Chen Wen-Hui, Wu Jiann-Cherng, Davydov Yury, Yeh Wei-Chen, Lin Yu-Chen
Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan.
Sensors (Basel). 2024 Aug 5;24(15):5066. doi: 10.3390/s24155066.
Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.
行人轨迹预测对于自动驾驶系统中碰撞避免算法的开发至关重要，其目的是根据检测到的行人过去的轨迹来预测他们未来的运动。传统的行人轨迹预测方法涉及一系列任务，包括检测和跟踪，以收集观察到的行人的历史运动。因此，轨迹预测的准确性严重依赖于检测和跟踪模型的准确性，使其容易受到其性能的影响。轨迹预测的先前研究主要使用公共数据集评估模型性能，而这些数据集往往忽略了源自检测和跟踪模型的误差。这种疏忽未能捕捉到不可避免的检测和跟踪不准确的现实场景。在本研究中，我们调查了集成检测、跟踪和轨迹预测管道中误差的累积效应。通过实证分析，我们检查了管道每个阶段引入的误差，并评估了它们对轨迹预测准确性的综合影响。我们在台湾收集的各种自定义数据集上评估这些模型，以提供全面的评估。我们对这些集成管道得出的结果的分析揭示了检测和跟踪误差对下游任务（如轨迹预测和距离估计）的重大影响。
