提出可实时运行的行人轨迹预测模型Snapshot，兼顾精度与实际部署能力。
Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments
- 采用模块化前馈网络架构，仅依赖必要信息实现高效预测。
- 在Argoverse 2数据集上使平均位移误差降低8.8%，优于当前最优方法。
- 适合集成到自动驾驶系统中，具备真实场景部署潜力。
本文研究城市交通环境中行人的轨迹预测，同时关注模型精度与实际可用性。现有方法常基于不包含交通信息的行人数据集，或采用非实时、不鲁棒的模型架构。为此，我们首先构建了基于Argoverse 2的专用基准，专门针对交通环境中的行人。随后提出Snapshot——一种模块化、前馈神经网络，在显著减少输入信息量的同时，将平均位移误差（ADE）降低8.8%，超越当前最优水平。尽管采用以智能体为中心的编码方式，Snapshot仍具备可扩展性、实时性能及对不同运动历史的鲁棒性。通过将其集成至模块化自动驾驶软件栈，进一步验证了其在真实场景中的应用价值。
原文摘要 · Abstract (English)
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.
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