In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirements in autonomous systems. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported. Alongside this, we perform an ablation study to investigate the impact of the observed motion history on prediction performance. To evaluate the scalability of each approach when confronted with varying amounts of agents, the inference time of each model is measured. Following a quantitative analysis, the resulting predictions are compared in a qualitative manner, giving insight into the strengths and weaknesses of current approaches. The results demonstrate that although a constant velocity model (CVM) provides a good approximation of the overall dynamics in the majority of cases, additional features need to be incorporated to reflect common pedestrian behavior observed. Therefore, this study presents a data-driven analysis with the intent to guide the future development of pedestrian trajectory prediction algorithms.

翻译：本文评估了在生成单一轨迹场景下行人轨迹预测的最新进展，这一场景是与自主系统需求相一致的关键方面。评估基于广泛使用的ETH/UCY数据集，并报告了平均位移误差（ADE）和最终位移误差（FDE）。同时，我们进行了消融研究以探究观测运动历史对预测性能的影响。为评估各方法在面对不同数量智能体时的可扩展性，测量了每个模型的推理时间。在定量分析之后，对生成的预测进行定性比较，揭示了当前方法的优势与不足。结果表明，尽管恒定速度模型（CVM）在大多数情况下能较好地近似整体动态，但需要引入额外特征以反映常见的行人行为。因此，本研究通过数据驱动分析，旨在指导行人轨迹预测算法的未来发展。