Design and Implementation of Deep Neural Network-Based Control for Automatic Parking Maneuver Process

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 4; pp. 1400 - 1413
• Main Authors: Chai, Runqi, Tsourdos, Antonios, Savvaris, Al, Chai, Senchun, Xia, Yuanqing, Chen, C. L. Philip
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Automatic control; Autonomous ground vehicles (AGVs); Autonomous vehicles; Computational modeling; Data management; deep neural network (DNN); Design; Land vehicles; motion controller; Multilayers; Neural networks; Parking; parking maneuver; Real time; Real-time systems; Roads; Trajectory optimization; Unmanned ground vehicles
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3042120

This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.