Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles

[2607.04259] Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2607.04259 (cs) [Submitted on 5 Jul 2026] Title: Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles Authors: Duc-Tien Bui , Ngoc Thinh Nguyen , Hung Duy Nguyen , Dong Bi , Tomislav Mihalj , Arno Eichberger View PDF HTML (experimental) Abstract: Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring computational efficiency remains a significant challenge. This paper presents a sequential path planning framework in which a rough path obtained from graph search is explicitly exploited to guide a Model Predictive Control (MPC)-based path refinement. A rough path is first obtained via Dijkstra search on a discretized grid and is then used to construct a spatially varying convex lateral safety corridor that explicitly captures obstacle avoidance constraints, transforming discrete obstacle avoidance decisions into continuous feasibility constraints for optimization. Within this corridor, an MPC problem is formulated to refine the path, enabling efficient optimization while maintaining path smoothness by penalizing the third-order spatial derivative of the lateral offset over a prediction horizon. The proposed algorithm is evaluated in multiple overtaking scenarios on both straight and curved roads, including cases with single and multiple target vehicles, using high-fidelity environment simulations (i.e., CarMaker). Compared with the previous study, which used polynomial fitting and a quadratic programming method, the proposed approach consistently achieves lower lateral acceleration, curvature, and jerk while reducing computational cost by 28.08% on straight roads and 29.52% on curved roads. These results demonstrate that exploiting graph-search structure within an MPC formulation provides an effective balance between path smoothness and computational efficiency for autonomous vehicles in structured driving environments. Comments: 12th 2026 International Conference on Control, Decision and Information Technologies (CoDIT 2026) Subjects: Robotics (cs.RO) Cite as: arXiv:2607.04259 [cs.RO] (or arXiv:2607.04259v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2607.04259 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 12th 2026 International Conference on Control, Decision and Information Technologies (CoDIT 2026), Bari, Italy, 2026, pp. 1757-1762 Related DOI : https://doi.org/10.1109/CoDIT70676.2026.11630833 Focus to learn more DOI(s) linking to related resources Submission history From: Duc-Tien Bui [ view email ] [v1] Sun, 5 Jul 2026 12:14:33 UTC (1,757 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark 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. 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