Analyzable Parameters Dominated Vehicle Platoon Dynamics Modeling and Analysis: A Physics-Encoded Deep Learning Approach

2025-02-09

AI Summary

Addressing the challenge of simultaneously achieving high modeling accuracy, physical interpretability, and stability analyzability in nonlinear vehicle platoon dynamics, this paper proposes PeMTFLN—a physically interpretable deep learning framework. PeMTFLN integrates an Analyzable Parameter-encoding Computational Graph (APeCG) with a Multi-scale Trajectory Feature Learning Network (MTFLN), enabling, for the first time, explicit mapping between neural network outputs and classical control-theoretic stability criteria—both in frequency and time domains. Moreover, it supports end-to-end inversion of physically interpretable parameters directly from trajectory data. By embedding vehicle dynamics constraints and employing differentiable physics-informed encoding, PeMTFLN achieves state-of-the-art performance on the HIGHSIM dataset: reducing speed and spacing prediction errors, decreasing simulated trajectory error by 32.7%, and accurately reproducing critical safety metrics—including marginal stability, collision risk, and emergency braking rate.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Modeling nonlinear vehicle platoon dynamics
• Ensuring high accuracy and physical analyzability
• Capturing platoon-scale vehicle behavior interactions

Innovation

• Physics-encoded deep learning
• Analyzable parameters encoded graph
• Multi-scale trajectory feature learning