• Anytime
    Application Deadline
  • 1 month
    Duration
This State Estimation and Localization for Self-Driving Cars offered by Coursera in partnership with University of Toronto is part of the Self-Driving Cars Specialization.
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Overview

Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. 

This State Estimation and Localization for Self-Driving Cars offered by Coursera in partnership with University of Toronto will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. 

By the end of this course, you will be able to:

  • 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
  • Understand LIDAR scan matching and the Iterative Closest Point algorithm 
  • Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car 

For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. 

Detailed Programme Facts

  • Deadline and start date A student can apply at any time for this programme, there is no deadline.
  • Programme intensity Part-time
    • Average part-time duration 1 months
    • Duration description
      • Approx. 25 hours to complete
      • Suggested: 4 weeks of study, 5-6 hours per week
  • Languages
    • English
  • Delivery mode
    Online
  • More information Go to the programme website

Programme Structure

Courses include:

  • Welcome to Course: State Estimation and Localization for Self-Driving Cars
  • Least Squares
  • State Estimation - Linear and Nonlinear Kalman Filters
  • GNSS/INS Sensing for Pose Estimation
  • LIDAR Sensing
  • Putting It together - An Autonomous Vehicle State Estimator

Details on Coursera Plus:

  • Learn Anything: Explore any interest or trending topic, take prerequisites, and advance your skills
  • Save money: Spend less money on your learning if you plan to take multiple courses this year
  • Flexible Learning: Learn at your own pace, move between multiple courses, or switch to a different course
  • Unlimited Certificates: Earn a certificate for every learning program that you complete at no additional cost

English Language Requirements

This programme may require students to demonstrate proficiency in English.

General Requirements

Advanced Level

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

Tuition Fee

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Visit Programme Website

Funding

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

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