Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control

Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control Zhenbin Chen, Jiaqin Lai, Peixin Li, Omar I. Awad, Yubing Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3850749/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prediction horizon is a key parameter in model predictive control (MPC), related to the effectiveness and stability of model predictive control. In vehicle control, the selection of prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the Proximal Policy Optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. In this paper, we constructed the vehicle dynamics model and designed a basic model prediction control. We have established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared the MPC of the fixed Prediction Horizon. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong trajectory tracking capability. Physical sciences/Engineering Physical sciences/Engineering/Electrical and electronic engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3850749","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267593820,"identity":"8988ebf8-448f-4813-b8fb-2aae9ddf86b4","order_by":0,"name":"Zhenbin Chen","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Zhenbin","middleName":"","lastName":"Chen","suffix":""},{"id":267593821,"identity":"ce2d4b9e-faaf-41f2-a799-322a490d5664","order_by":1,"name":"Jiaqin Lai","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqin","middleName":"","lastName":"Lai","suffix":""},{"id":267593822,"identity":"8c653c29-caea-4518-a993-712fae88a184","order_by":2,"name":"Peixin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACfvb2gw8SKmwSGJhhQgcIaJHsOZNs8OFMGglaDG4kmEnObDucgBAiQkuaNM+Z83n87bwHPxe2Mcjx3Uhg/FyAz2FnHh625qm4XSxxmC9ZemYbg7HkjQRm6Rl4tPAdT0i8zXPmdmLDYR4Dad42hsQNNxLYmHnwuexAAkjlucT5h3mMfwO11BPUInAiwQjo/QOJGw7zmIFsSTAgpAUayMmJG4FarHnOSRjOPPOwWRqfFmhU2iXOO3/G+DZPmY083/Hkg5/x+gUNSAAxYwMJGkbBKBgFo2AUYAMASMtRv8MGDVYAAAAASUVORK5CYII=","orcid":"","institution":"Hainan University","correspondingAuthor":true,"prefix":"","firstName":"Peixin","middleName":"","lastName":"Li","suffix":""},{"id":267593823,"identity":"a2fa6473-8bc5-486d-b3d4-8277207bf0bb","order_by":3,"name":"Omar I. 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In vehicle control, the selection of prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the Proximal Policy Optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. In this paper, we constructed the vehicle dynamics model and designed a basic model prediction control. We have established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared the MPC of the fixed Prediction Horizon. 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