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Efficient Motion Planning for Autonomous Vehicle Maneuvers Using Duality-Based Decomposition
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Lund Univ, Sweden.ORCID iD: 0000-0003-1320-032X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 5, p. 78-84Conference paper, Published paper (Refereed)
Abstract [en]

A method to decompose a motion-planning problem into several segments is presented. It is based on a modification of the original problem, such that certain variables at the splitting points are considered to be precomputed and thus fixed and the remaining variables are obtained by performing Lagrange relaxation. The resulting dual problem is split into several subproblems, allowing parallel computation. The method is formalized as a computational algorithm and evaluated in a safety critical double lane-change situation. The resulting maneuver has close-to-optimal behavior and, for certain initialization strategies, it is obtained in shorter computational time compared to computing the full maneuver in one step. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER , 2019. Vol. 52, no 5, p. 78-84
Keywords [en]
trajectory and path planning; autonomous vehicles; duality-based decomposition; motion control; safety; intelligent transportation systems
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-161215DOI: 10.1016/j.ifacol.2019.09.013ISI: 000486629500014OAI: oai:DiVA.org:liu-161215DiVA, id: diva2:1365651
Conference
9th IFAC International Symposium on Advances in Automotive Control (AAC)
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2019-11-21
In thesis
1. Computation of Autonomous Safety Maneuvers Using Segmentation and Optimization
Open this publication in new window or tab >>Computation of Autonomous Safety Maneuvers Using Segmentation and Optimization
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis studies motion planning for future autonomous vehicles with main focus on passenger cars. By having automatic steering and braking together with information about the environment, such as other participants in the traffic or obstacles, it would be possible to perform autonomous maneuvers while taking limitations of the vehicle and road–tire interaction into account. Motion planning is performed to find such maneuvers that bring the vehicle from the current state to a desired future state, here by formulating the motion-planning problem as an optimal control problem. There are a number of challenges for such an approach to motion planning; some of them are how to formulate the criterion in the motion planning (objective function in the corresponding optimal control problem), and how to make the solution of motion-planning problems efficient to be useful in online applications. These challenges are addressed in this thesis.

As a criterion for motion-planning problems of passenger vehicles on doublelane roads, it is investigated to use a lane-deviation penalty function to capture the observation that it is dangerous to drive in the opposing lane, but safe to drive in the original lane after the obstacle. The penalty function is augmented with certain additional terms to address also the recovery behavior of the vehicle. The resulting formulation is shown to provide efficient and steady maneuvers and gives a lower time in the opposing lane compared to other objective functions. Under varying parameters of the scenario formulation, the resulting maneuvers are changing in a way that exhibits structured characteristics.

As an approach to improve efficiency of computations for the motion-planning problem, it is investigated to segment motion planning of the full maneuver into several smaller maneuvers. A way to extract segments is considered from a vehicle dynamics point of view, and it is based on extrema of the vehicle orientation and the yaw rate. The segmentation points determined using this approach are observed to allow efficient splitting of the optimal control problem for the full maneuver into subproblems.

Having a method to segment maneuvers, this thesis further studies methods to allow parallel computation of these maneuvers. One investigated method is based on Lagrange relaxation and duality decomposition. Smaller subproblems are formulated, which are governed by solving a low-complexity coordination problem. Lagrangian relaxation is performed on a subset of the dynamic constraints at the segmentation points, while the remaining variables are predicted. The prediction is possible because of the observed structured characteristics resulting from the used lane-deviation penalty function. An alternative approach is based on adoption of the alternating augmented Lagrangian method. Augmentation of the Lagrangian allows to apply relaxation for all dynamic constraints at the segmentation points, and the alternating approach makes it possible to decompose the full problem into subproblems and coordinating their solutions by analytically solving an overall coordination problem. The presented decomposition methods allow computation of maneuvers with high correspondence and lower computational times compared to the results obtained for solving the full maneuver in one step.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 11
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1860
National Category
Vehicle Engineering Robotics Computer Vision and Robotics (Autonomous Systems) Computational Mathematics Control Engineering
Identifiers
urn:nbn:se:liu:diva-162164 (URN)10.3384/lic.diva-162164 (DOI)9789179299477 (ISBN)
Presentation
2019-12-12, Ada Lovelace, B-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-22Bibliographically approved

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