University of Toronto
Visual Perception for Self-Driving Cars
University of Toronto

Visual Perception for Self-Driving Cars

This course is part of Self-Driving Cars Specialization

Steven Waslander
Jonathan Kelly

Instructors: Steven Waslander

41,480 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(556 reviews)

|

96%

Advanced level

Recommended experience

31 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

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Assessments

4 quizzes

Taught in English

Course

Gain insight into a topic and learn the fundamentals

4.7

(556 reviews)

|

96%

Advanced level

Recommended experience

31 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Self-Driving Cars Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 7 modules in this course

This module introduces the main concepts from the broad and exciting 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.

What's included

4 videos4 readings1 discussion prompt

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.

What's included

6 videos4 readings1 quiz1 programming assignment2 ungraded labs

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.

What's included

6 videos5 readings1 programming assignment1 ungraded lab

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.

What's included

6 videos6 readings1 quiz

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.

What's included

4 videos4 readings1 quiz

The second most prevalent application of deep neural networks to self-driving is semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. The main use for segmentation is to identify the drivable surface, which aids in ground plane estimation, object detection and lane boundary assessment. Segmentation labels are also being directly integrated into object detection as pixel masks, for static objects such as signs, lights and lanes, and moving objects such cars, trucks, bicycles and pedestrians.

What's included

3 videos3 readings1 quiz

The final module of this course focuses on the implementation of a collision warning system that alerts a self-driving car about the position and category of obstacles present in their lane. The project is comprised of three major segments: 1) Estimating the drivable space in 3D, 2) Semantic Lane Estimation and 3) Filter wrong output from object detection using semantic segmentation.

What's included

4 videos1 programming assignment1 discussion prompt1 ungraded lab

Instructors

Instructor ratings
4.7 (72 ratings)
Steven Waslander
University of Toronto
4 Courses163,081 learners
Jonathan Kelly
University of Toronto
4 Courses163,081 learners

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