Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 52 |
| Fachzeitschrift | Algorithms |
| Jahrgang | 13 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 28 Feb. 2020 |
Abstract
This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bezier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bezier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Mathematik (insg.)
- Numerische Mathematik
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Mathematik (insg.)
- Computational Mathematics
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in: Algorithms, Jahrgang 13, Nr. 3, 52, 28.02.2020.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
AU - Abdelaal, Mohamed
AU - Schön, Steffen
N1 - Funding information: This research was funded by the German Research Foundation (DFG) as a part of the Research Training Group Integrity and Collaboration in dynamic SENSor networks (i.c.sens) [GRK2159].
PY - 2020/2/28
Y1 - 2020/2/28
N2 - This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bezier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bezier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
AB - This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bezier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bezier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
KW - Autonomous driving
KW - Nonlinear model predictive control
KW - Optimization
KW - Path following
UR - http://www.scopus.com/inward/record.url?scp=85083495147&partnerID=8YFLogxK
U2 - 10.3390/a13030052
DO - 10.3390/a13030052
M3 - Article
AN - SCOPUS:85083495147
VL - 13
JO - Algorithms
JF - Algorithms
IS - 3
M1 - 52
ER -