EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Longzhong Lin Xuewu Lin Tianwei Lin Lichao Huang Rong Xiong Yue Wang
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
https://github.com/Longzhong-Lin/EDA.