Abstract
For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization algorithm and model predictive control. In the trajectory planning layer, the vehicle kinematics model suitable for the low-velocity parking scene is established. Considering the vehicle physical constraints, boundary condition constraints, and obstacle avoidance constraints during the parking process, the parking trajectory planning task is described as an optimal control problem, further transformed into a nonlinear programming problem by Gauss pseudo-spectral method. Taking the shortest parking completion time as the optimization objective function, the parking trajectories of the large, medium and small parking spaces are obtained, respectively. A parking trajectory tracking controller based on the model predictive control algorithm is designed in the trajectory tracking control layer. The linear error model is used as the prediction model, and the quadratic programming is adopted as the rolling optimization algorithm in the tracking controller. The velocity and front-wheel swing angle are obtained as control signals for parking trajectory tracking. Through CarSim and Simulink's co-simulation, the feasibility and effectiveness of the proposed parallel parking trajectory planning and tracking control method are verified. The co-simulation results show that the maximum tracking errors of horizontal and longitudinal positions are less than 0.15m. The maximum tracking errors of heading angle are less than 2° under three different parking spaces. Real vehicle tests are carried out to verify the effectiveness of the proposed hierarchical control method. The test results show that the vehicle can park in the parking space safely, quickly and accurately when the actual parking space is detected. The proposed method can plan the parking trajectory with the constraints and the shortest time and control the vehicle to complete the parking operation accurately along the planned trajectory.
Highlights
With the continuous improvement of automobile intelligence and network, assisted driving and autonomous driving technology have become the research hotspot of major automobile manufacturers at home and abroad
The innovation of this paper is to propose a hierarchical control method for autonomous parallel parking trajectory planning and trajectory tracking
The Gauss pseudo-spectral Method (GPM) algorithm is adopted in the planning layer to realize the rapid planning of autonomous parallel parking trajectory
Summary
With the continuous improvement of automobile intelligence and network, assisted driving and autonomous driving technology have become the research hotspot of major automobile manufacturers at home and abroad. The interior-point method is further adopted to solve the nonlinear programming problem Many advantages, such as fast convergence speed and low sensitivity to the initial value, make this method more suitable for autonomous parking trajectory planning. The application of model predictive control (MPC) is more and more in control scenarios, which requires less model accuracy, but has better control stability and accuracy It is more suitable for solving the trajectory tracking control of autonomous parking with obvious features, such as low speed, a significant change of reference path curvature, and heading; many constraints exist in the solution process. This paper proposes a GPM-MPC based hierarchical control method for autonomous parking trajectory planning and tracking according to the above architecture.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
