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[Submitted on 21 Oct 2026] Title:Integrated Longitudinal and Lateral Control for High-Speed Platooning of Articulated Vehicles View a PDF of the paper titled Integrated Longitudinal and Lateral Control for High-Speed Platooning of Articulated Vehicles, by Aashish Shaju and 2 other authors View PDF Abstract:This paper presents an integrated control framework for high-speed platooning of articulated vehicles, combining advanced longitudinal and lateral control strategies to address the challenges of safe and efficient autonomous driving. A novel proof of string stability for the proposed longitudinal control algorithm, based on a PID framework, ensures robust inter-vehicle distance regulation even under dynamic operating conditions. Additionally, an arc-length-based longitudinal error formulation enhances the applicability of the control system for curved roads, making it realistic for diverse driving scenarios. For lateral control, significant improvements to the clothoid-based path tracking algorithm are introduced to mitigate oscillations and enhance stability at high speeds. These enhancements include a tangential criterion for oscillation reduction, trajectory segment classification for adaptive lookahead distance tuning, a dual-adaptive mechanism for regulating the rate of lookahead distance adjustments based on both cross-track error (CTE) and anticipated road curvature, and extensions for articulated vehicle dynamics. The proposed integrated control framework is validated through simulations, demonstrating its effectiveness in maintaining stability, minimizing tracking errors, and ensuring robust performance in high-speed and challenging platooning scenarios. Comments: Original manuscript of an article published in Vehicle System Dynamics on September 20, 2026. Published version: this https URL Subjects: Robotics (cs.RO); Systems and Control (eess.SY) Cite as: arXiv:2610.30437 [cs.RO] (or arXiv:2610.30437v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.30437 arXiv-issued DOI via DataCite (pending registration) Journal reference: Vehicle System Dynamics (2026) Related DOI: https://doi.org/10.1080/00423114.2026.2733699 DOI(s) linking to related resources Submission history From: Aashish Shaju [view email] [v1] Wed, 21 Oct 2026 20:47:32 UTC (4,719 KB) Full-text links: Access Paper: View a PDF of the paper titled Integrated Longitudinal and Lateral Control for High-Speed Platooning of Articulated Vehicles, by Aashish Shaju and 2 other authors View PDF view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs cs.SY eess eess.SY References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)