Self-Driving Car Engineer v8.0.5

Nanodegree key: nd013

Version: 8.0.5

Locale: en-us 求知若渴，虚心若愚。

Work on the future of autonomous vehicles and help make the self-driving car revolution a reality!

Content

Part 01 : Computer Vision

In the part 01 ~ 03, you'll first become an expert in applying Computer Vision and Deep Learning on automotive problems. You will teach the car to detect lane lines, predict steering angle, and more all based on just camera data, along with working with lidar and radar data later on.

Module 01: Introduction
- Lesson 01: Introduction
  
  Welcome! Get an introduction to the program, discover the services we provide, and get a sneak-peek at the incredible projects you'll build!
- Lesson 02: Image Representation & Classification
  
  Learn how images are represented numerically and implement image processing techniques, such as color masking and binary classification.
- Lesson 03: Convolutional Filters and Edge Detection
  
  Learn about frequency in images and implement your own image filters for detecting edges and shapes in an image. Use a computer vision library to perform face detection.
- Lesson 04: Types of Features & Image Segmentation
  
  Program a corner detector and learn techniques, like k-means clustering, for segmenting an image into unique parts.
- Lesson 05: Feature Vectors
  
  Learn how to describe objects and images using feature vectors.
- Lesson 06: CNN Layers and Feature Visualization
  
  Define and train your own convolution neural network for clothing recognition. Use feature visualization techniques to see what a network has learned.
- Lesson 07: Project: Facial Keypoint Detection
  
  Apply your knowledge of image processing and deep learning to create a CNN for facial keypoint (eyes, mouth, nose, etc.) detection.
  
  Project Description - Facial Keypoint Detection
  
  Project Rubric - Facial Keypoint Detection
- Lesson 08: Jobs in Computer Vision
  
  Learn about common jobs in computer vision, and get tips on how to stay active in the community.

Module 02: Computer Vision

Lesson 01: Computer Vision Fundamentals

Get a taste of some basic computer vision techniques to find lane markings on the road.
Lesson 02: Project: Finding Lane Lines

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
Lesson 03: Camera Calibration

Learn how to calibrate your camera to remove inherent distortions that can affect its perception of the world.
Lesson 04: Gradients and Color Spaces

Learn how to use gradient thresholds and different color spaces to more easily identify lane markings on the road.
Lesson 05: Advanced Computer Vision

Discover more advanced computer vision techniques to improve upon your lane lines algorithm!
Lesson 06: Project: Advanced Lane Finding

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

Part 02 : Deep Learning

Module 01: Deep Learning
Module 02: GitHub Lesson
- Lesson 01: GitHub
  
  Build a GitHub Profile on par with senior software engineers.

Part 03 : Sensor Fusion

Module 01: Sensor Fusion
- Lesson 01: Sensors
  
  Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
- Lesson 02: Kalman Filters
  
  Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
- Lesson 03: C++ Checkpoint
  
  Are you ready to build Kalman Filters with C++? Take these quizzes to find out!
- Lesson 04: Geometry and Trigonometry Refresher
  
  This optional content is designed to refresh knowledge of trigonometry and geometry in support of Term 1 objectives.
- Lesson 05: Extended Kalman Filters
  
  Build a Kalman Filter in C++ that's capable of handling data from multiple sources.
- Lesson 06: Extended Kalman Filters Project
  
  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

Part 04 : Localization

In the part 04 ~ 07, you'll expand on your sensor knowledge to localize and control the vehicle. You'll evaluate sensor data from camera, radar, lidar, and GPS, and use these in closed-loop controllers that actuate the vehicle, finishing by combining all your skills on a real self-driving car!

Module 01: Localization

**Part 08 (Elective) :** Additional Content

Find additional optional content on Unscented Kalman Filters, Model Predictive Control, Advanced Deep Learning / Semantic Segmentation, and Functional Safety here.

Module 01: Vehicle Detection and Tracking
- Lesson 01: Object Detection
  
  In this lesson, you'll learn how to detect and track vehicles using color and gradient features and a support vector machine classifier.
Module 02: Unscented Kalman Filters
- Lesson 01: Unscented 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!
Module 03: Model Predictive Control
- Lesson 01: Vehicle Models
  
  In this lesson, you'll learn about kinematic and dynamic vehicle models. We'll use these later with Model Predictive Control.
- Lesson 02: 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!
Module 04: Advanced Deep Learning
- Lesson 01: Fully Convolutional Networks
  
  In this lesson you'll learn the motivation for Fully Convolutional Networks and how they are structured.
- Lesson 02: Scene Understanding
  
  In this lesson you'll be introduced to the problem of Scene Understanding and the role FCNs play.
- Lesson 03: Inference Performance
  
  In this lesson you'll become familiar with various optimizations in an effort to squeeze every last bit of performance at inference.
Module 05: Functional Safety
- Lesson 01: Introduction to Functional Safety
  
  You will learn to make safer vehicles using risk evaluation and systems engineering.
- Lesson 02: Functional Safety: Safety Plan
  
  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.
- Lesson 03: Functional Safety: Hazard Analysis and Risk Assessment
  
  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.
- Lesson 04: Functional Safety: Functional Safety Concept
  
  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.
- Lesson 05: Functional Safety: Technical Safety Concept
  
  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.
- Lesson 06: Functional Safety at the Software and Hardware Levels
  
  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.

**Part 09 (Elective) :** Autonomous Systems Interview

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!

Module 01: Autonomous Systems Interview
- Lesson 01: Autonomous Systems Interview Practice
  
  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

**Part 10 (Elective) :** Technical Interview Prep

Learn the skills technical interviewers expect you to know—efficiency, common algorithms, manipulating popular data structures, and how to explain a solution.

Module 01: Technical Interview Prep
- Lesson 01: Introduction and Efficiency
  
  Begin the section on data structures and algorithms, including Python and efficiency practice.
- Lesson 02: List-Based Collections
  
  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.
- Lesson 03: Searching and Sorting
  
  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.
- Lesson 04: Maps and Hashing
  
  Understand the concepts of sets, maps (dictionaries), and hashing. Examine common problems and approaches to hashing, and practice with examples.
- Lesson 05: Trees
  
  Learn the concepts and terminology associated with tree data structures. Investigate tree types, such as binary search trees, heaps, and self-balancing trees.
- Lesson 06: Graphs
  
  Examine the theoretical concept of a graph and understand common graph terms, coded representations, properties, traversals, and paths.
- Lesson 07: Case Studies in Algorithms
  
  Explore famous computer science problems, specifically the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem.
- Lesson 08: Technical Interviewing Techniques
  
  Learn about the “algorithm” for answering common technical interviewing questions. Practice and get tips for giving interviewers what they’re looking for.
Module 02: Behavioral Questions
- Lesson 01: Practice Behavioral Questions
  
  Practice answering behavioral questions and evaluate sample responses.
- Lesson 02: Land a Job Offer
  
  You've practiced a lot for the interview by now. Continue practicing, and you'll ace the interview!
  - Concept 01: Land a Job Offer

Part 11 (Elective): C++ Basics

This course is the first step in a rewarding journey towards C++ expertise. The goal is translation: get a program written in Python, and translate it into C++.

Module 01: C++ Basics
- Lesson 01: C++ Getting Started
  
  The differences between C++ and Python and how to write C++ code.
- Lesson 02: 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.
- Lesson 03: 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.
- Lesson 04: 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.
- Lesson 05: Python and C++ Speed
  
  In this lesson, we'll compare the execution times of C++ and Python programs.
- Lesson 06: Translate Python to C++
  
  Apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.
  
  Project Description - Translate Python to C++
  
  Project Rubric - Translate Python to C++

Content

Part 01 : Computer Vision

Part 02 : Deep Learning

Part 03 : Sensor Fusion

Part 04 : Localization

Part 05 : Path Planning

Part 06 : Control

Part 07 : System Integration

Part 08 (Elective) : Additional Content

Part 09 (Elective) : Autonomous Systems Interview

Part 10 (Elective) : Technical Interview Prep

Part 11 (Elective): C++ Basics

**Part 08 (Elective) :** Additional Content

**Part 09 (Elective) :** Autonomous Systems Interview

**Part 10 (Elective) :** Technical Interview Prep