About this Course

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Advanced Level

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.

Approx. 27 hours to complete
English
Subtitles: English

What you will learn

  • Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares

  • Develop a model for typical vehicle localization sensors, including GPS and IMUs

  • Apply extended and unscented Kalman Filters to a vehicle state estimation problem

  • Apply LIDAR scan matching and the Iterative Closest Point algorithm

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level

This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.

Approx. 27 hours to complete
English
Subtitles: English

Offered by

University of Toronto logo

University of Toronto

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(1,256 ratings)Info
Week
1

Week 1

2 hours to complete

Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars

2 hours to complete
9 videos (Total 33 min), 3 readings
9 videos
Welcome to the Course3m
Meet the Instructor, Jonathan Kelly2m
Meet the Instructor, Steven Waslander5m
Meet Diana, Firmware Engineer2m
Meet Winston, Software Engineer3m
Meet Andy, Autonomous Systems Architect2m
Meet Paul Newman, Founder, Oxbotica & Professor at University of Oxford5m
The Importance of State Estimation1m
3 readings
Course Prerequisites: Knowledge, Hardware & Software15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
7 hours to complete

Module 1: Least Squares

7 hours to complete
4 videos (Total 33 min), 3 readings, 3 quizzes
4 videos
Lesson 1 (Part 2): Squared Error Criterion and the Method of Least Squares6m
Lesson 2: Recursive Least Squares7m
Lesson 3: Least Squares and the Method of Maximum Likelihood8m
3 readings
Lesson 1 Supplementary Reading: The Squared Error Criterion and the Method of Least Squares45m
Lesson 2 Supplementary Reading: Recursive Least Squares30m
Lesson 3 Supplementary Reading: Least Squares and the Method of Maximum Likelihood30m
3 practice exercises
Lesson 1: Practice Quiz30m
Lesson 2: Practice Quiz30m
Module 1: Graded Quiz50m
Week
2

Week 2

7 hours to complete

Module 2: State Estimation - Linear and Nonlinear Kalman Filters

7 hours to complete
6 videos (Total 53 min), 5 readings, 1 quiz
6 videos
Lesson 2: Kalman Filter and The Bias BLUEs5m
Lesson 3: Going Nonlinear - The Extended Kalman Filter9m
Lesson 4: An Improved EKF - The Error State Extended Kalman Filter6m
Lesson 5: Limitations of the EKF7m
Lesson 6: An Alternative to the EKF - The Unscented Kalman Filter15m
5 readings
Lesson 1 Supplementary Reading: The Linear Kalman Filter45m
Lesson 2 Supplementary Reading: The Kalman Filter - The Bias BLUEs10m
Lesson 3 Supplementary Reading: Going Nonlinear - The Extended Kalman Filter45m
Lesson 4 Supplementary Reading: An Improved EKF - The Error State Kalman FIlter1h
Lesson 6 Supplementary Reading: An Alternative to the EKF - The Unscented Kalman Filter30m
Week
3

Week 3

2 hours to complete

Module 3: GNSS/INS Sensing for Pose Estimation

2 hours to complete
4 videos (Total 34 min), 3 readings, 1 quiz
4 videos
Lesson 2: The Inertial Measurement Unit (IMU)10m
Lesson 3: The Global Navigation Satellite Systems (GNSS)8m
Why Sensor Fusion?3m
3 readings
Lesson 1 Supplementary Reading: 3D Geometry and Reference Frames10m
Lesson 2 Supplementary Reading: The Inertial Measurement Unit (IMU)30m
Lesson 3 Supplementary Reading: The Global Navigation Satellite System (GNSS)15m
1 practice exercise
Module 3: Graded Quiz50m
Week
4

Week 4

2 hours to complete

Module 4: LIDAR Sensing

2 hours to complete
4 videos (Total 48 min), 3 readings, 1 quiz
4 videos
Lesson 2: LIDAR Sensor Models and Point Clouds12m
Lesson 3: Pose Estimation from LIDAR Data17m
Optimizing State Estimation3m
3 readings
Lesson 1 Supplementary Reading: Light Detection and Ranging Sensors10m
Lesson 2 Supplementary Reading: LIDAR Sensor Models and Point Clouds10m
Lesson 3 Supplementary Reading: Pose Estimation from LIDAR Data30m
1 practice exercise
Module 4: Graded Quiz30m

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About the Self-Driving Cars Specialization

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
Self-Driving Cars

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