A safety-prioritized receding horizon control framework for platoon formation in a mixed traffic environment
Platoon formation with connected and automated vehicles (CAVs) in a mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with unknown dynamics and control actions. In this paper, we develop a safety-prioritized receding horizon control framework...
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Veröffentlicht in: | Automatica (Oxford) 2023-09, Vol.155, p.111115, Article 111115 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Platoon formation with connected and automated vehicles (CAVs) in a mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with unknown dynamics and control actions. In this paper, we develop a safety-prioritized receding horizon control framework for creating platoons of HDVs preceded by a CAV Our framework ensures indirect control of the following HDVs by directly controlling the leading CAV given the safety constraints. The framework utilizes a data-driven prediction model that is based on the recursive least squares algorithm and the constant time headway relative velocity car-following model to predict future trajectories of human-driven vehicles. To demonstrate the efficacy of the proposed framework, we conduct numerical simulations and provide the associated scalability, robustness, and performance analyses. |
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ISSN: | 0005-1098 1873-2836 |
DOI: | 10.1016/j.automatica.2023.111115 |