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Trajectory prediction aims to forecast agents' possible future locationsconsidering their observations along with the video context. It is stronglyneeded by many autonomous platforms like tracking, detection, robot navigation,and self-driving cars. Whether it is agents' internal personality factors,interactive behaviors with the neighborhood, or the influence of surroundings,they all impact agents' future planning. However, many previous methods modeland predict agents' behaviors with the same strategy or feature distribution,making them challenging to make predictions with sufficient style differences.This paper proposes the Multi-Style Network (MSN), which utilizes styleproposal and stylized prediction using two sub-networks, to provide multi-stylepredictions in a novel categorical way adaptively. The proposed networkcontains a series of style channels, and each channel is bound to a unique andspecific behavior style. We use agents' end-point plannings and theirinteraction context as the basis for the behavior classification, so as toadaptively learn multiple diverse behavior styles through these channels. Then,we assume that the target agents may plan their future behaviors according toeach of these categorized styles, thus utilizing different style channels tomake predictions with significant style differences in parallel. Experimentsshow that the proposed MSN outperforms current state-of-the-art methods up to10% quantitatively on two widely used datasets, and presents better multi-stylecharacteristics qualitatively.

MSN: Multi-Style Network for Trajectory Prediction