Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

翻译：行人轨迹预测在确保自动驾驶车辆和交通管理系统等多种应用的安全与效率方面发挥着关键作用。本文提出了一种新颖的行人轨迹预测方法，称为多阶段目标驱动网络（MGNet）。与先前依赖逐步递归预测和单一长期目标预测的方法不同，MGNet通过预测中间阶段目标来引导轨迹生成，从而减少预测误差。该网络包含三个主要组件：条件变分自编码器（CVAE）、注意力模块和多阶段目标评估器。轨迹通过条件变分自编码器进行编码，以获取行人未来轨迹近似分布的知识，并结合注意力机制来捕捉轨迹序列之间的时间依赖性。关键模块是多阶段目标评估器，它利用编码后的特征向量来预测中间目标，有效最小化递归推理过程中的累积误差。通过在JAAD和PIE数据集上的综合实验验证了MGNet的有效性。与最先进算法的对比评估表明，我们提出的方法实现了显著的性能提升。