It is essential but challenging 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 behavior styles. It means that even for the same physical type of agents, there are huge differences in their behavior preferences. Although recent works have made significant progress in studying agents multi-modal plannings, most of them still apply the same prediction strategy to all agents, which makes them difficult to fully show the multiple styles of vast agents. In this paper, we propose the Multi-Style Network (MSN) to focus on this problem by divide agents preference styles into several hidden behavior categories adaptively and train each categorys prediction network separately, therefore giving agents all styles of predictions simultaneously. Experiments demonstrate that our deterministic MSN-D and generative MSN-G outperform many recent state-of-the-art methods and show better multi-style characteristics in the visualized results.
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