University of Toronto
Self-Driving Cars Specialization
University of Toronto

Self-Driving Cars Specialization

Launch Your Career in Self-Driving Cars. Be at the forefront of the autonomous driving industry.

Taught in English

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Steven Waslander
Jonathan Kelly

Instructors: Steven Waslander

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.7

(2,623 reviews)

Advanced level
Designed for those already in the industry
3 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the detailed architecture and components of a self-driving car software stack

  • Implement methods for static and dynamic object detection, localization and mapping, behaviour and maneuver planning, and vehicle control

  • Use realistic vehicle physics, complete sensor suite: camera, LIDAR, GPS/INS, wheel odometry, depth map, semantic segmentation, object bounding boxes

  • Demonstrate skills in CARLA and build programs with Python

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.7

(2,623 reviews)

Advanced level
Designed for those already in the industry
3 months at 10 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 4 course series

Introduction to Self-Driving Cars

Course 135 hours4.7 (2,794 ratings)

What you'll learn

  • Understand commonly used hardware used for self-driving cars

  • Identify the main components of the self-driving software stack

  • Program vehicle modelling and control

  • Analyze the safety frameworks and current industry practices for vehicle development

State Estimation and Localization for Self-Driving Cars

Course 226 hours4.7 (809 ratings)

What you'll 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

Visual Perception for Self-Driving Cars

Course 331 hours4.7 (553 ratings)

What you'll learn

  • Work with the pinhole camera model, and perform intrinsic and extrinsic camera calibration

  • Detect, describe and match image features and design your own convolutional neural networks

  • Apply these methods to visual odometry, object detection and tracking

  • Apply semantic segmentation for drivable surface estimation

Motion Planning for Self-Driving Cars

Course 432 hours4.8 (453 ratings)

What you'll learn

Instructors

Steven Waslander
University of Toronto
4 Courses161,018 learners

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