标题： 行人轨迹预测中的缺失数据：数据集、插补和基准测试 显示英文标题

标题： Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking

摘要： 行人轨迹预测对于机器人和自动驾驶车辆等应用至关重要。 在过去十年中，由于行人轨迹数据集的可用性，取得了显著进展，这些数据集使轨迹预测方法能够从行人的过去运动中学习并预测未来的轨迹。 然而，这些数据集和方法通常假设观察到的轨迹序列是完整的，忽略了现实世界中的问题，如传感器故障、遮挡和有限的视野，这些可能导致观察到的轨迹中出现缺失值。 为了解决这一挑战，我们提出了TrajImpute，这是一个模拟观察到的轨迹中缺失坐标的行人轨迹预测数据集，以增强现实适用性。 TrajImpute在观察到的轨迹中保持缺失数据的均匀分布。 在本工作中，我们全面检查了几种插补方法来重建缺失的坐标，并对它们进行基准测试以插补行人轨迹。 此外，我们提供了对最近轨迹预测方法的深入分析，并评估这些模型在插补轨迹上的性能。 我们对插补和轨迹预测方法的实验评估提供了几个有价值的见解。 我们的数据集为未来针对插补感知的行人轨迹预测研究提供了一个基础资源，可能加速这些方法在现实应用中的部署。 数据集和代码文件的公开访问链接可在 https://github.com/Pranav-chib/TrajImpute 获取。 显示英文摘要

摘要： Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://github.com/Pranav-chib/TrajImpute.