Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific datasets limits generalization across diverse scenarios and reduces reliability in real-world deployment. Moreover, these models typically focus on behavioral fidelity and do not support the explicit optimization of local and string stability, which are increasingly important for the safe and efficient operation of autonomous vehicles (AVs). To address these limitations, we propose a Knowledge-Informed Deep Learning (KIDL) paradigm that distills the generalization capabilities of pre-trained Large Language Models (LLMs) into a lightweight and stability-aware neural architecture. LLMs are used to extract fundamental car-following knowledge beyond dataset-specific patterns, and this knowledge is transferred to a reliable, tractable, and computationally efficient model through knowledge distillation. KIDL also incorporates stability constraints directly into its training objective, ensuring that the resulting model not only emulates human-like behavior but also satisfies the local and string stability requirements essential for real-world AV deployment. We evaluate KIDL on the real-world NGSIM and HighD datasets, comparing its performance with representative physics-based, data-driven, and hybrid CFMs. Both empirical and theoretical results consistently demonstrate KIDL's superior behavioral generalization and traffic flow stability, offering a robust and scalable solution for next-generation traffic systems.

翻译：跟驰模型是交通流分析与自动驾驶的基础。尽管经过校准的基于物理学的模型和经过训练的数据驱动模型能够复现人类驾驶行为，但它们对特定数据集的依赖限制了在不同场景下的泛化能力，并降低了实际部署的可靠性。此外，这些模型通常侧重于行为保真度，不支持对局部稳定性和串稳定性进行显式优化，而这两种稳定性对于自动驾驶车辆的安全高效运行日益重要。为解决这些局限性，我们提出了一种知识引导的深度学习范式，该范式将预训练大型语言模型的泛化能力提炼到一个轻量级且具有稳定性感知的神经架构中。我们利用大型语言模型提取超越数据集特定模式的基本跟驰知识，并通过知识蒸馏将这些知识迁移到一个可靠、可处理且计算高效的模型中。该范式还将稳定性约束直接纳入其训练目标，确保所得模型不仅能模拟类人行为，还能满足实际自动驾驶车辆部署所必需的局部与串稳定性要求。我们在真实世界的NGSIM和HighD数据集上评估了该范式，并将其性能与代表性的基于物理学、数据驱动及混合跟驰模型进行了比较。实证与理论结果均一致表明，该范式在行为泛化与交通流稳定性方面具有优越性，为下一代交通系统提供了一个鲁棒且可扩展的解决方案。