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.

翻译：运动预测对于自动驾驶系统理解复杂驾驶场景并做出明智决策至关重要。然而，由于交通参与者的多样化行为以及复杂的环境背景，这一任务极具挑战性。本文提出运动Transformer（MTR）框架以应对这些挑战。初始MTR框架采用带有可学习意图查询的Transformer编码器-解码器结构，能够高效准确地预测未来轨迹。通过为不同运动模态定制意图查询，MTR在提高多模态运动预测性能的同时减少了对密集目标候选点的依赖。该框架包含两个核心过程：全局意图定位，用于识别智能体意图以提升整体效率；以及局部运动精化，通过自适应优化预测轨迹来提高精度。此外，我们引入先进的MTR++框架，将MTR的能力扩展至同时预测多个智能体的多模态运动。MTR++集成了对称上下文建模与相互引导意图查询模块，以促进多智能体间的未来行为交互，从而生成符合场景约束的未来轨迹。大量实验结果表明，MTR框架在极具竞争力的运动预测基准上达到了先进水平，而MTR++框架在预测多个智能体精确多模态未来轨迹方面超越了其前身，展现出更优的性能与效率。