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.

自动驾驶汽车 (AV) 的转向角预测和控制的结合是一项具有挑战性的任务。为了提高实时转向角预测精度和转向控制的有效性，提出了一种新颖的基于 YOLOv5 的 AV 端到端自适应神经网络控制的转向角预测。首先，由于大多数车道线数据集是模拟图像且缺乏多样性，因此手动制作了一个源自真实道路的新型车道数据集来训练 You Only Look Once version 5 (YOLOv5) 网络模型。为提高网络模型的检测精度，将边界框回归损失函数的Generalized Intersection over Union (GIoU)更新为收敛效果更好的Complete Intersection over Union (CIoU)。此外，提出了基于神经网络的控制器和干扰观测器来有效控制YOLOv5预测的转向角并估计集总不确定性。同时，利用跟踪误差和建模误差构建复合自适应更新律以提高转向性能。最后，通过李雅普诺夫理论证明了系统的稳定性，并通过实验验证了所提方法的有效性。