Trajectory prediction is fundamental in computer vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range, physical obstructions, and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns, as they can result in missing essential, non-visible objects. To bridge this gap, we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj), setting a new benchmark for future research. The code is available at \url{https://github.com/Hai-chao-Zhang/OOSTraj}.

翻译：轨迹预测是计算机视觉和自动驾驶领域的基础任务，尤其对理解行人行为及实现主动决策至关重要。现有方法通常假设观测数据精确完整，却忽视了因摄像头视野限制、物理遮挡导致的视外物体检测难题，以及传感器数据固有噪声（缺乏去噪真值）带来的挑战。这些疏忽可能遗漏关键非可视物体，构成严重安全隐患。为填补这一空白，我们提出一种基于视觉定位技术的视外轨迹预测方法。该方法以无监督方式对含噪传感器观测数据进行去噪，并将传感器采集的视外物体轨迹精确映射为视觉轨迹。在Vi-Fi和JRDB数据集上，该方法在视外含噪传感器轨迹去噪与预测任务中均达到当前最优性能。通过提升轨迹预测精度并解决视外物体带来的挑战，本研究显著增强了复杂环境下自动驾驶的安全性与可靠性。此项工作首次提出视外轨迹预测（OOSTraj）框架，为后续研究确立了新基准。代码开源地址：\url{https://github.com/Hai-chao-Zhang/OOSTraj}。