SIPTraj：无高精地图的物理引导场景交互端到端轨迹预测

中文摘要 AI 辅助

自动驾驶中，周围智能体的轨迹预测是安全规划与决策的前提。若无高精地图，传感器生成的鸟瞰（BEV）特征无法提供明确的车道拓扑或可行驶区域先验，导致难以将每个智能体锚定到其周围场景上下文。此外，仅通过数据驱动学习仍难以捕捉物理可行性，因为若没有结构化监督，无法显式编码智能体运动的运动学约束。现有无地图预测器通过单次融合步骤以智能体无关方式提取场景上下文，且仅将物理约束作为输出级惩罚，未解决上述两个挑战。本文提出SIPTraj，一种无地图轨迹预测框架，共同解决场景锚定与物理可行性问题。SIPTraj引入分层智能体-场景编码器（HASE），逐步将每个智能体锚定到智能体引导的场景证据，并在场景锚定空间内优化智能体间关系。为解决预测轨迹中的物理不可行问题，本文开发了物理引导迭代解码器（PGID），其以瞬时运动学状态为条件进行解码，将物理监督传播到内部表示而非仅输出轨迹。在nuScenes和Argoverse 2 Sensor数据集上的大量实验表明，SIPTraj在推理时无需任何高精地图，即可超越现有无地图预测器和强大的有地图基线方法，代码将作为开源发布。

英文摘要

Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD) maps, sensor-derived bird's-eye-view (BEV) features provide no explicit lane topology or drivable-area priors, making it inherently difficult to ground each agent in its surrounding scene context. Moreover, physical feasibility remains difficult to capture through data-driven learning alone, as kinematic constraints on agent motion cannot be explicitly encoded without structured supervision. Existing map-free predictors extract scene context in an agent-agnostic manner through a single fusion step and treat physical constraints only as output-level penalties, leaving both challenges unaddressed. We propose SIPTraj, a map-free trajectory prediction framework that jointly addresses scene grounding and physical feasibility. SIPTraj introduces a Hierarchical Agent-Scene Encoder (HASE) progressively grounding each agent in agent-guided scene evidence and refining inter-agent relations within the scene-grounded space. To tackle physical infeasibility in predicted trajectories, we develop a Physics-Guided Iterative Decoder (PGID). It conditions decoding on instantaneous kinematic states, propagating physical supervision into internal representations rather than output trajectories alone. Extensive experiments on nuScenes and Argoverse 2 Sensor show that SIPTraj surpasses prior map-free predictors and strong map-based baselines without any HD map at inference. Our code will be released as open-source.

发表机构

• School of Data Science and Engineering, Xingzhi College, South China Normal University（华南师范大学行知学院数据科学与工程学院）
• Hong Kong University of Science and Technology（香港科技大学）
• University of Macau（澳门大学）
• College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）
• School of Electronic Science and Engineering, South China Normal University（华南师范大学电子科学与工程学院）

机构由 AI 辅助整理，请以论文原文为准。