融合物理规律与数据的深度学习模型，精准预测车队动态并保证可解释性。
Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach
- 用物理编码图约束车队响应前车行为，确保局部稳定。
- 从轨迹数据中学习多尺度跟驰模式，还原真实物理参数。
- 适用于智能交通系统建模，适合关注可解释性的研究者。
近年来，基于人工智能的非线性车队动力学建模在预测和优化车辆交互中起关键作用。现有方法缺乏对车队尺度下车辆行为交互特征的提取与捕捉，且难以在保持高精度的同时兼顾物理可分析性。为此，本文提出一种新型物理编码深度学习网络PeMTFLN，用于建模非线性车队动力学。具体地，设计了可分析参数编码计算图（APeCG），引导车队响应前车驾驶行为，同时保证局部稳定性；构建多尺度轨迹特征学习网络（MTFLN），从轨迹数据中捕捉车队跟驰模式，并推断APeCG所需的物理参数。采用人工驾驶车辆轨迹数据集HIGHSIM训练所提PeMTFLN。轨迹预测实验表明，其在速度与间距预测上优于基线模型。稳定性分析显示，APeCG中的物理参数可复现真实场景下的车队稳定性。仿真实验中，PeMTFLN生成的车队轨迹推理误差低，且准确再现真实安全统计数据。代码已开源。
原文摘要 · Abstract (English)
Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Existing efforts lack the extraction and capture of vehicle behavior interaction features at the platoon scale. More importantly, maintaining high modeling accuracy without losing physical analyzability remains to be solved. To this end, this paper proposes a novel physics-encoded deep learning network, named PeMTFLN, to model the nonlinear vehicle platoon dynamics. Specifically, an analyzable parameters encoded computational graph (APeCG) is designed to guide the platoon to respond to the driving behavior of the lead vehicle while ensuring local stability. Besides, a multi-scale trajectory feature learning network (MTFLN) is constructed to capture platoon following patterns and infer the physical parameters required for APeCG from trajectory data. The human-driven vehicle trajectory datasets (HIGHSIM) were used to train the proposed PeMTFLN. The trajectories prediction experiments show that PeMTFLN exhibits superior compared to the baseline models in terms of predictive accuracy in speed and gap. The stability analysis result shows that the physical parameters in APeCG is able to reproduce the platoon stability in real-world condition. In simulation experiments, PeMTFLN performs low inference error in platoon trajectories generation. Moreover, PeMTFLN also accurately reproduces ground-truth safety statistics. The code of proposed PeMTFLN is open source.
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