Computer Vision for Autonomous Driving

Abstract

Autonomous driving has emerged as one of the most transformative innovations in intelligent transportation systems. Modern self-driving vehicles rely heavily on computer vision techniques to understand, interpret, and react to their surroundings in real time. The complexity of the driving environment characterized by dynamic obstacles, traffic signs, lane markings, pedestrians, and varying lighting conditions demands robust perception systems. Computer vision enables autonomous vehicles to detect objects, segment scenes, estimate depth, track motion, and make high-accuracy decisions essential for safe navigation. This paper presents a comprehensive overview of the essential computer vision components used in autonomous driving, including camera systems, sensor fusion, object detection, lane detection, traffic sign recognition, and depth estimation. It also discusses commonly used datasets, algorithms, and deep learning models such as CNNs, YOLO, SSD, HOG, SVM, RANSAC, Canny, and semantic segmentation networks. The work synthesizes how these modules collectively support perception, prediction, and planning stages in autonomous vehicles. Expanded sections detail the design, workflow, implementation considerations, and practical challenges encountered in real-world deployments. The discussion illustrates the critical role of computer vision in enabling vehicles to operate safely and efficiently without human intervention.