Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

authored by: Mohamed Abdelaal, Steffen Schön
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.
Organisation(s): Institute of Cartography and Geoinformatics
Geodetic Institute
Leibniz Research Centre FZ:GEO
Type: Article
Journal: Algorithms
Volume: 13
Publication date: 28.02.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, Numerical Analysis, Computational Theory and Mathematics, Computational Mathematics
Electronic version(s): https://doi.org/10.3390/a13030052 (Access: Open)
: Details in the research portal "Research@Leibniz University"