It is essential to predict future trajectories of various agents in complex scenes. Whether it is internal personality factors of agents, interactive behavior of the neighborhood, or the influence of surroundings, it will have an impact on their future plannings. It means that even for the same physical type of agents, there are huge differences in their behavior styles. We concentrate on the problem of modeling agents' multi-style characteristics when predicting their trajectories. We propose the Multi-Style Network (MSN) to focus on this problem by dividing agents' behaviors into several hidden behavior categories adaptively, and then train each category's prediction network jointly, thus giving agents multiple styles of predictions simultaneously. Experiments show that MSN outperforms current state-of-the-art methods with 10\% - 20\% performance improvement on two widely used datasets, and presents better multi-style characteristics in predictions.

翻译：预测各种代理人在复杂场景中的未来轨迹至关重要。 不管是代理人的内部个性因素、 邻居的互动行为, 还是周围环境的影响, 它都会对其未来的规划产生影响。 这意味着即使对相同的代理人而言, 其行为风格也存在巨大的差异。 我们在预测其轨迹时集中关注模拟代理人的多式特征问题。 我们建议多级网络(MSN)通过将代理人的行为分为若干隐藏的行为类别来适应, 然后联合培训每一类的预测网络, 从而同时给代理人提供多种预测方式。 实验显示, MSN在两种广泛使用的数据集上表现改进了10° - 20° 的当前最先进的方法, 并在预测中提出了更好的多式特征 。