用对抗方法增强合成行人轨迹，提升预测模型在真实场景的表现。
AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
- 训练时用对抗机制优化合成轨迹数据
- 在真实数据集上轨迹预测准确率显著提升
- 适合需要高质量合成数据的自动驾驶研究
准确预测行人轨迹对自动驾驶、服务机器人等应用至关重要。深度生成模型在此任务中表现优异，但依赖大量标注轨迹数据。目前存在大量由游戏生成的合成轨迹，但其运动模式不真实，难以有效训练预测模型。本文提出一种方法与架构，在训练阶段通过对抗方式增强合成轨迹。实验表明，该方法在使用先进生成模型评估真实世界轨迹时，带来显著性能提升。
原文摘要 · Abstract (English)
Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generated by video games). However, such trajectories are not meant to represent pedestrian motion realistically and are ineffective at training a predictive model. We propose a method and an architecture to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories.
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