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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.01790 |
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| _version_ | 1866918425445531648 |
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| author | Cario, Gianni Carriuolo, Valentino Casavola, Alessandro Gagliardi, Gianfranco Lupia, Marco Torchiaro, Franco Angelo |
| author_facet | Cario, Gianni Carriuolo, Valentino Casavola, Alessandro Gagliardi, Gianfranco Lupia, Marco Torchiaro, Franco Angelo |
| contents | This article addresses obstacle avoidance motion planning for autonomous vehicles, specifically focusing on highway overtaking maneuvers. The control design challenge is handled by considering a mathematical vehicle model that captures both lateral and longitudinal dynamics. Unlike existing numerical optimization methods that suffer from significant online computational overhead, this work extends the state-of-the-art by leveraging a fast set-theoretic ellipsoidal Model Predictive Control (Fast-MPC) technique. While originally restricted to stabilization tasks, the proposed framework is successfully adapted to handle motion planning for vehicles modeled as uncertain polytopic discrete-time linear systems. The control action is computed online via a set-membership evaluation against a structured sequence of nested inner ellipsoidal approximations of the exact one-step ahead controllable set within a receding horizon framework. A six-degrees-of-freedom (6-DOF) nonlinear model characterizes the vehicle dynamics, while a polytopic embedding approximates the nonlinearities within a linear framework with parameter uncertainties. Finally, to assess performance and real-time feasibility, comparative co-simulations against a baseline Non-Linear MPC (NLMPC) were conducted. Using the high-fidelity CARLA 3D simulator, results demonstrate that the proposed approach seamlessly rejects dynamic traffic disturbances while reducing online computational time by over 90% compared to standard optimization-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01790 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Set-Theoretic Receding Horizon Control for Obstacle Avoidance and Overtaking in Autonomous Highway Driving Cario, Gianni Carriuolo, Valentino Casavola, Alessandro Gagliardi, Gianfranco Lupia, Marco Torchiaro, Franco Angelo Systems and Control This article addresses obstacle avoidance motion planning for autonomous vehicles, specifically focusing on highway overtaking maneuvers. The control design challenge is handled by considering a mathematical vehicle model that captures both lateral and longitudinal dynamics. Unlike existing numerical optimization methods that suffer from significant online computational overhead, this work extends the state-of-the-art by leveraging a fast set-theoretic ellipsoidal Model Predictive Control (Fast-MPC) technique. While originally restricted to stabilization tasks, the proposed framework is successfully adapted to handle motion planning for vehicles modeled as uncertain polytopic discrete-time linear systems. The control action is computed online via a set-membership evaluation against a structured sequence of nested inner ellipsoidal approximations of the exact one-step ahead controllable set within a receding horizon framework. A six-degrees-of-freedom (6-DOF) nonlinear model characterizes the vehicle dynamics, while a polytopic embedding approximates the nonlinearities within a linear framework with parameter uncertainties. Finally, to assess performance and real-time feasibility, comparative co-simulations against a baseline Non-Linear MPC (NLMPC) were conducted. Using the high-fidelity CARLA 3D simulator, results demonstrate that the proposed approach seamlessly rejects dynamic traffic disturbances while reducing online computational time by over 90% compared to standard optimization-based approaches. |
| title | Set-Theoretic Receding Horizon Control for Obstacle Avoidance and Overtaking in Autonomous Highway Driving |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.01790 |