MliG: Scene-Level Multimodal Motion Prediction Based on Multi-Layer Interaction Graph

The representation form of multimodal motion prediction results is critical to the efficiency of the prediction-planning workflow. However, most existing methods generate multiple sets of trajectories for each independent agent to cover diverse modalities, overlooking interactions among agents during the prediction period, making it challenging to achieve scene-level consistency in multi-agent predictions. This paper introduces MliG, a scene-level multimodal conditional motion prediction framework based on Multi-Layer Interaction Graph. The interaction graph designed explicitly represent the interaction behaviors of multi-agents over future periods, guiding conditional trajectory prediction to achieve planning-friendly prediction result representation. First, an Interaction-Mask-based determination strategy is introduced to obtain ground truth interaction labels, enabling data-driven implicit relationship learning, and the generated training labels can support the accurate construction of the interaction graph. Second, scene simplification and modality decoupling using the interaction graph make it easier to fuse information and represent driving scenarios efficiently. Based on the multi-layer design, the complex interactions between agent pairs are decomposed into different group and modality layers, ensuring the diversity of interactions while improving the efficiency of conditional predictions. Extensive experiments on the Argoverse 2, Argoverse 1, and INTERACTION Datasets demonstrate that the proposed MliG achieves high prediction accuracy and significantly improves the scene-level consistency of trajectory prediction modalities for multi-agents in the same driving scenario.