Online Self-Driving Car Engineer Training Course | Udacity

This Nanodegree prepares you to design, build, and optimize self-driving car systems by mastering computer vision, sensor fusion, localization, and control through hands-on projects using C++, Python, and deep learning.

Subscription · Monthly

• Cancel Anytime
• Unlimited access to hundreds of top-rated courses
• Hands-on projects with expert feedback
• Personalized career coaching and interview prep
• Program Certificates

1. An Introduction to Your Nanodegree Program
2. Getting Help

   You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

3. Meet Waymo

   Hear from Waymo, one of the most cutting-edge autonomous vehicle companies out there! You'll learn about the company as well as about the Waymo Open Dataset, which you'll use in parts of the program.

1. Introduction to Deep Learning for Computer Vision

   Dive into Deep Learning for Computer Vision, learning about its use cases, history, and what you’ll build by the end of the course.

2. The Machine Learning Workflow

   Machine learning is more than just building a model - getting each step of the workflow right is crucial.

3. Sensor and Camera Calibration

   Learn how to calibrate your camera to remove distortions for improved perception.

4. From Linear Regression to Feedforward Neural Networks

   Build skills in linear and logistic regression before taking on feedforward neural networks, a type of deep learning.

5. Image Classification with CNNs

   Convolutional networks improve on feedforward networks for areas such as image classification - let’s get started building them!

6. Object Detection in Images

   Object detection builds on classification by finding multiple important objects within a single image - find out how!

7. Object Detection in an Urban Environment

   Use the Waymo dataset to detect objects in an urban environment.

1. Introduction to Sensor Fusion and Perception

   Get started with sensor fusion and perception, why they are important, and the history of their development in self-driving cars.

2. The Lidar Sensor

   Learn about the lidar sensor, capable of capturing important 3D data in point clouds.

3. Detecting Objects in Lidar

   Detect objects from the 3D data coming in from a lidar sensor.

4. Mid-Term Project: 3D Object Detection

   Use the Waymo dataset to detect 3D objects in the surrounding environment.

5. Kalman Filters

   Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.

6. Extended Kalman Filters

   Build an Extended Kalman Filter that's capable of handling data from multiple sources.

7. Multi-Target Tracking

   Get your tracking skills ready for the real world by learning how to track multiple targets simultaneously.

8. Final Project: Sensor Fusion and Object Tracking

   Use the Waymo dataset, along with sensor fusion, to track multiple 3D objects in the surrounding environment.

1. Introduction to Localization

   Meet the team that will guide you through the localization lessons, and learn the intuition behind robotic localization!

2. C++ Checkpoint

   Are you ready to build Kalman Filters with C++? Take these quizzes to find out!

3. Markov Localization

   Learn the math behind localization, as well as how to implement Markov localization in C++.

4. Creating Scan Matching Algorithms

   Learn about and build two scan matching algorithms for localization: Iterative Closest Point (ICP) and Normal Distributions Transforms (NDT).

5. Utilizing Scan Matching

   Learn how to align point clouds with ICP and NDT before leveraging them to localize a self-driving car in a simulated environment!

6. Scan Matching Localization

   Localize a self-driving car within a point cloud from the CARLA simulator with the localization algorithms you learned in previous lessons - how accurate is your algorithm?

Program Instructors

Average Rating: 4.2 (45 Reviews)