Motion prediction is a crucial task in autonomous driving, and one of its
major challenges lands in the multimodality of future behaviors. Many
successful works have utilized mixture models which require identification of
positive mixture components, and correspondingly fall into two main lines:
prediction-based and anchor-based matching. The prediction clustering
phenomenon in prediction-based matching makes it difficult to pick
representative trajectories for downstream tasks, while the anchor-based
matching suffers from a limited regression capability. In this paper, we
introduce a novel paradigm, named Evolving and Distinct Anchors (EDA), to
define the positive and negative components for multimodal motion prediction
based on mixture models. We enable anchors to evolve and redistribute
themselves under specific scenes for an enlarged regression capacity.
Furthermore, we select distinct anchors before matching them with the ground
truth, which results in impressive scoring performance. Our approach enhances
all metrics compared to the baseline MTR, particularly with a notable relative
reduction of 13.5% in Miss Rate, resulting in state-of-the-art performance on
the Waymo Open Motion Dataset. Code is available at
this https URL

基于混合模型的多模式运动预测中，我们引入了一种新的范例 Evolution and Distinct Anchors (EDA)，用于定义多模态运动预测的正负组件，通过使锚点在特定场景下演化和重新分布以扩大回归能力，并在与地面真实值匹配之前选择不同的锚点，从而提高了所有度量标准，在 Waymo Open Motion 数据集上表现出卓越的得分性能。