Existing trajectory prediction studies intensively leverage generative models. Normalizing flow is one of the genres with the advantage of being invertible to derive the probability density of predicted trajectories. However, mapping from a standard Gaussian by a flow-based model hurts the capacity to capture complicated patterns of trajectories, ignoring the under-represented motion intentions in the training data. To solve the problem, we propose a flow-based model to transform a mixed Gaussian prior into the future trajectory manifold. The model shows a better capacity for generating diverse trajectory patterns. Also, by associating each sub-Gaussian with a certain subspace of trajectories, we can generate future trajectories with controllable motion intentions. In such a fashion, the flow-based model is not encouraged to simply seek the most likelihood of the intended manifold anymore but a family of controlled manifolds with explicit interpretability. Our proposed method is demonstrated to show state-of-the-art performance in the quantitative evaluation of sampling well-aligned trajectories in top-M generated candidates. We also demonstrate that it can generate diverse, controllable, and out-of-distribution trajectories. Code is available at https://github.com/mulplue/MGF.

翻译：现有轨迹预测研究大量利用生成模型。归一化流是其中一类，其优势在于可逆性，能够推导预测轨迹的概率密度。然而，基于流的模型从标准高斯分布映射，会损害捕捉轨迹复杂模式的能力，忽略训练数据中未被充分代表的运动意图。为解决该问题，我们提出一种基于流的模型，将混合高斯先验变换至未来轨迹流形。该模型展现出生成多样化轨迹模式的更强能力。此外，通过将每个子高斯分布与特定的轨迹子空间关联，我们能够生成具有可控运动意图的未来轨迹。通过这种方式，基于流的模型不再被鼓励仅仅寻求意图流形的最大似然，而是寻求一系列具有显式可解释性的受控流形。我们的方法在定量评估中表现出最先进的性能，能够从前M个生成候选样本中采样高度对齐的轨迹。我们还证明该方法能够生成多样化、可控且分布外的轨迹。代码开源地址：https://github.com/mulplue/MGF。