About this Course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 19 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 19 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Welcome to Course 3: Visual Perception for Self-Driving Cars

This module introduces the main concepts from the broad field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations....
4 videos (Total 18 min), 3 readings
4 videos
Welcome to the course4m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
3 readings
CARLA Installation Guide45m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
3 hours to complete

Module 1: Basics of 3D Computer Vision

...
5 videos (Total 37 min), 1 quiz
5 videos
Camera Projective Geometry8m
Camera Calibration7m
Visual Depth Perception - Stereopsis7m
Image Filtering7m
Week
2
6 hours to complete

Module 2: Visual Features - Detection, Description and Matching

Visual features are used to track motion through an environment and to recognize places in a map. This module describes how features can be detected and tracked through a sequence of images and fused with other sources for localization as described in Course 2. Feature extraction is also fundamental to object detection and semantic segmentation in deep networks, and this module introduces some of the feature detection methods employed in that context as well....
6 videos (Total 44 min), 1 quiz
6 videos
Feature Descriptors6m
Feature Matching7m
Feature Matching: Handling Ambiguity in Matching5m
Outlier Rejection8m
Visual Odometry9m
Week
3
1 hour to complete

Module 3: Feedforward Neural Networks

Deep learning is a core enabling technology for self-driving perception. This module briefly introduces the core concepts employed in modern convolutional neural networks, with an emphasis on methods that have been proven to be effective for tasks such as object detection and semantic segmentation. Basic network architectures, common components and helpful tools for constructing and training networks are described....
6 videos (Total 58 min), 1 quiz
6 videos
Output Layers and Loss Functions10m
Neural Network Training with Gradient Descent10m
Data Splits and Neural Network Performance Evaluation8m
Neural Network Regularization9m
Convolutional Neural Networks9m
1 practice exercise
Feed-Forward Neural Networks30m
Week
4
1 hour to complete

Module 4: 2D Object Detection

The two most prevalent applications of deep neural networks to self-driving are object detection, including pedestrian, cyclists and vehicles, and semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. This module presents baseline techniques for object detection and the following module introduce semantic segmentation, both of which can be used to create a complete self-driving car perception pipeline....
4 videos (Total 52 min), 1 quiz
4 videos
2D Object detection with Convolutional Neural Networks11m
Training vs. Inference11m
Using 2D Object Detectors for Self-Driving Cars14m
1 practice exercise
Object Detection For Self-Driving Cars30m

Instructor

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

About University of Toronto

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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