For autonomous cars to drive safely and effectively, they must anticipate the stochastic future trajectories of other agents in the scene, such as pedestrians and other cars. Forecasting such complex multi-modal distributions requires powerful probabilistic approaches. Normalizing flows have recently emerged as an attractive tool to model such distributions. However, when generating trajectory predictions from a flow model, a key drawback is that independent samples often do not adequately capture all the modes in the underlying distribution. We propose Diversity Sampling for Flow (DSF), a method for improving the quality and the diversity of trajectory samples from a pre-trained flow model. Rather than producing individual samples, DSF produces a set of trajectories in one shot. Given a pre-trained forecasting flow model, we train DSF 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 between trajectories. DSF is easy to implement, and we show that it offers a simple plug-in improvement for several existing flow-based forecasting models, achieving state-of-art results on two challenging vehicle and pedestrian forecasting benchmarks.

本文提出了一种基于概率建模的多模式数学预测的新方法：Likelihood-Based Diverse Sampling（LDS），通过使用预训练的流模型和优化目标函数以提高轨迹样本的质量和多样性，可以明显提高模型预测的准确性和数据生成的有效性，并在多个有挑战性的基准测试中得到验证。

基于似然的轨迹预测多样性采样