Intro to Self-Driving Cars

Introduction

Welcome to the Intro to Self-Driving Cars Nanodegree program! We are excited to have you and hope you are looking forward to learning about this game-changing field!

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

Getting Help

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The Carla Chronicles: Back on Track

Work through the readiness assessment with Carla and her friends to make sure you are ready to begin your own personal adventure with self-driving cars!

Get Ready - Intro to Self-Driving Cars

While you wait for your classroom to open, refresh your math and programming skills with these helpful resources.

Introduction

A brief introduction to Bayesian Thinking from Sebastian.

Joy Ride

A quick introduction to controlling a (simulated) car with code. Parts 1 and 2 will show you how to control gas and steering and in part 3 you'll program a car to parallel park.

Probability

Learn the basics of probability - the language of robotics. This lesson will focus on the math. In later lessons you'll apply this math in Python code.

Conditional Probability

In order to infer meaning from noisy sensor measurements, a self driving car needs to use the math of Conditional Probability. Learn this math from Sebastian (and then apply it in the next lesson).

Programming Probability in Python

Your chance to learn basic Python syntax while applying what you learned about probability and conditional probability in the last two lessons.

Bayes' Rule

Learn about Bayes' Rule from Sebastian and get your first peek at how a self driving car uses Bayes' Rule to understand where in the world it is.

Programming Bayes' Rule and World Representations

In this lesson, you can expect a lot of hands-on practice programming Bayesian probability in Python, and representing a 2D world that you'll need to localize a car.

Probability Distributions

Learn how a robot represents it's belief about uncertain quantities using something known as a **probability distribution**.

Programming Probability Distributions

Apply what you've learned in this course by programming and visualizing probability distributions.

Gaussian Distributions

You will work with a specific continuous probability distribution called the Gaussian distribution. A Gaussian distribution helps describe uncertainty in sensor measurements and a vehicle's location.

Robot Localization

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

Histogram Filter in Python

Write the `sense` and `move` functions for a 2 dimensional histogram filter in Python.

Section Overview

An introduction to the amazing tools and algorithms you'll learn in this lesson.

Introduction to Kalman Filters

Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.

State and Object-Oriented Programming

In this lesson, students will learn about representing the state of a car in programming as classes and objects and mathematically as vectors that can be changed with linear algebra!

Matrices and Transformation of State

Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.

Implement Matrix Class

Practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed `Matrix` class.

C++ Getting Started

The differences between C++ and Python and how to write C++ code.

C++ Vectors

To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.

Practical C++

Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.

C++ Object Oriented Programming

Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.

Python and C++ Speed

In this lesson, we'll compare the execution times of C++ and Python programs.

Translate Python to C++

Apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.

C++ Intro to Optimization

Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.

C++ Optimization Practice

Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.

Project: Optimize Histogram Filter

Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!

How to Solve Problems

A systematic way of approaching and breaking down problems.

Data Structures

The list isn't the only structure for storing data! In this lesson you'll learn about sets, dictionaries and other Python data structures.

The Search Problem

When programming a car to drive itself you run into problems. Many of these are "search" problems. In this lesson you'll learn what search problems are and several algorithms for solving them.

Implement Route Planner

In this lesson you will actually implement a Google-maps style routing algorithm using A star search.

Odometers, Speedometers and Derivatives

Gain a conceptual understanding of the *derivative* and basic calculus by plotting points and finding slopes.

Accelerometers, Rate Gyros and Integrals

Learn how **integrals** can be used to calculate accumulated changes by finding the area under a curve.

Two Dimensional Robot Motion and Trigonometry

Learn the basics of trigonometry and how to decompose a self driving car's motion into X and Y components.

Reconstructing Trajectories from Sensor Data

Use raw acceleration, displacement, and angular rotation data from a vehicle's accelerometer, odometer, and rate gyros to reconstruct a vehicle's X, Y trajectory.

Computer Vision and Classification

Students will learn how to program an image classifier using computer vision techniques. Along the way you'll learn about machine learning, color transformation, feature extraction, and more!

Traffic Light Classifier

Build a classification pipeline that takes in an image of a traffic and outputs a label that classifies the image as a: red, green, or yellow traffic light.

Congratulations! You've finished!

Congratulations! You've reached the end of the Intro to Self-Driving Cars Nanodegree program!

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