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}.

通过利用视觉定位技术，我们提出了一种新的方法来预测失去视线的轨迹，该方法能够在无监督的情况下去噪传感器观测，并将基于传感器的轨迹精确映射到视觉轨迹。该方法在失去视线的噪声传感器轨迹去噪和预测方面展示了最先进的性能，在Vi-Fi和JRDB数据集上取得了重要的成果。通过提高轨迹预测的准确性并解决失去视线物体的挑战，我们的工作显著有助于提高复杂环境下自动驾驶的安全性和可靠性。我们的工作代表着失去视线轨迹预测（OOSTraj）的第一个尝试，并为未来的研究设定了新的基准。代码可在https://github.com/Hai-chao-Zhang/OOSTraj找到。

OOSTraj: 视觉定位去噪下的隐形轨迹预测