Self-driving cars are set to revolutionize the way we live. This is transformational technology, on the cutting-edge of robotics, machine learning, software engineering, and mechanical engineering. In this program, we learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world like Google and Didi.
- Finding Lane Lines on the Road: In this project I detect lane lines in images using Python and OpenCV. OpenCV means "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images.
- Traffic Sign Classifier: In this project, I use what I've learned about deep neural networks and convolutional neural networks to classify traffic signs. Specifically, I trained a model to classify traffic signs from the German Traffic Sign Dataset.
- Behavioral Clonning: In this project, I use the Udacity Simulator to generate a car driving data and train a Convolutional Neural Network model to clone my driving behavior.
- Advanced Lane Finding: In this project, I wrote a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. The camera calibration images, test road images, and project videos are available in the project repository.
- Vehicle Detection & Tracking: In this project, I wrote a software pipeline to identify vehicles in a video from a front-facing camera on a car. The test images and project video are available in the project repository.
