Self-Driving Car Engineer

Welcome! This lesson will introduce you to the program, help you discover the services we provide, and show you all the incredible projects you'll build!

An introduction to and explanation of Linux-based workspaces that can be interfaced via a shell (BASH).

In this first lesson, you'll discover some basic computer vision techniques to find lane markings on the road. We’ll dive much deeper into computer vision in later lessons, so relax and have fun!

In the first project, apply your new computer vision skills to find lane markings on the road!

Project Description - Finding Lane Lines on the Road

Project Rubric - Finding Lane Lines on the Road

As you learn the skills you’ll need in order to work in the autonomous vehicle industry, you’ll see optional Career Lessons and Projects that will help ensure you're best presenting your new skills to potential employers. Meet the Careers team who will help you meet your career goals.

Learn to build and train neural networks, starting with the foundations in linear and logistic regression, and culminating in backpropagation and multilayer perceptron networks.

In this assignment, you will build your own neural network library from scratch! Your library, MiniFlow, will behave much like TensorFlow, Google's deep learning library.

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

Vincent walks you through how to go from a simple neural network to a deep neural network. You'll learn about why additional layers can help and how to prevent overfitting.

Vincent explains 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!

You just finished getting your feet wet with deep learning. Now put your skills to the test by using deep learning to build your own model to classify different traffic signs!

Project Description - Traffic Sign Classifier

Project Rubric - Traffic Sign Classifier

Take on the neural network framework, Keras. You'll be amazed how few lines of code you'll need to build and train deep neural networks!

Learn about some of the most famous neural network architectures and how you can use them. By the end of this lesson, you'll know how to create new models by leveraging existing canonical networks.

Put your deep learning skills to the test with this project! Train a deep neural network to drive a car like you!

Project Description - Behavioral Cloning

Project Rubric - Behavioral Cloning

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!

Build a pipeline using distortion correction, image rectification, color transforms, and gradient thresholding to identify lane lines and their curvature in a video.

Project Description - Advanced Lane Finding

Project Rubric - Advanced Lane Finding

Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.

See how support vector machines work, and code one using Scikit Learn.

Learn about decision trees, including entropy and information gain.

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

In the final Term 1 project, you'll leverage your object detection abilities toward a video pipeline for vehicle detection and tracking.

Project Description - Vehicle Detection and Tracking

Project Rubric - Vehicle Detection and Tracking

Congratulations once more on completing term 1! Your diligent studying and newly honed skills have brought you one major step closer to graduating the program and getting into the field. Today, you begin Term 2, and we can't wait for you to jump in.

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

Learn from the best! 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.

In this lesson, you'll build a Kalman Filter in C++ that's capable of handling data from multiple sources. Why C++? Its performance enables the application of object tracking with a Kalman Filter in real-time.

In this project, you'll apply everything you've learned so far about Sensor Fusion by implementing an Extended Kalman Filter in C++!

Project Description - Extended Kalman Filters

Project Rubric - Extended Kalman Filters

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!

Put your skills to the test! Use C++ to code an Unscented Kalman Filter capable of tracking non-linear motion.

Project Description - Unscented Kalman Filters

Project Rubric - Unscented Kalman Filters

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

Sebastian Thrun will give you an overview of the theory behind localization!

In this lesson, you'll learn the math behind localization as well as how to implement Markov localization in C++.

Here you'll learn about vehicle movement and motion models to predict where your car will be at a future time.

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

Now that you understand how a particle filter works, you'll 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

In this lesson you'll learn about and how to use PID controllers with Sebastian!

Implement a PID Controller to race around the lake track.

Project Description - PID Controller

Project Rubric - PID Controller

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 project, you'll implement Model Predictive Control to drive the vehicle around the track even with additional latency between commands!

Project Description - Model Predictive Control

Project Rubric - Model Predictive Control

This optional content is designed to refresh knowledge of trigonometry and geometry, in support of Self Driving Car Engineer term 2 objectives.

Welcome to Term 3! This term includes path planning, an elective concentration, and system integration, which is the capstone of the program. You'll run your code on Udacity's own self-driving car!

In this lesson you will learn about discrete path planning and algorithms for solving the path planning problem.

In this lesson you'll learn how to use data from sensor fusion to generate predictions about the likely behavior of moving objects.

In this lesson you'll learn how to think about high level behavior planning in a self driving car.

In this lesson, you’ll 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 - Path Planning Project

Project Rubric - Path Planning Project

An overview of the elective options and how to proceed.

Students in this specialization, built with the NVIDIA Deep Learning Institute, will learn about semantic segmentation, and inference optimization, active areas of deep learning research.

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.

Train segmentation networks, which paint each pixel of the image a different color, based on its class. Use segmented images to find free space on the road.

Project Description - Semantic Segmentation

Project Rubric - Semantic Segmentation

If you choose the Functional Safety specialization, built with Elektrobit, you’ll learn functional safety frameworks to ensure that vehicles are safe, both at the system and component levels.

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.

It's time to start the functional safety project! You will be creating a safety case for a lane assistance system.

Project Description - Functional Safety of a Lane Assistance System

Project Rubric - Functional Safety of a Lane Assistance System

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

Obtain an architectural overview of the Robot Operating System 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. Harnessing these will be key to building shippable software using ROS.

ROS Nodes are a key abstraction that allows a robot system to be built modularly. In this lesson, you'll learn how to write them using Python.

Run your code on Carla, Udacity's 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 Self-Driving Car Engineer Nanodegree program! Read on to learn how to officially complete the program and graduate.

Learn how to search for jobs effectively through industry research, and targeting your application to a specific role.

Receive a personalized review of your resume. This resume review is best suited for applicants who have 0-3 years of work experience in any industry.

Project Description - Resume Review Project (Entry-level)

Project Rubric - Resume Review Project (Entry-level)

Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in an unrelated field.

Project Description - Resume Review Project (Career Change)

Project Rubric - Resume Review Project (Career Change)

Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in a related field.

Project Description - Resume Review Project (Prior Industry Experience)

Project Rubric - Resume Review Project (Prior Industry Experience)

Get a personalized review of your cover letter. A successful cover letter will convey your enthusiasm, specific technical qualifications, and communication skills applicable to the position.

Project Description - Craft Your Cover Letter

Project Rubric - Craft Your Cover Letter

In this lesson, learn how to tell your unique story in a succinct and professional way. Communicate to employers that you know how to solve problems, overcome challenges, and achieve results.

Optimize your LinkedIn profile to show up in recruiter searches, build your network, and attract employers. Learn to read your LinkedIn profile through the lens of a recruiter or hiring manager.

Project Description - LinkedIn Profile Review Project

Project Rubric - LinkedIn Profile Review Project

Update and personalize your Udacity Professional Profile as you complete your Nanodegree program, and make your Profile visible to Udacity hiring partners when you’re ready to start your job search.

Project Description - Udacity Professional Profile Review

Project Rubric - Udacity Professional Profile Review

Review how your GitHub profile, projects, and code represent you as a potential job candidate. Learn to assess your GitHub profile through the eyes of a recruiter or hiring manager.

Project Description - Optimize Your GitHub Profile

Project Rubric - Optimize Your GitHub Profile

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.