，并构建一个采集函数来衡量潜在采样价值。该函数以原始分布为先验，并鼓励探索长尾区域中的路径。该采样方法可无缝集成至现有随机预测模型中，无需重新训练。在多种基线方法上的实验结果表明了我们的方法的有效性。