[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82798-en":3,"doc-seo-82798-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82798,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles","Path planning is a core capability of autonomous vehicles, requiring safe, comfortable, and dynamically feasible motion while staying computationally efficient. The work introduces a sequential framework that uses a graph-search rough path to guide Model Predictive Control (MPC) refinement. Dijkstra search on a discretized grid generates a spatially varying convex lateral safety corridor, turning discrete obstacle decisions into continuous optimization constraints. MPC then refines the trajectory by penalizing the third-order derivative of lateral offset over the prediction horizon, evaluated in overtaking scenarios with CarMaker simulations and improved smoothness metrics with lower runtime.","Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles  \nDuc-Tien Bui 1 , Ngoc Thinh Nguyen2 , Hung Duy Nguyen3 , Dong Bi 1 , Tomislav Mihalj 1 and Arno Eichberger 1  \narXiv :2607 .04259v 1 [ cs .RO] 5 Jul 2026  \nAbstract—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.  \nI. INTRODUCTION  \nThe development of automated vehicles (AVs) has witnessed significant progress in recent years. As a core component of AVs, path planning is responsible for generating safe and dynamically feasible paths that guide the vehicle through complex environments while meeting requirements for passenger comfort and computational efficiency [1], [2] .  \n*This work was supported by ASEAN-UNINET Project  \n1Duc-Tien Bui is with the Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, [Austria](Austria d.t.bui@tugraz.at)[ d.t.bui@tugraz.at](Austria d.t.bui@tugraz.at)  \n2Ngoc Thinh Nguyen is with Drone Engineering, University of Applied Science Kufstein Tirol, Austria[ngoc-thinh.nguyen@fh-kufstein.ac.at](ngoc-thinh.nguyen@fh-kufstein.ac.at)  \n3Hung Duy Nguyen is with Automation and Control Institute (ACIN), Vienna University of Technology, Austria [nguyen@acin.tuwien.ac.at](nguyen@acin.tuwien.ac.at)  \n1Dong Bi is with the Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, [Austria](Austria dong.bi@tugraz.at)[ dong.bi@tugraz.at](Austria dong.bi@tugraz.at)  \n1Tomislav Mihalj is with the Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria [tomislav.mihalj@tugraz.at](tomislav.mihalj@tugraz.at)  \n1Arno Eichberger is with the Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria [arno.eichberger@tugraz.at](arno.eichberger@tugraz.at)  \nPath planning can be divided into global and local levels [2], [3], where global path planning creates a route from the starting point to the ending point and local path planning generates a concrete segment satisfying constraints, adapting to the changing dynamic environment [4], [5] . Both layers work cooperatively: the global planner provides macroscopic guidance, whereas the local planner ensures safety, feasibility, and smoothness in execution. Despite extensive research, key challenges in path planning remain in achieving smooth path","cbCainXN183d7n0x","https://ap.wps.com/l/cbCainXN183d7n0x","pdf",2023671,1,6,"English","en",105,"# Introduction\n## Global and local path planning\n## Graph-based and potential-field approaches\n## Optimization-based trajectory planning\n## Sampling-based planning and dynamic programming","[{\"question\":\"How does the method combine graph search with MPC for path planning?\",\"answer\":\"It first generates a rough path using Dijkstra search on a discretized grid, then uses that rough path to build a convex lateral safety corridor that guides an MPC-based refinement step.\"},{\"question\":\"What is the role of the convex lateral safety corridor?\",\"answer\":\"The corridor captures obstacle avoidance requirements as continuous feasibility constraints, converting discrete obstacle avoidance decisions into constraints suitable for optimization.\"},{\"question\":\"How is path smoothness ensured in the MPC refinement?\",\"answer\":\"Smoothness is promoted by penalizing the third-order spatial derivative of the lateral offset across the MPC prediction horizon.\"}]",1784183006,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"integrated-graph-search-and-model-predictive-control-for-smooth-and-efficient-path-planning-in-autonomous-vehicles","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/integrated-graph-search-and-model-predictive-control-for-smooth-and-efficient-path-planning-in-autonomous-vehicles/82798/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method combine graph search with MPC for path planning?","Question",{"text":75,"@type":76},"It first generates a rough path using Dijkstra search on a discretized grid, then uses that rough path to build a convex lateral safety corridor that guides an MPC-based refinement step.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of the convex lateral safety corridor?",{"text":80,"@type":76},"The corridor captures obstacle avoidance requirements as continuous feasibility constraints, converting discrete obstacle avoidance decisions into constraints suitable for optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"How is path smoothness ensured in the MPC refinement?",{"text":84,"@type":76},"Smoothness is promoted by penalizing the third-order spatial derivative of the lateral offset across the MPC prediction 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