MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying

Motion prediction is crucial for autonomous driving systems to understandcomplex driving scenarios and make informed decisions. However, this task ischallenging due to the diverse behaviors of traffic participants and complexenvironmental contexts. In this paper, we propose Motion TRansformer (MTR)frameworks to address these challenges. The initial MTR framework utilizes atransformer encoder-decoder structure with learnable intention queries,enabling efficient and accurate prediction of future trajectories. Bycustomizing intention queries for distinct motion modalities, MTR improvesmultimodal 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 localmovement refinement, adaptively refining predicted trajectories for improvedaccuracy. Moreover, we introduce an advanced MTR++ framework, extending thecapability of MTR to simultaneously predict multimodal motion for multipleagents. MTR++ incorporates symmetric context modeling and mutually-guidedintention querying modules to facilitate future behavior interaction amongmultiple agents, resulting in scene-compliant future trajectories. Extensiveexperimental results demonstrate that the MTR framework achievesstate-of-the-art performance on the highly-competitive motion predictionbenchmarks, while the MTR++ framework surpasses its precursor, exhibitingenhanced performance and efficiency in predicting accurate multimodal futuretrajectories for multiple agents.