A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models (2504.14241v1)

Abstract: 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 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.

• The paper proposes the Knowledge-Informed Deep Learning (KIDL) paradigm, which leverages LLMs to improve generalization and stability in car-following models.
• KIDL uses knowledge distillation from pre-trained LLMs to transfer high-level car-following insights into lightweight neural networks, enhancing generalization.
• The framework explicitly incorporates stability constraints into training and demonstrates superior generalization and verified traffic flow stability compared to other models.

A Knowledge-Informed Deep Learning Paradigm for Car-Following Models

The paper "A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models" proposes an innovative approach to car-following models, which are a fundamental component of microscopic traffic simulation and autonomous driving systems. Traditional car-following models often face limitations in their ability to generalize across diverse driving scenarios due to their dependence on specific datasets and inherent challenges in ensuring traffic flow stability. To address these issues, the authors introduce the Knowledge-Informed Deep Learning (KIDL) paradigm, which leverages LLMs to enhance both generalization capabilities and stability optimization.

The KIDL framework distills car-following knowledge from pre-trained LLMs into a lightweight neural architecture, significantly improving the generalization capability of car-following models. This is achieved through a carefully designed knowledge distillation process wherein LLMs serve as teacher models that provide high-level car-following insights, which are then transferred to the student models, represented by deep neural networks. By harnessing the intrinsic ability of LLMs to encode comprehensive knowledge beyond the scope of specific datasets, KIDL overcomes the generalization problem that traditional car-following models often face.

Another critical aspect of KIDL is its explicit incorporation of stability constraints into the model training process. The paper highlights the importance of local and string stability for safe and efficient autonomous vehicle operation. While traditional car-following models may excel in replicating human-like driving behavior, they often overlook the system-level optimization objectives necessary for maintaining stability under varying traffic conditions. KIDL addresses this gap by embedding theoretically grounded stability constraints into its loss function during model training. This ensures that the resulted model not only mimics human driving but also complies with stability requirements.

The experimental evaluation conducted in the paper demonstrates KIDL's superior performance in comparison to representative physics-based, data-driven, and hybrid car-following models. The empirical results show that KIDL significantly improves behavioral generalization when evaluated on real-world datasets such as NGSIM and HighD. Furthermore, theoretical analysis confirms that KIDL achieves enhanced traffic flow stability, with local and string stability metrics verified across diverse equilibrium states.

With its ability to generate human-like driving behaviors while maintaining inherent traffic flow stability, KIDL presents a robust and scalable solution for next-generation traffic systems. The implications of this research are particularly pronounced in the context of autonomous driving, where scalable models ensuring generalization and stability are crucial for safe deployment.

Looking ahead, this paper opens several avenues for future research in AI and transportation systems:

1. Enhanced Model Complexity: Future work may explore more sophisticated neural network architectures that can further improve generalization without compromising efficiency.
2. Incorporation of Behavioral Diversity: While KIDL captures general car-following principles, integrating individual driver behavior variations could yield models with finer accuracy.
3. Broader Application: The principles established in KIDL can potentially extend to other domains within transportation systems, such as lane-change modeling and intersection management.

In conclusion, the KIDL paradigm represents a significant advancement in car-following model development, moving beyond data-centric methodologies to knowledge-driven models that meet the practical and theoretical needs of modern traffic systems. As AI capabilities continue to evolve, frameworks like KIDL will no doubt play a pivotal role in the ongoing development of intelligent transportation networks.