Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimodal handling. We propose Perceiver with Register queries (PerReg+), a novel trajectory prediction framework that introduces: (1) Dual-Level Representation Learning via Self-Distillation (SD) and Masked Reconstruction (MR), capturing global context and fine-grained details. Additionally, our approach of reconstructing segmentlevel trajectories and lane segments from masked inputs with query drop, enables effective use of contextual information and improves generalization; (2) Enhanced Multimodality using register-based queries and pretraining, eliminating the need for clustering and suppression; and (3) Adaptive Prompt Tuning during fine-tuning, freezing the main architecture and optimizing a small number of prompts for efficient adaptation. PerReg+ sets a new state-of-the-art performance on nuScenes [1], Argoverse 2 [2], and Waymo Open Motion Dataset (WOMD) [3]. Remarkable, our pretrained model reduces the error by 6.8% on smaller datasets, and multi-dataset training enhances generalization. In cross-domain tests, PerReg+ reduces B-FDE by 11.8% compared to its non-pretrained variant.

本研究针对现有车辆轨迹预测模型泛化能力不足和预测不确定性的问题，提出了一种新颖的轨迹预测框架PerReg+，通过自我蒸馏和掩码重建实现双层表示学习，有效捕获全局上下文和细节信息。此外，研究引入基于注册的查询和自适应提示调优，显著提升了多模态处理能力和模型适应性。PerReg+在多个数据集上设立了新的性能基准，并在小型数据集上减少了6.8%的误差，展现出优越的泛化能力。 

基于双层表示学习和自适应提示的可泛化轨迹预测