Dynamics-Based Optimal Motion Planning of Multiple Lane Changes using Segmentation
(2022) 10th IFAC Symposium on Advances in Automotive Control, AAC 2022 In IFAC-PapersOnLine 55(24). p.233-240- Abstract
Avoidance maneuvers at normal driving speed or higher are demanding driving situations that force the vehicle to the limit of tire–road friction in critical situations. To study and develop control for these situations, dynamic optimization has been in growing use in research. One idea to handle such optimization computations effectively is to divide the total maneuver into a sequence of sub-maneuvers and to associate a segmented optimization problem to each sub-maneuver. Here, the alternating augmented Lagrangian method is adopted, which like many other optimization methods benefits strongly from a good initialization, and to that purpose a method with motion candidates is proposed to get an initially feasible motion. The two main... (More)
Avoidance maneuvers at normal driving speed or higher are demanding driving situations that force the vehicle to the limit of tire–road friction in critical situations. To study and develop control for these situations, dynamic optimization has been in growing use in research. One idea to handle such optimization computations effectively is to divide the total maneuver into a sequence of sub-maneuvers and to associate a segmented optimization problem to each sub-maneuver. Here, the alternating augmented Lagrangian method is adopted, which like many other optimization methods benefits strongly from a good initialization, and to that purpose a method with motion candidates is proposed to get an initially feasible motion. The two main contributions are, firstly, the method for computing an initially feasible motion that is found to use obstacle positions and progress of vehicle variables to its advantage, and secondly, the integration with a subsequent step with segmented optimization showing clear improvements in paths and trajectories. Overall, the combined method is able to handle driving scenarios at demanding speeds.
(Less)
- author
- Anistratov, Pavel ; Olofsson, Björn LU and Nielsen, Lars LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IFAC-PapersOnLine
- volume
- 55
- issue
- 24
- pages
- 8 pages
- publisher
- IFAC Secretariat
- conference name
- 10th IFAC Symposium on Advances in Automotive Control, AAC 2022
- conference location
- Columbus, United States
- conference dates
- 2022-08-29 - 2022-08-31
- external identifiers
-
- scopus:85144292206
- ISSN
- 2405-8963
- DOI
- 10.1016/j.ifacol.2022.10.290
- project
- RobotLab LTH
- ELLIIT B14: Autonomous Force-Aware Swift Motion Control
- language
- English
- LU publication?
- yes
- id
- 037d0b5c-53b4-4e1d-9522-61c50ca5254e
- date added to LUP
- 2023-01-12 10:56:43
- date last changed
- 2023-11-21 15:25:17
@article{037d0b5c-53b4-4e1d-9522-61c50ca5254e,
abstract = {{<p>Avoidance maneuvers at normal driving speed or higher are demanding driving situations that force the vehicle to the limit of tire–road friction in critical situations. To study and develop control for these situations, dynamic optimization has been in growing use in research. One idea to handle such optimization computations effectively is to divide the total maneuver into a sequence of sub-maneuvers and to associate a segmented optimization problem to each sub-maneuver. Here, the alternating augmented Lagrangian method is adopted, which like many other optimization methods benefits strongly from a good initialization, and to that purpose a method with motion candidates is proposed to get an initially feasible motion. The two main contributions are, firstly, the method for computing an initially feasible motion that is found to use obstacle positions and progress of vehicle variables to its advantage, and secondly, the integration with a subsequent step with segmented optimization showing clear improvements in paths and trajectories. Overall, the combined method is able to handle driving scenarios at demanding speeds.</p>}},
author = {{Anistratov, Pavel and Olofsson, Björn and Nielsen, Lars}},
issn = {{2405-8963}},
language = {{eng}},
number = {{24}},
pages = {{233--240}},
publisher = {{IFAC Secretariat}},
series = {{IFAC-PapersOnLine}},
title = {{Dynamics-Based Optimal Motion Planning of Multiple Lane Changes using Segmentation}},
url = {{http://dx.doi.org/10.1016/j.ifacol.2022.10.290}},
doi = {{10.1016/j.ifacol.2022.10.290}},
volume = {{55}},
year = {{2022}},
}