Forecasting complex vehicle and pedestrian multi-modal distributions requires powerful probabilistic approaches. Normalizing flows (NF) have recently emerged as an attractive tool to model such distributions. However, a key drawback is that independent samples drawn from a flow model often do not adequately capture all the modes in the underlying distribution. We propose Likelihood-Based Diverse Sampling (LDS), a method for improving the quality and the diversity of trajectory samples from a pre-trained flow model. Rather than producing individual samples, LDS produces a set of trajectories in one shot. Given a pre-trained forecasting flow model, we train LDS using gradients from the model, to optimize an objective function that rewards high likelihood for individual trajectories in the predicted set, together with high spatial separation among trajectories. LDS outperforms state-of-art post-hoc neural diverse forecasting methods for various pre-trained flow models as well as conditional variational autoencoder (CVAE) models. Crucially, it can also be used for transductive trajectory forecasting, where the diverse forecasts are trained on-the-fly on unlabeled test examples. LDS is easy to implement, and we show that it offers a simple plug-in improvement over baselines on two challenging benchmarks. Code is at: https://github.com/JasonMa2016/LDS
翻译:预测复杂的车辆和行人多式分布需要强大的概率方法。 正常流动(NF)最近成为模拟这种分布的有吸引力的工具。 但是,一个关键的缺点是,从流动模型中提取的独立样本往往不能充分捕捉基本分布中的所有模式。 我们提议,一种改进预先培训的流量模型的轨迹样本质量和多样性的方法,即“基于可能性的多样化抽样”(LDS),一种提高预培训前流动模型的轨迹样本质量和多样性的方法。LDS不是制作单个样本,而是用一个镜头制作一套轨迹。考虑到预先培训的预测流模型,我们用模型的梯度来培训LDS,以优化一个客观功能,奖励预测中单个轨迹的高度可能性,同时在轨迹图中进行高度的空间分隔。 LDS 超越了各种预培训前流动模型以及有条件的变异性自动电解模型(CVAE) 。 关键是,它也可以用于转导轨迹轨迹预测,使用该模型的梯度梯度预测,在其中,对简单的LDS标准进行了测试。