Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detectionfor Autonomous Driving

The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane detection. This paper presents an integrated method to improve autonomous driving systems by merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional Neural Network (CNN)-based lane detection. HOG effectively identifies vehicles by capturing edge orientations and structural features, while CNNs excel in detecting intricate lane patterns through deep learning. The combination of these techniques offers a robust solution for detecting both vehicles and lanes, essential for autonomous navigation. Evaluated across a diverse dataset featuring various driving conditions, the system's performance is measured using precision, recall, F1 score (for vehicle detection), and accuracy (for lane detection). The results indicate significant enhancements in detection capabilities, leading to improved situational awareness and safer navigation. Future work will aim to refine the system further and tackle challenges in more complex driving environments, marking this approach as a promising advancement in autonomous driving technology.