Efficient Local Navigation Approach for Autonomous Driving Vehicles
This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has...
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.79776-79792 |
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| creator | Lopez, Joaquin Sanchez-Vilarino, Pablo Sanz, Rafael Paz, Enrique |
| description | This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane' s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype. |
| doi_str_mv | 10.1109/ACCESS.2021.3084807 |
| format | Article |
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The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane' s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3084807</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Automobiles ; Autonomous navigation ; Autonomous vehicles ; Autonomous vehicles (AVs) ; Collision avoidance ; Constraints ; Control algorithms ; Control theory ; Curvature ; Navigation ; Navigation systems ; Obstacle avoidance ; Optimization ; reactive control ; Roads ; Sensors ; Trajectory ; vehicle motion control ; Velocity</subject><ispartof>IEEE access, 2021, Vol.9, p.79776-79792</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane' s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.</description><subject>Algorithms</subject><subject>Automobiles</subject><subject>Autonomous navigation</subject><subject>Autonomous vehicles</subject><subject>Autonomous vehicles (AVs)</subject><subject>Collision avoidance</subject><subject>Constraints</subject><subject>Control algorithms</subject><subject>Control theory</subject><subject>Curvature</subject><subject>Navigation</subject><subject>Navigation systems</subject><subject>Obstacle avoidance</subject><subject>Optimization</subject><subject>reactive control</subject><subject>Roads</subject><subject>Sensors</subject><subject>Trajectory</subject><subject>vehicle motion control</subject><subject>Velocity</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkE1PwzAMhiMEEhPsF-xSiXNHvpqPY1UGTJrgMOAaJWm6ZdqakXaT-PdkdJrwxZbl97X9ADBBcIoQlI9lVc2WyymGGE0JFFRAfgVGGDGZk4Kw63_1LRh33QamEKlV8BGoZk3jrXdtny2C1dvsTR_9Svc-tFm538eg7TprQszKQx_asAuHLnuK_ujbVfbl1t5uXXcPbhq97dz4nO_A5_Pso3rNF-8v86pc5JZC0efMEmGZREJDJikyjBgpDWaFMFxTVFNWyFpCg50RTHPpSC2M5IhT7hgnkNyB-eBbB71R--h3Ov6ooL36a4S4Ujr2p5OUrQXndYOLGlmqiyZtQMbwxtQIOyRR8noYvNKL3wfX9WoTDrFN5ytcEEkKLAVNU2SYsjF0XXTNZSuC6gRfDfDVCb46w0-qyaDyzrmLQlJKEnTyCx53ftI</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Lopez, Joaquin</creator><creator>Sanchez-Vilarino, Pablo</creator><creator>Sanz, Rafael</creator><creator>Paz, Enrique</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane' s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3084807</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-0337-7407</orcidid><orcidid>https://orcid.org/0000-0001-9151-4346</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Automobiles Autonomous navigation Autonomous vehicles Autonomous vehicles (AVs) Collision avoidance Constraints Control algorithms Control theory Curvature Navigation Navigation systems Obstacle avoidance Optimization reactive control Roads Sensors Trajectory vehicle motion control Velocity |
| title | Efficient Local Navigation Approach for Autonomous Driving Vehicles |
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