Trajectory prediction is an important task to support safe and intelligent behaviours in autonomous systems. Many advanced approaches have been proposed over the years with improved spatial and temporal feature extraction. However, human behaviour is naturally multimodal and uncertain: given the past trajectory and surrounding environment information, an agent can have multiple plausible trajectories in the future. To tackle this problem, an essential task named multimodal trajectory prediction (MTP) has recently been studied, which aims to generate a diverse, acceptable and explainable distribution of future predictions for each agent. In this paper, we present the first survey for MTP with our unique taxonomies and comprehensive analysis of frameworks, datasets and evaluation metrics. In addition, we discuss multiple future directions that can help researchers develop novel multimodal trajectory prediction systems.

翻译：轨迹预测是支持自主系统安全与智能行为的重要任务。近年来，随着空间与时间特征提取技术的改进，许多先进方法被提出。然而，人类行为本质上具有多模态性和不确定性：基于历史轨迹及周围环境信息，一个智能体未来可能存在多条合理轨迹。为解决这一问题，近期出现了一项关键任务——多模态轨迹预测（MTP），其目标是为每个智能体生成多样化、可接受且可解释的未来预测分布。本文首次对MTP领域进行综述，提出了独特的分类体系，并对框架、数据集与评估指标进行了全面分析。此外，我们探讨了多个未来研究方向，以帮助研究者开发新型多模态轨迹预测系统。