用深度柯尔莫哥洛夫模型提升自动驾驶车在混合交通中的控流稳定性。
Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control
- 通过自适应学习人类驾驶行为，将非线性动态转为高维空间的线性系统。
- 在HighD数据集上预测精度优于基线模型，低渗透率下仍有效抑制震荡。
- 计算成本低，适合真实混合交通场景，开源代码可复现。
在自动驾驶车辆（CAVs）与人工驾驶车辆（HDVs）混合交通流中缓解交通震荡对提升交通稳定性至关重要。核心挑战在于如何在计算可处理的预测控制框架内建模HDVs复杂的非线性异质行为。本文提出一种自适应深度柯尔莫哥洛夫预测控制框架（AdapKoopPC），其核心为新型深度柯尔莫哥洛夫网络（AdapKoopnet），该网络通过自适应学习自然驾驶数据，将复杂的HDV跟驰动态表示为高维空间中的线性系统。该学习得到的线性表示被嵌入模型预测控制（MPC）框架，实现对CAVs的实时、可扩展且最优控制。基于HighD数据集和大量数值模拟验证表明，AdapKoopnet在轨迹预测精度上显著优于基线模型；完整控制器能有效抑制交通震荡，且计算开销更低，即使在低比例CAVs场景下也表现优异。所提框架为真实混合交通环境下的稳定性提升提供了一种可扩展的数据驱动解决方案。代码已公开。
原文摘要 · Abstract (English)
Mitigating traffic oscillations in mixed flows of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is critical for enhancing traffic stability. A key challenge lies in modeling the nonlinear, heterogeneous behaviors of HDVs within computationally tractable predictive control frameworks. This study proposes an adaptive deep Koopman predictive control framework (AdapKoopPC) to address this issue. The framework features a novel deep Koopman network, AdapKoopnet, which represents complex HDV car-following dynamics as a linear system in a high-dimensional space by adaptively learning from naturalistic data. This learned linear representation is then embedded into a Model Predictive Control (MPC) scheme, enabling real-time, scalable, and optimal control of CAVs. We validate our framework using the HighD dataset and extensive numerical simulations. Results demonstrate that AdapKoopnet achieves superior trajectory prediction accuracy over baseline models. Furthermore, the complete AdapKoopPC controller significantly dampens traffic oscillations with lower computational cost, exhibiting strong performance even at low CAV penetration rates. The proposed framework offers a scalable and data-driven solution for enhancing stability in realistic mixed traffic environments. The code is made publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。