  Multimodal prediction results are essential for trajectory prediction task as
there is no single correct answer for the future. Previous frameworks can be
divided into three categories: regression, generation and classification
frameworks. However, these frameworks have weaknesses in different aspects so
that they cannot model the multimodal prediction task comprehensively. In this
paper, we present a novel insight along with a brand-new prediction framework
by formulating multimodal prediction into three steps: modality clustering,
classification and synthesis, and address the shortcomings of earlier
frameworks. Exhaustive experiments on popular benchmarks have demonstrated that
our proposed method surpasses state-of-the-art works even without introducing
social and map information. Specifically, we achieve 19.2% and 20.8%
improvement on ADE and FDE respectively on ETH/UCY dataset. Our code will be
made publicly availabe.
