This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they often revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchmark based on Argoverse 2, specifically targeting pedestrians in traffic environments. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art, reducing the Average Displacement Error (ADE) by 8.8% while utilizing significantly less information. Despite its agent-centric encoding scheme, Snapshot demonstrates scalability, real-time performance, and robustness to varying motion histories. Moreover, by integrating Snapshot into a modular autonomous driving software stack, we showcase its real-world applicability.

翻译：本文探讨了城市交通中的行人轨迹预测，同时关注模型精度与实际应用性。尽管现有方法前景广阔，但它们通常基于缺乏交通信息的行人数据集，或采用无法实时运行或鲁棒性不足的架构。为应对这些局限，我们首先基于Argoverse 2数据集构建了专门针对交通环境行人的基准测试。随后提出快照模型——一种模块化的前馈神经网络，该模型在显著减少信息使用量的同时，将平均位移误差降低8.8%，性能超越当前最优方法。尽管采用以智能体为中心的编码方案，快照模型仍展现出可扩展性、实时处理能力以及对不同运动历史的鲁棒性。此外，通过将快照模型集成至模块化自动驾驶软件栈，我们验证了其实际应用价值。