Autonomous Vehicles bundle

Inside bundle

Intro to Self-Driving Cars


This will be the introductory course for Self-Driving Cars. We will learn the basics of Autonomous Driving.

Sensor Fusion


We will explore everything that goes on in fusing data in self-driving cars. Probably it will be the most challenging part of your journey.



The objective of SLAM is to concurrently build a map of an environment and allow the robot to localize itself within that environment. Although SLAM is not only devoted to mobile robots, it was first thought of as a tool for mobile robot autonomous navigation.



For self-driving cars, we can apply forces to the vehicle via three "control inputs": steering, throttle, and brake. The algorithms that determine how to use these control inputs,

Self-Driving Car prototype


 Vehicle detection is one of the autonomous car applications to detect nearby vehicles to avoid collisions. This application can also be used for traffic management based on traffic density. In this course we will do the project based on Nvidia Jetson Nano.

Self-Driving Cars with Computer Vision


We will apply computer vision techniques to build our vision base driverless car and we will test it in the Carla simulator.

Reinforcement Learning for Self-Driving Cars


This dissertation argues that reinforcement learning utilizing stored instances of past observations as value estimates is an effective and practical means of controlling dynamical systems such as autonomous vehicles.

Kalman Filter


Autonomous Vehicle Kalman Filter can be used to predict the next set of actions that the car in front of our autonomous vehicle is going to take based upon the data our vehicle receives



Due to those characteristics, ROS is a perfect tool for self-driving cars. After all, an autonomous vehicle can be considered just as another type of robot, so the same types of programs can be used to control them.

Tesla Autopilot


In this course, we will thoroughly discuss how the Tesla Autopilot works, and we will do some demos by ourselves on a cloud-based GPU platform.