Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method.

中文翻译：

---

通过分布判别的个性化轨迹预测

轨迹预测面临着以多样性和准确性捕捉未来动态的多模态性质的困境。在本文中，我们提出了一种分布鉴别（DisDis）方法，通过区分潜在分布来预测个性化运动模式。由于每个人的运动模式因他/她的习惯而个性化，我们的 DisDis 学习潜在分布以表示不同的运动模式，并通过对比辨别对其进行优化。这种分布歧视鼓励潜在分布更具歧视性。我们的方法可以作为即插即用模块与现有的多模态随机预测模型集成，以学习更具辨别力的潜在分布。为了评估潜在分布，我们进一步提出了一个新指标，概率累积最小距离 (PCMD) 曲线，它累积计算已排序概率的最小距离。在 ETH 和 UCY 数据集上的实验结果表明了我们方法的有效性。