Visual Perception for Autonomous Driving

For a self-driving car to navigate safely, it must first accurately interpret the visual world around it. This course provides a solid foundation in how visual data is processed to identify obstacles, lane markings, and traffic signs, turning raw pixels into actionable information for autonomous systems. You will gain a clear understanding of how cameras function as sensors and how to transform 2D images into 3D spatial data. By the end of this course, you will be able to explain the core mechanisms that allow a vehicle to perceive its environment and distinguish between different types of road participants. What you'll learn: - Understand the pinhole camera model and the geometry of image formation - Perform camera calibration to account for intrinsic and extrinsic parameters - Identify and match image features to track movement across frames - Apply object detection techniques for both static and dynamic road elements - Explore modern vision architectures and transformers for enhanced spatial awareness - Interpret semantic segmentation to distinguish drivable surfaces from obstacles The course begins with fundamental definitions of light and camera geometry before moving into the logic behind sophisticated recognition and tracking algorithms. You will read through detailed explanations and analyze code snippets that demonstrate how these principles are applied in modern automotive technology. This course is designed for beginners interested in robotics, AI, or automotive engineering. No prior experience in computer vision is required to start. Begin your journey into the technology behind autonomous navigation today.