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. 26 hours to complete
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

Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs External Link
Advanced Level

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

Approx. 26 hours to complete
English

Offered by

Placeholder

University of Toronto

Syllabus - What you will learn from this course

Content RatingThumbs Up95%(2,600 ratings)
Week1
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
7 hours to complete

Module 1: Least Squares

7 hours to complete
4 videos (Total 33 min), 3 readings, 3 quizzes
Week2
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
Week3
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
Week4
Week 4
2 hours to complete

Module 4: LIDAR Sensing

2 hours to complete
4 videos (Total 48 min), 3 readings, 1 quiz

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About the Self-Driving Cars Specialization

Self-Driving Cars

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