Trajectory prediction is crucial for the reliability and safety of autonomous driving systems, yet it remains a challenging task in complex interactive scenarios due to noisy trajectory observations and intricate agent interactions. Existing methods often struggle to filter redundant scene data for discriminative information extraction, directly impairing trajectory prediction accuracy especially when handling outliers and dynamic multi-agent interactions. In response to these limitations, we present a novel map-free trajectory prediction method which adaptively eliminates redundant information and selects discriminative features across the temporal, spatial, and frequency domains, thereby enabling precise trajectory prediction in real-world driving environments. First, we design a MoE based frequency domain filter to adaptively weight distinct frequency components of observed trajectory data and suppress outlier related noise; then a selective spatiotemporal attention module that reallocates weights across temporal nodes (sequential dependencies), temporal trends (evolution patterns), and spatial nodes to extract salient information is proposed. Finally, our multimodal decoder-supervised by joint patch level and point-level losses generates reasonable and temporally consistent trajectories, and comprehensive experiments on the large-scale NuScenes and Argoverse dataset demonstrate that our method achieves competitive performance and low-latency inference performance compared with recently proposed methods.

翻译：轨迹预测对于自动驾驶系统的可靠性与安全性至关重要，然而在复杂的交互场景中，由于轨迹观测噪声和智能体间复杂的交互作用，这仍然是一项具有挑战性的任务。现有方法往往难以过滤冗余的场景数据以提取判别性信息，这直接损害了轨迹预测的准确性，尤其是在处理异常值和动态多智能体交互时。针对这些局限性，我们提出了一种新颖的无地图轨迹预测方法，该方法自适应地消除冗余信息，并在时域、空域和频域中选择判别性特征，从而能够在真实驾驶环境中实现精确的轨迹预测。首先，我们设计了一个基于MoE的频域滤波器，以自适应地对观测轨迹数据的不同频率分量进行加权，并抑制与异常值相关的噪声；然后，提出了一个选择性时空注意力模块，该模块在时间节点（序列依赖）、时间趋势（演化模式）和空间节点之间重新分配权重，以提取显著信息。最后，我们的多模态解码器在联合补丁级和点级损失的监督下，生成合理且时间一致的轨迹。在大规模NuScenes和Argoverse数据集上的综合实验表明，与近期提出的方法相比，我们的方法实现了具有竞争力的性能和低延迟推理性能。