Projects implemented within the Udacity Self-Driving Car Nanodegree, grouped under one repository:

Project	Screenshot
1. Lane Finding: Basic lane lines detection from a video stream
2. Traffic Signs Classification: Trained a convolutional neural network with TensorFlow to recognize various traffic signs.
3. Behaviorial Cloning: Trained a car to autonomously drive in a simulator using a convolutional neural network, implemented with Keras (YouTube video)
4. Advanced Lane Finding: Lane detection in various lighting conditions and different curvatures from a video stream (YouTube video)
5. Vehicle Detection: Accurately detected moving cars in a video stream

If you plan to test the projects, you'll need to clone the udacity-sdcnd-data as well. Both repositories need to sit side-by-side.

I used a jupyter notebook for each project from Term 1. A straightforward way to run them, is to use the Docker image I built for this purpose:

1. Make sure you have Docker installed.
2. cd to the parent directory of both udacity-sdcnd and udacity-sdcnd-data
3. $ docker run -it -v $PWD:/src -p 8888:8888 yrahal/udacity-carnd bash /bin/run_jupyter.sh. This will pull the Docker image on the first run, create a container and run a jupyter server inside.
4. Open localhost:8888 in your browser and navigate to the project you'd like to test.