Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach

TL;DR

This document describes two LaTeX class files, cas-sc.cls and cas-dc.cls, designed for Elsevier's Complex Article Service workflow. It covers front matter customization (title and author configuration, title/author marks, and abstract/keywords) and main-matter formatting (tables, figures, and theorem environments), including support for long front matter via the longmktitle option. The material provides usage guidance and concrete examples to ensure consistent formatting and metadata handling for CAS submissions. Overall, it facilitates robust, publication-ready manuscript preparation within the CAS ecosystem, enhancing layout control and metadata fidelity for journal articles.

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.

Figures (3)

• Figure 1: Double column output (classfile: cas-dc.cls).
• Figure 2: The evanescent light - $1S$ quadrupole coupling ($g_{1,l}$) scaled to the bulk exciton-photon coupling ($g_{1,2}$). The size parameter $kr_{0}$ is denoted as $x$ and the is placed directly on the cuprous oxide sample ($\delta r=0$, See also Fig. \ref{['FIG:2']}).
• Figure 3: Schematic of formation of the evanescent polariton on linear chain of . The actual dispersion is determined by the ratio of two coupling parameters such as exciton- coupling and - coupling between the microspheres.