Motion prediction is crucial for autonomous driving systems to understand
complex driving scenarios and make informed decisions. However, this task is
challenging due to the diverse behaviors of traffic participants and complex
environmental contexts. In this paper, we propose Motion TRansformer (MTR)
frameworks to address these challenges. The initial MTR framework utilizes a
transformer encoder-decoder structure with learnable intention queries,
enabling efficient and accurate prediction of future trajectories. By
customizing intention queries for distinct motion modalities, MTR improves
multimodal motion prediction while reducing reliance on dense goal candidates.
The framework comprises two essential processes: global intention localization,
identifying the agent's intent to enhance overall efficiency, and local
movement refinement, adaptively refining predicted trajectories for improved
accuracy. Moreover, we introduce an advanced MTR++ framework, extending the
capability of MTR to simultaneously predict multimodal motion for multiple
agents. MTR++ incorporates symmetric context modeling and mutually-guided
intention querying modules to facilitate future behavior interaction among
multiple agents, resulting in scene-compliant future trajectories. Extensive
experimental results demonstrate that the MTR framework achieves
state-of-the-art performance on the highly-competitive motion prediction
benchmarks, while the MTR++ framework surpasses its precursor, exhibiting
enhanced performance and efficiency in predicting accurate multimodal future
trajectories for multiple agents.

本文提出了 Motion TRansformer (MTR) 框架进行自动驾驶中的动态预测，该框架使用可学习的意图查询来提高效率和准确性，适应多种行动方式的需求，同时扩展到多个代理的多模态预测，实现了对高度竞争的运动预测基准的最新表现和有效性的提升。