Korbmacher Raphael, Tordeux Antoine
Department for Traffic Safety and Reliability, University of Wuppertal, 42119 Wuppertal, Germany.
Sensors (Basel). 2024 Apr 8;24(7):2356. doi: 10.3390/s24072356.
Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2-2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments, but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.
由于行人行为的复杂相互作用，预测人类轨迹面临重大挑战，行人行为受环境布局和人际动态影响。场景密度的变化进一步加剧了这种复杂性。为解决这一问题，我们引入了一个来自2022年里昂灯光节的新型数据集，其特点是具有广泛的密度范围（0.2-2.2人/平方米）。我们的分析表明，基于密度的数据分类可以显著提高预测算法的准确性。我们提出了一种创新的两阶段处理方法，在性能上超越了当前的先进方法。此外，我们使用基于碰撞的误差度量来更好地考虑轨迹预测中的碰撞。我们的研究结果表明，这种误差度量的有效性取决于密度，提供了预测见解。这项研究不仅推进了我们对密集环境中人类轨迹预测的理解，还提出了一个将密度考虑因素纳入预测建模的方法框架，从而提高算法性能和避免碰撞。
