Nanodegree Program
Complete your journey to a Self-Driving Car career
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Time
3 Three-Month Terms
Study 15 hrs/week and complete in 9 mo.
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Student Rating
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Estimated Salary
Based on US job data
Prerequisites and Requirements
To optimize your chances for a successful application to our Self-Driving Car Engineer Nanodegree program, we’ve created a list of prerequisites and recommendations to help prepare you for the program curriculum. Prior to applying, you should have the following knowledge:
- Intermediate Python (Classes, Data structures)
- Intermediate C++ (Classes, Memory management, Linking)
- Basic Linear Algebra (Matrices, Vectors, Matrix multiplication)
- Basic Calculus (Derivatives, Integrals)
- Basic Statistics (Mean, Standard deviation, Gaussian distribution)
- Basic Physics (Forces)
Certain knowledge areas are particularly important to address, and we recommended the following resources for those wishing to refine their skills in these key arenas:
- Python - The free Introduction To Python Programming Course on Udacity
- Probability - Khan Academy's great probability lectures
- Linear Algebra - Khan Academy's great linear algebra lectures
- Calculus - Khan Academy's great multivariable calculus lectures
We also recommend the following suite of Udacity courses as excellent preparation for incoming students:
- Artificial Intelligence for Robotics
- Intro to Machine Learning
- Intro to Data Science
- Machine Learning Engineer Nanodegree program
- Data Analyst Nanodegree program
- Intro to Programming Nanodegree program
For those aspiring Self-Driving Car Engineers who currently have limited backgrounds in either programming or math, we recommend the following Nanodegree programs and courses:
- Programming foundations with Python (course)
- Intro to Programming (Nanodegree program)
- Data Analyst (Nanodegree program)
And for those who have programming and math backgrounds, but would benefit from additional studies in machine learning and/or computer vision:
- Intro to Machine Learning (course)
- Intro to Data Science (course)
- Intro to Computer Vision (course)
- Artificial Intelligence for Robotics (course)
- Machine Learning Engineer (Nanodegree program)
In Collaboration With
Why Take This Nanodegree Program?
Self-driving cars represent one of the most significant advances in modern history. Their impact will go beyond technology, beyond transportation, beyond urban planning to change our daily lives in ways we have yet to imagine.
Students who enroll in this program will master technologies that are going to shape the future. Through interactive projects in computer vision, robotic controls, localization, path planning, and more, you’ll prepare yourself for a key role in this incredible field. If your goal is to build the future, then your future begins here.
A One-Of-A-Kind Program
Sebastian Thrun and the Udacity Self-Driving Car team are pioneering educators in this field, and Udacity offers the only program of its kind, where you can learn everything you need to know to launch a successful career as a Self-Driving Car Engineer.
World-Class Curriculum
In this program, you’ll learn from the some of the most innovative companies operating in this field. Companies like NVIDIA, Mercedes-Benz, and more. Their teams are defining the future of autonomous transportation, and they helped us build this incredible curriculum.
Valuable Hiring Partnerships
Our hiring partners are some of the most forward-looking companies in the world, and they're looking for Udacity graduates to fill critical roles today. These partnerships represent a unique opportunity to benefit from fast-tracked consideration for open roles at partner companies, and this affords you a distinct leg up in your job searches.
Real-World Learning
In addition to the groundbreaking work you’ll do in simulation, you’ll have the opportunity to run your code on a real self-driving car!
Learn with the Best
Sebastian Thrun
Udacity President
Sebastian Thrun is a scientist, educator, inventor, and entrepreneur. As the founder and president of Udacity, Sebastian’s mission is to democratize education by providing lifelong learning to millions of students worldwide.
David Silver
Curriculum Lead
David Silver leads the Self-Driving Car Engineer Nanodegree Program. Before Udacity, David was a research engineer on the autonomous vehicle team at Ford. He has an MBA from Stanford, and a BSE in computer science from Princeton.
Ryan Keenan
Content Developer
Ryan has a PhD in Astrophysics and a passion for teaching and learning. He is also one of the lead instructors in the Self-Driving Car Nanodegree program. When he’s not building Udacious robotics lessons you’ll find him up in the mountains or out in the surf.
Cezanne Camacho
Content Developer
Cezanne is an expert in computer vision with an M.S. in Electrical Engineering from Stanford University. Inspired by anyone with the drive and imagination to learn something new, she aims to create more inclusive and effective STEM education.
Mercedes-Benz
Mercedes-Benz Team
Mercedes-Benz R&D North America develops the world’s most advanced automotive technology and vehicle design with luxury and style. The team from Mercedes built our Sensor Fusion, Localization, and Path Planning content.
Nvidia
Nvidia Team
NVIDIA is a company built upon great minds and groundbreaking research. With more than 120 scientists around the globe, the areas of focus include AI, self-driving cars, high-performance computing, graphics, VR, and augmented reality.
Uber ATG
Uber ATG Team
The Advanced Technologies Group is comprised of Uber’s self-driving engineering team dedicated to self-driving technologies, mapping, and vehicle safety.
Elektrobit
Elektrobit Team
Benjamin Brentrop, Elektrobit’s Head of Functional Safety Consulting, has been working in the testing and functional safety field since 2006. In his current role at EB, he consults with OEM and Tier 1s, to provide functional safety knowledge and expertise for global automotive projects.
What You Will Learn
Download SyllabusSelf-Driving Car Engineer Syllabus
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Term 1
Computer Vision and Deep Learning
See Fewer Details
Prerequisite Knowledge
To optimize your chances for a successful application to our Self-Driving Car Engineer Nanodegree program, we’ve created a list of prerequisites and recommendations to help prepare you for the program curriculum. See detailed requirements.
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Introduction
In this course, you will learn about how self-driving cars work, and you’ll take a crack at your very first autonomous vehicle project - finding lane lines on the road! We’ll also introduce the Nanodegree Program and the services we provide over the course of the journey.
Finding Lane Lines on the Road
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Deep Learning
Deep learning has become the most important frontier in both machine learning and autonomous vehicle development. Experts from NVIDIA and Uber ATG will teach you to build deep neural networks and train them with data from the real world and from the Udacity simulator.
Traffic Sign Classifier
Behavioral Cloning
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Computer Vision
You’ll use a combination of cameras, software, and machine learning to find lane lines on difficult roads and to track vehicles. You’ll start with calibrating cameras and manipulating images, and end by applying support vector machines and decision trees to extract information from video.
Advanced Lane Finding
Vehicle Tracking
Term 2
Sensor Fusion, Localization, and Control
See Fewer Details
Prerequisite Knowledge
To optimize your chances for a successful application to our Self-Driving Car Engineer Nanodegree program, we’ve created a list of prerequisites and recommendations to help prepare you for the program curriculum. See detailed requirements.
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Sensor Fusion
Tracking objects over time is a major challenge for understanding the environment surrounding a vehicle. Sensor fusion engineers from Mercedes-Benz will show you how to program fundamental mathematical tools called Kalman filters. These filters predict and determine with certainty the location of other vehicles on the road.
Extended Kalman Filters
Unscented Kalman Filters
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Localization
Localization is how we determine where our vehicle is in the world. GPS is great, but it’s only accurate to within a few meters. We need single-digit centimeter-level accuracy! To achieve this, Mercedes-Benz engineers will demonstrate the principles of Markov localization to program a particle filter, which uses data and a map to determine the precise location of a vehicle.
Kidnapped Vehicle
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Control
Ultimately, a self-driving car is still a car, and we need to send steering, throttle, and brake commands to move the car through the world. Uber ATG will walk you through building both proportional-integral-derivative (PID) controllers and model predictive controllers. Between these control algorithms, you’ll become familiar with both basic and advanced techniques for actuating a vehicle.
PID Controller
Model Predictive Control
Term 3
Path Planning, Concentrations, and Systems
See Fewer Details
Prerequisite Knowledge
To optimize your chances for a successful application to our Self-Driving Car Engineer Nanodegree program, we’ve created a list of prerequisites and recommendations to help prepare you for the program curriculum. See detailed requirements.
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Path Planning
The Mercedes-Benz Vehicle Intelligence team will take you through the three stages of path planning. First, you’ll apply model-driven and data-driven approaches to predict how other vehicles on the road will behave. Then you’ll construct a finite state machine to decide which of several maneuvers your own vehicle should undertake. Finally, you’ll generate a safe and comfortable trajectory to execute that maneuver.
Path Planning Project
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Elective: Advanced Deep Learning
Students in this elective, built with the NVIDIA Deep Learning Institute, will learn about semantic segmentation, and inference optimization, active areas of deep learning research. This course is an elective. Students choose between completing either Advanced Deep Learning or Functional Safety for graduation.
Elective: Advanced Deep Learning
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Elective: Functional Safety
Students who select the Functional Safety specialization, built with Elektrobit, learn functional safety frameworks to ensure that vehicles are safe, both at the system and component levels. This course is an elective. Students choose between completing either Advanced Deep Learning or Functional Safety for graduation.
Elective: Functional Safety
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System Integration
This is capstone of the entire Self-Driving Car Engineer Nanodegree Program! We’ll introduce Carla, the Udacity self-driving car, and the Robot Operating System that controls her. You’ll work with a team of othe Nanodegree students to combine what you’ve learned over the course of the entire Nanodegree Program to drive Carla, a real self-driving car, around the Udacity test track!
Programming a Real Self-Driving Car!
Apply Now
Term 1
Computer Vision and Deep Learning
total
Start your self-driving car training by applying Computer Vision and Deep Learning to automotive problems.
Related Nanodegree Programs
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intermediate
This Program
Self-Driving Car Engineer
Nanodegree Program
Become a Self-Driving Car Engineer
Advanced
“There's an enormous market for self-driving car engineers. Lots and lots of companies that you wouldn't suspect have entered that field and are massively hiring.”
— Sebastian Thrun, Udacity
Previous
FAQ
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Why should I enroll in this program?
Udacity is the only place to offer this kind of opportunity. We have partnered with the best companies in the field to offer world-class curriculum, expert instructors, and exclusive hiring opportunities. Almost any student anywhere in the world with an internet connection can study to become a self-driving car engineer at Udacity. You'll even build and run code on an actual autonomous vehicle that is owned by Udacity.
Project
Behavioral Cloning
Put your deep learning skills to the test with this project! Train a deep neural network to drive a car like you!
Project
Elective: Functional Safety
In this project, you will create a report that documents the functional safety of a lane assistance system, demonstrating their knowledge of functional safety including developing a hazard and risk analysis, safety concepts, and engineering safety requirements.
Project
Elective: Advanced Deep Learning
In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).
Project
Extended Kalman Filters
In this project, you'll apply everything you've learned so far about Sensor Fusion by implementing an Extended Kalman Filter in C++!
Project
Finding Lane Lines on the Road
In this project, you will be writing code to identify lane lines on the road, first in an image, and later in a video stream (really just a series of images). To complete this project you will use the tools you learned about in the lesson, and build upon them.
Project
Unscented Kalman Filters
Put your skills to the test! Use C++ to code an Unscented Kalman Filter capable of tracking non-linear motion.
Project
Advanced Lane Finding
In this project, your goal is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car.
Project
Model Predictive Control
In this project, you'll implement Model Predictive Control to drive a vehicle around the track even with additional latency between commands!
Project
Programming a Real Self-Driving Car!
In this project you will be run your code on Carla, the Udacity self-driving vehicle! You can provide feedback in the project rubric along with links to datasets or videos that you want to provide to the student.
Project
PID Controller
In this project you'll revisit the lake race track from the Behavioral Cloning Project. This time, however, you'll implement a PID controller in C++ to maneuver the vehicle around the track!
Project
Vehicle Tracking
In this project, your goal is to write a software pipeline to identify vehicles in a video from a front-facing camera on a car.
Project
Traffic Sign Classifier
You just finished getting your feet wet with deep learning. Now put your skills to the test by using deep learning to classify different traffic signs! In this project, you will use what you've learned about deep neural networks and convolutional neural networks to classify traffic signs.
Project
Path Planning Project
In this project, you’ll design a path planner that is able to create smooth, safe paths for the car to follow along a 3 lane highway with traffic.
Project
Kidnapped Vehicle
In this project, you'll build a particle filter and combine it with a real map to localize a vehicle!
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