Self-Driving Car Engineer

Welcome! Get an introduction to the program, discover the services we provide, and get a sneak-peek at the incredible projects you'll build!

What to do if you have questions about your account or general questions about the program.

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

Get a taste of some basic computer vision techniques to find lane markings on the road.

Write code to identify lane lines on the road, first in an image, and later in a video stream.

Project Description - Finding Lane Lines

Project Rubric - Finding Lane Lines

Learn how to calibrate your camera to remove inherent distortions that can affect its perception of the world.

Learn how to use gradient thresholds and different color spaces to more easily identify lane markings on the road.

Discover more advanced computer vision techniques to improve upon your lane lines algorithm!

Write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car.

Project Description - Advanced Lane Finding

Project Rubric - Advanced Lane Finding

Build and train neural networks from linear and logistic regression to backpropagation and multilayer perceptron networks.

The Principal Scientist at Google Brain introduces you to deep learning and Tensorflow, Google's deep learning framework.

Learn how to go from a simple neural network to a deep neural network.

Learn the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.

Using the infamous LeNet neural network architecture, take your first steps toward building a Traffic Sign classifier!

Put your skills to the test by using deep learning to classify different traffic signs!

Project Description - Traffic Sign Classifier

Project Rubric - Traffic Sign Classifier

Take on the neural network framework, Keras! Build and train neural networks more easily.

Learn about some of the most famous neural network architectures, and how you can use them to create new models by leveraging existing canonical networks.

Train a deep neural network to drive a car like you!

Project Description - Behavioral Cloning

Project Rubric - Behavioral Cloning

Build a GitHub Profile on par with senior software engineers.

Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.

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

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

This optional content is designed to refresh knowledge of trigonometry and geometry in support of Term 1 objectives.

Build a Kalman Filter in C++ that's capable of handling data from multiple sources.

Apply everything you've learned about Sensor Fusion by implementing an Extended Kalman Filter in C++!

Project Description - Extended Kalman Filters

Project Rubric - Extended Kalman Filters

Meet the team that will guide you through the localization lessons!

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

Learn about vehicle movement and motion models to predict where your car will be at a future time.

Sebastian will teach you what a particle filter is, as well as the theory and math behind the filter.

Now that you know the theory, learn how to code a particle filter!

In this project, you'll build a particle filter and combine it with a real map to localize a vehicle!

Project Description - Kidnapped Vehicle

Project Rubric - Kidnapped Vehicle

Learn about discrete path planning and algorithms for solving the path planning problem.

Use data from sensor fusion to generate predictions about the likely behavior of moving objects.

Learn how to think about high-level behavior planning in a self-driving car.

Use C++ and the Eigen linear algebra library to build candidate trajectories for the vehicle to follow.

Drive a car down a highway with other cars using your own path planner!

Project Description - Highway Driving

Project Rubric - Highway Driving

Learn about and how to use PID controllers with Sebastian!

Implement a PID controller in C++ to maneuver the vehicle around the lake race track!

Project Description - PID Controller

Project Rubric - PID Controller

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Project Description - Improve Your LinkedIn Profile

Project Rubric - Improve Your LinkedIn Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

Project Description - Optimize Your GitHub Profile

Project Rubric - Optimize Your GitHub Profile

Learn about the system architecture for Carla, Udacity's autonomous vehicle!

Obtain an architectural overview of the ROS Framework and setup your own ROS environment on your computer.

Learn about ROS workspace structure, essential command line utilities, and how to manage software packages within a project.

Learn to write ROS Nodes in Python.

Run your code on Carla, Udacity's own autonomous vehicle!

Project Description - Programming a Real Self-Driving Car

Project Rubric - Programming a Real Self-Driving Car

Congratulations! You've reached the end of the program! Learn how to officially complete the program and graduate.

In this lesson, you'll learn how to detect and track vehicles using color and gradient features and a support vector machine classifier.

While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!

In this lesson, you'll learn about kinematic and dynamic vehicle models. We'll use these later with Model Predictive Control.

In this lesson, you'll learn how to frame the control problem as an optimization problem over time horizons. This is Model Predictive Control!

In this lesson you'll learn the motivation for Fully Convolutional Networks and how they are structured.

In this lesson you'll be introduced to the problem of Scene Understanding and the role FCNs play.

In this lesson you'll become familiar with various optimizations in an effort to squeeze every last bit of performance at inference.

You will learn to make safer vehicles using risk evaluation and systems engineering.

A functional safety plan is critical to any functional safety project. Here you will learn what goes into a safety plan so that you can document your own.

In a hazard analysis and risk assessment, you will identify vehicular malfunctions and evaluate their risk levels. You can then derive safety goals defining how your vehicle will remain safe.

You will derive functional safety requirements from the safety goals and then add extra functionality to the system diagram. Finally you document your work, a part of functional safety.

Once you have derived functional safety requirements, you drill down into more detail. In the technical safety concept, you refine your requirements into technical safety requirements.

The last step in the vehicle safety design phase is to derive hardware and software safety requirements. In this lesson, you will derive these requirements and refine a software system architecture.

Start off with some tips on interviewing for an autonomous systems role, then watch how candidates approach their interview questions. Finish off by practicing some questions of your own!

Project Description - Autonomous Systems Interview Practice Project

Project Rubric - Autonomous Systems Interview Practice Project

Begin the section on data structures and algorithms, including Python and efficiency practice.

Learn the definition of a list in computer science, and see definitions and examples of list-based data structures, arrays, linked lists, stacks, and queues.

Explore how to search and sort with list-based data structures, including binary search and bubble, merge, and quick sort. Learn how to use recursion.

Understand the concepts of sets, maps (dictionaries), and hashing. Examine common problems and approaches to hashing, and practice with examples.

Learn the concepts and terminology associated with tree data structures. Investigate tree types, such as binary search trees, heaps, and self-balancing trees.

Examine the theoretical concept of a graph and understand common graph terms, coded representations, properties, traversals, and paths.

Explore famous computer science problems, specifically the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem.

Learn about the “algorithm” for answering common technical interviewing questions. Practice and get tips for giving interviewers what they’re looking for.

Practice answering behavioral questions and evaluate sample responses.

You've practiced a lot for the interview by now. Continue practicing, and you'll ace the interview!