Autonomous Vehicles - Jupyter Notebooks
In the following notebooks, we learn various algorithms and tools used for autonomous driving.
In this notebook, we learn to use another optimisation tool
GEKKO to carry out trajectory optimisation.
Here, we implement the rapidly exploring random trees (RRT) algorithm for search space exploration.
We provide the solutions for Tutorial 7 in this notebook.
Using machine learning and computer vision, we learn about image segmentation to detect lanes.
In this notebook, we use classical computer vision without relying on machine learning to detect lanes.
Not only do we learn to detect lanes but also pedestrians in this notebook.
We encourage you to have a look at the notebooks and understand the basics of algorithms used in autonomous vehicles.
You can download all notebooks for Session 7 along with the data used in the notebooks from the link below.