Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis

Jianhua Sun; Yuxuan Li; Hao-Shu Fang; Cewu Lu

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arXiv · 2103.07854

Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis

Jianhua Sun·Yuxuan Li·Hao-Shu Fang·Cewu Lu

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Abstract

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.

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Jianhua Sun, Yuxuan Li, Hao-Shu Fang, Cewu Lu. 2021-03-14. Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis. https://arxiv.org/abs/2103.07854

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