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Spatial-based Trajectory Planning under Vehicle Dimension Constraints Using Sequential Linear Programming
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-6802-7520
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-3672-5316
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, ob- stacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are lin- earized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space. 

Place, publisher, year, edition, pages
IEEE, 2017.
Keyword [en]
Path Planning, Optimization, Autonomous driving
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220574OAI: oai:DiVA.org:kth-220574DiVA, id: diva2:1169478
Conference
IEEE Intelligent Transportation Systems Conference
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-01-19Bibliographically approved

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fulltext(401 kB)44 downloads
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Lima, Pedro F.Mårtensson, JonasWahlberg, Bo
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CiteExportLink to record
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Citation style
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