Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis
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Multimodal prediction results are essential for trajectory forecasting 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 improvement on ADE and FDE respectively on ETH/UCY dataset. Our code will be made publicly available.
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