About this Course

81,658 recent views
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 computer vision and deep learning.

Approx. 31 hours to complete
English
Subtitles: English

What you will 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

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 computer vision and deep learning.

Approx. 31 hours to complete
English
Subtitles: English

Instructor

Offered by

University of Toronto logo

University of Toronto

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

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

2 hours to complete
4 videos (Total 18 min), 4 readings
4 videos
Welcome to the course4m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
4 readings
Course Prerequisites15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
Recommended Textbooks15m
7 hours to complete

Module 1: Basics of 3D Computer Vision

7 hours to complete
6 videos (Total 43 min), 4 readings, 2 quizzes
6 videos
Lesson 1 Part 2: Camera Projective Geometry8m
Lesson 2: Camera Calibration7m
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7m
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5m
Lesson 4: Image Filtering7m
4 readings
Supplementary Reading: The Camera Sensor30m
Supplementary Reading: Camera Calibration15m
Supplementary Reading: Visual Depth Perception30m
Supplementary Reading: Image Filtering15m
1 practice exercise
Module 1 Graded Quiz30m
Week
2

Week 2

7 hours to complete

Module 2: Visual Features - Detection, Description and Matching

7 hours to complete
6 videos (Total 44 min), 5 readings, 1 quiz
6 videos
Lesson 2: Feature Descriptors6m
Lesson 3 Part 1: Feature Matching7m
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5m
Lesson 4: Outlier Rejection8m
Lesson 5: Visual Odometry9m
5 readings
Supplementary Reading: Feature Detectors and Descriptors30m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Outlier Rejection15m
Supplementary Reading: Visual Odometry10m
Week
3

Week 3

3 hours to complete

Module 3: Feedforward Neural Networks

3 hours to complete
6 videos (Total 58 min), 6 readings, 1 quiz
6 videos
Lesson 2: Output Layers and Loss Functions10m
Lesson 3: Neural Network Training with Gradient Descent10m
Lesson 4: Data Splits and Neural Network Performance Evaluation8m
Lesson 5: Neural Network Regularization9m
Lesson 6: Convolutional Neural Networks9m
6 readings
Supplementary Reading: Feed-Forward Neural Networks15m
Supplementary Reading: Output Layers and Loss Functions15m
Supplementary Reading: Neural Network Training with Gradient Descent15m
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10m
Supplementary Reading: Neural Network Regularization15m
Supplementary Reading: Convolutional Neural Networks10m
1 practice exercise
Feed-Forward Neural Networks30m
Week
4

Week 4

3 hours to complete

Module 4: 2D Object Detection

3 hours to complete
4 videos (Total 52 min), 4 readings, 1 quiz
4 videos
Lesson 2: 2D Object detection with Convolutional Neural Networks11m
Lesson 3: Training vs. Inference11m
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14m
4 readings
Supplementary Reading: The Object Detection Problem15m
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30m
Supplementary Reading: Training vs. Inference45m
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30m
1 practice exercise
Object Detection For Self-Driving Cars30m

Reviews

TOP REVIEWS FROM VISUAL PERCEPTION FOR SELF-DRIVING CARS

View all reviews

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

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, 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. Learn more.

More questions? Visit the Learner Help Center.