Autonomous-Vehicle Maneuver Planning Using Segmentation and the Alternating Augmented Lagrangian Method

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

Segmenting a motion-planning problem into smaller subproblems could be beneficial in terms of computational complexity. This observation is used as a basis for a new sub-maneuver decomposition approach investigated in this paper in the context of optimal evasive maneuvers for autonomous ground vehicles. The recently published alternating augmented Lagrangianmethod is adopted and leveraged on, which turns out to fit the problem formulation with several attractive properties of the solution procedure. The decomposition is based on moving the coupling constraints between the sub-maneuvers into a separate coordination problem, which is possible to solve analytically. The remaining constraints and the objective function are decomposed into subproblems, one for each segment, which means that parallel computation is possible and benecial. The method is implemented and evaluated in a safety-critical double lane-change scenario. By using the solution of a low-complexity initialization problem and applying warm-start techniques in the optimization, a solution is possible to obtain after just a few alternating iterations using the developed approach. The resulting computational time is lower than solving one optimization problem for the full maneuver.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Linköping University
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Reglerteknik
Originalspråkengelska
Antal sidor8
TidskriftIFAC-PapersOnLine
StatusAccepted/In press - 2020
PublikationskategoriForskning
Peer review utfördJa
Evenemang21st IFAC World Congress - Virtual (Berlin), Berlin, Tyskland
Varaktighet: 2020 jul 132020 jul 17
https://www.ifac2020.org/

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