Trajectory prediction aims to forecast agents' possible future locations considering their observations along with the video context. It is strongly needed 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 model and 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 style proposal and stylized prediction using two sub-networks, to provide multi-style predictions in a novel categorical way adaptively. The proposed network contains a series of style channels, and each channel is bound to a unique and specific behavior style. We use agents' end-point plannings and their interaction context as the basis for the behavior classification, so as to adaptively learn multiple diverse behavior styles through these channels. Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel. Experiments show that the proposed MSN outperforms current state-of-the-art methods up to 10% quantitatively on two widely used datasets, and presents better multi-style characteristics qualitatively.

翻译：轨迹预测旨在根据智能体的观测信息及视频上下文预测其未来可能的运动位置，这在跟踪、检测、机器人导航和自动驾驶等自主系统中具有重要应用需求。无论是智能体的内在个性因素、与邻域的交互行为，还是环境的影响，都会影响其未来规划。然而，许多先前的方法采用相同策略或特征分布来建模和预测智能体行为，导致难以生成具有充分风格差异的预测。本文提出多风格网络（MSN），通过风格提议和风格化预测两个子网络，以新颖的分类方式自适应地提供多风格预测。该网络包含一系列风格通道，每个通道绑定一种独特且具体的行为风格。我们利用智能体的终点规划及其交互上下文作为行为分类依据，从而通过这些通道自适应学习多种不同的行为风格。随后，我们假设目标智能体可能依据这些分类风格中的每一种规划其未来行为，从而并行利用不同风格通道生成具有显著风格差异的预测。实验表明，所提出的MSN在两个广泛使用的数据集上，定量指标相较当前最先进方法提升了最高10%，并在定性分析中展现出更优的多风格特征。