In this paper, the state of the art in the field of pedestrian trajectory prediction is evaluated alongside the constant velocity model (CVM) with respect to its applicability in autonomous vehicles. 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. To align with requirements in real-world applications, modifications are made to the input features of the initially proposed models. An ablation study is conducted to examine the influence of the observed motion history on the prediction performance, thereby establishing a better understanding of its impact. Additionally, the inference time of each model is measured to evaluate the scalability of each model when confronted with varying amounts of agents. The results demonstrate that simple models remain competitive when generating single trajectories, and certain features commonly thought of as useful have little impact on the overall performance across different architectures. Based on these findings, recommendations are proposed to guide the future development of trajectory prediction algorithms.

翻译：本文针对行人轨迹预测领域的最新技术与恒速模型（CVM），在自动驾驶车辆适用性方面进行了评估。评估基于广泛使用的ETH/UCY数据集，报告了平均位移误差（ADE）和最终位移误差（FDE）。为契合实际应用需求，对初始提出模型的输入特征进行了修正。通过消融实验，研究了观测运动历史对预测性能的影响，从而更深入地理解其作用机制。此外，测量了各模型的推理时间，以评估其在面对不同数量智能体时的可扩展性。结果表明，简单模型在生成单一轨迹时仍具有竞争力，且某些通常被认为有用的特征对不同架构的整体性能影响甚微。基于这些发现，提出了指导未来轨迹预测算法发展的建议。