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<p>With the rapid advancement and widespread deployment of intelligent connected vehicle technology, autonomous driving systems, and vehicle-to-infrastructure (V2I) cooperative frameworks, mixed traffic environments comprising both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) have become increasingly prevalent. However, in real-world applications, mixed vehicle platoons encounter significant challenges when negotiating curved road segments. Due to the influence of centripetal force, vehicles must simultaneously manage complex control tasks in both lateral and longitudinal dimensions during turning maneuvers. This complexity often leads to substantial errors in lateral and longitudinal displacement, velocity, acceleration, and heading angle, which severely degrade the robustness and stability of the platoon control system. To address this practical issue, this study proposes a lateral-longitudinal decoupled control model for mixed vehicle platoons in curved road scenarios under a V2I communication environment. The proposed model explicitly accounts for the effect of centripetal force on lateral vehicle dynamics and aims to resolve the control challenges posed by real traffic conditions. Through comprehensive simulation experiments, the stability, convergence properties, and robustness of the mixed platoon are rigorously analyzed in terms of lateral and longitudinal distance errors, heading angle deviations, and H<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11071_2025_11211_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(\infty \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>∞</mi> </math></EquationSource> </InlineEquation> performance metrics. The simulation results demonstrate that the proposed control strategy significantly enhances the system’s convergence rate and robustness, providing a feasible and effective solution for mixed vehicle platoon control in complex traffic scenarios.</p>

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H\(\infty \) feedback control for mixed vehicle platoons in curve scenarios under a V2I environment

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Dan Zhou,
  • Mingru Lu,
  • Guanghan Peng,
  • Tao Wang,
  • Hongzhuan Zhao,
  • Xiaoqin Zhou

摘要

With the rapid advancement and widespread deployment of intelligent connected vehicle technology, autonomous driving systems, and vehicle-to-infrastructure (V2I) cooperative frameworks, mixed traffic environments comprising both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) have become increasingly prevalent. However, in real-world applications, mixed vehicle platoons encounter significant challenges when negotiating curved road segments. Due to the influence of centripetal force, vehicles must simultaneously manage complex control tasks in both lateral and longitudinal dimensions during turning maneuvers. This complexity often leads to substantial errors in lateral and longitudinal displacement, velocity, acceleration, and heading angle, which severely degrade the robustness and stability of the platoon control system. To address this practical issue, this study proposes a lateral-longitudinal decoupled control model for mixed vehicle platoons in curved road scenarios under a V2I communication environment. The proposed model explicitly accounts for the effect of centripetal force on lateral vehicle dynamics and aims to resolve the control challenges posed by real traffic conditions. Through comprehensive simulation experiments, the stability, convergence properties, and robustness of the mixed platoon are rigorously analyzed in terms of lateral and longitudinal distance errors, heading angle deviations, and H \(\infty \) ∞ performance metrics. The simulation results demonstrate that the proposed control strategy significantly enhances the system’s convergence rate and robustness, providing a feasible and effective solution for mixed vehicle platoon control in complex traffic scenarios.