An Improved Lane-Keeping Controller for Autonomous Vehicles Leveraging an Integrated CNN-LSTM Approach

Representing the task of navigating a car through traffic using traditional algorithms is complex endeavor that presents significant challenges. To overcome this, researchers have started training artificial neural networks data from front-facing cameras, combined with corresponding steering angles. However, many current solutions focus solely on visual information camera frames, overlooking important temporal relationships between these frames. This paper introduces novel approach to end-to-end control by combining VGG16 convolutional network (CNN) architecture Long Short-Term Memory (LSTM). integrated model enables learning both dependencies within sequence images and dynamics process. Furthermore, we will present evaluate estimated accuracy proposed for angle prediction, comparing it various CNN models including Nvidia classic model, MobilenetV2 when LSTM. The method demonstrates superior compared other approaches, achieving lowest loss function. its performance, recorded video saved results based human perception robot operating system (ROS2). videos are then split into image sequences be smoothly fed processing training.