About this Course

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Learner Career Outcomes

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67%

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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

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

Learner Career Outcomes

40%

got a tangible career benefit from this course

67%

got a pay increase or promotion
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

Offered by

Placeholder

University of Toronto

Syllabus - What you will learn from this course

Content RatingThumbs Up95%(2,011 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
7 hours to complete

Module 1: Least Squares

7 hours to complete
4 videos (Total 33 min), 3 readings, 3 quizzes
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
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
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

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About the Self-Driving Cars Specialization

Self-Driving Cars

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