SSP-DMGTimeNet：车辆队列时空轨迹预测的物理约束学习

中文摘要 AI 辅助

现有的跟驰预测方法主要优化轨迹精度，而很少考虑预测的扰动是否沿车辆队列真实传播。这一局限可能导致预测准确但队列不稳定。我们提出SSP-DMGTimeNet，一种用于车辆队列时空轨迹预测的物理约束学习框架。该模型结合多尺度时间表示与跨车交互特征，以捕捉复杂且时变的队列动态。一种传播延迟感知的因果注意力机制通过学习相邻车辆之间的响应延迟并沿队列累积，显式建模从上游到下游的扰动传播。此外，时域和频域的队列稳定性损失在训练过程中缓解了相邻车辆及任意子队列间的扰动放大。在HighD上的实验表明，SSP-DMGTimeNet对五车队列实现了0.65%的不稳定窗口率，在真实激励子集上的最大头尾放大为0.898，同时保持了有竞争力的轨迹预测性能。在NGSIM US-101和I-80上的零样本评估中，模型实现了1.316米/秒和1.252米/秒的速度平均绝对误差，不稳定窗口率分别为3.90%和4.10%。这些结果表明，引入队列级物理约束能有效平衡轨迹预测精度与扰动传播稳定性。

英文摘要

Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle platoon. This limitation may lead to accurate but string-unstable predictions. We propose SSP-DMGTimeNet, a physics-constrained learning framework for spatiotemporal trajectory prediction of vehicle platoons. The model combines multi-scale temporal representations with cross-vehicle interaction features to capture complex and time-varying platoon dynamics. A propagation-delay-aware causal attention mechanism explicitly models upstream-to-downstream disturbance propagation by learning response delays between adjacent vehicles and accumulating them along the platoon. In addition, time- and frequency-domain string-stability losses relieve disturbance amplification across both adjacent vehicles and arbitrary sub-platoons during training. Experiments on HighD show that SSP-DMGTimeNet achieves an unstable-window rate of 0.65\% for five-vehicle platoons and a maximum head-to-tail amplification of 0.898 on the ground-truth excitation subset, while maintaining competitive trajectory prediction performance. In zero-shot evaluation on NGSIM US-101 and I-80, the model achieves velocity MAEs of 1.316~m/s and 1.252~m/s, with unstable-window rates of 3.90\% and 4.10\%, respectively. These results demonstrate that incorporating platoon-level physical constraints can effectively balance trajectory prediction accuracy and disturbance propagation stability.

发表机构

• Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院）
• Chinese Academy of Sciences（中国科学院）
• Nanyang Technological University（南洋理工大学）
• National University of Singapore（新加坡国立大学）

机构由 AI 辅助整理，请以论文原文为准。