Steering angle prediction YOLOv5-based end-to-end adaptive neural network control for autonomous vehicles

Abstract

The combination of steering angle prediction and control of autonomous vehicles (AVs) is a challenging task. To improve the real-time steering angle prediction accuracy and the effectiveness of steering control, a novel steering angle prediction YOLOv5-based end-to-end adaptive neural network control for AVs is proposed. Firstly, since most of the lane line datasets are simulated images and lack of diversity, a novel lane dataset derived from the real roads are made manually to train the You Only Look Once version 5 (YOLOv5) network model. To improve the detection accuracy of the network model, the Generalized Intersection over Union (GIoU) of the bounding box regression loss function is updated to a Complete Intersection over Union (CIoU) with a better convergence effect. Furthermore, the neural network-based controller and disturbance observer are proposed to effectively control the steering angle predicted by YOLOv5 and estimate the lumped uncertainty. Meanwhile, a composite adaptive updating law is constructed by utilizing the tracking error and modeling error to improve steering performance. Finally, the system stability is proved by Lyapunov theory and the effectiveness of the proposed method is verified with experiments.