从以自我为中心（egocentric）视角准确预测人类运动轨迹，在人形机器人、可穿戴感知系统及辅助导航等应用中具有核心作用。然而，由于缺乏在真实世界环境中采集的以自我为中心轨迹数据集，该方向的研究进展仍十分有限。为填补这一空白，我们提出 EgoTraj——一个使用 Meta Quest Pro（MQPro）设备录制的以自我为中心、多模态、开放数据集。EgoTraj 包含 75 段人类导航序列，由多名 MQPro 佩戴者在真实城市环境中采集。每段录制数据均提供同步的 RGB 视频及真值标注，包括连续时间同步的六自由度头部姿态、逐帧三维眼动向量以及场景标注。据我们所知，EgoTraj 区别于典型以自我为中心轨迹数据集之处在于：其覆盖长时程、自主导向的城市导航任务，涵盖多样化的城市路线与广泛参与者的个体差异。为验证该数据集的潜力，我们对多种当前最先进的以自我为中心轨迹预测方法进行了基准测试，并开展消融实验，分析眼动、场景与运动线索各自的贡献。结果表明，EgoTraj 对增强现实（AR）驱动的感知、导航及辅助系统具有重要价值。EgoTraj 数据集、代码及 EgoViz 可视化仪表盘已公开发布于 https://github.com/yehiahmad/EgoTraj。

Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensing systems, and assistive navigation. However, progress in this direction remains limited due to the scarcity of egocentric trajectory datasets collected in real-world environments. Addressing this need, we introduce EgoTraj, an egocentric multimodal open dataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75 sequences of human navigation collected from multiple MQPro wearers in real-world urban environments. Each recording provides synchronized RGB video along with ground-truth data, including continuous time-synchronized 6-degree-of-freedom head poses, per-frame 3D eye gaze vectors, scene annotations. To the best of our knowledge, EgoTraj differs from typical egocentric trajectory datasets by capturing long-horizon, self-directed navigation across diverse urban routes with broad participant diversity. To demonstrate the potential of the dataset, we benchmark several state-of-the-art methods for egocentric trajectory prediction and conduct ablation studies to analyze the contributions of gaze, scene, and motion cues. The results highlight the utility of EgoTraj for AR-based perception, navigation, and assistive systems. The EgoTraj dataset, code, and EgoViz Dashboard are publicly available at https://github.com/yehiahmad/EgoTraj.