Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Visual Perception for Self-Driving Cars certification course will introduce candidates to the key perception tasks and survey popular computer vision methods for robotic perception in autonomous driving, and dynamic and static object detection. Candidates will acquire the skills to work with the pinhole camera model by the end of this course. Along with them they will detect, explain and fit image characteristics, conduct intrinsic and extrinsic calibration of the camera and build their own convolutional neural networks. For drivable surfaces, estimation candidates can apply these methods to object detection and tracking, visual odometry, and semantic segmentation.

Candidates will build algorithms for the final project in the Visual Perception for Self-Driving Cars training course that defines the limits of the drivable surface and recognise bounding boxes for objects in the scene. On a realistic dataset, students learn to work using synthetic as well as real image data.

The Visual Perception for Self-Driving Cars online course is part of a self-driving car specialisation programme. It is the third one of a total 4 courses. This specialisation gives a detailed understanding of state-of-the-art engineering approaches used in the self-driving automotive industry.

The Highlights

Advanced level course
3rd course of self-driving car specialisation
Learn with own schedule
Flexible deadlines
Approximately 31 hours to finish
Certification by Coursera
Offered by University of Toronto

Programme Offerings

online
Quiz
Assignment
Practice Exercises

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The Visual Perception for Self-Driving Cars certification fee is chargeable on a monthly basis. The details of the breakup are given below

Visual Perception for Self-Driving Cars Fee Structure

Description	Amount
1 month, 20 hours per week	Rs. 6,486
3 months, 12 hours a week	Rs. 12,973
6 months, 6 hours a week	Rs. 19,459

Eligibility Criteria

Education

Candidates should have basic knowledge of deep learning, computer vision, linear algebra, and Python 3.0

Certification Qualifying Details

Certification will be done only after the successful completion of the Visual Perception for Self-Driving Cars certification by Coursera. To get full access to the programme and to get a certificate of completion candidates should subscribe to it by paying the required fee.

What you will learn

Robotic skills

After the completion of the Visual Perception for Self-Driving Cars certification syllabus, candidates will learn these:

Candidates will be working with models of pinhole cameras and performing intrinsic as well as extrinsic camera calibration. Candidates will detect, explain and fit image characteristics, conduct intrinsic and extrinsic calibration of the camera and build their own convolutional neural networks. And they will learn the skills to apply these methods to object detection and tracking, visual odometry and semantic segmentation for drivable surface estimation.
Using the open source simulator ‘CARLA’ candidates can get to communicate with actual data sets from an autonomous vehicle (AV)through hands-on projects.
Candidates can hear from industry experts working at companies such as Oxbotica and Zoox during their courses as they share insights into autonomous technology and how it fuels job growth in the field.
Candidates can benefit from a highly realistic driving experience that features 3D modelling and environmental conditions for pedestrians.
They will be able to create their own self-driving software stack and apply for jobs in the autonomous vehicle industry.

Who it is for

Car Designer

Admission Details

Candidates applying for the Visual Perception for Self-Driving Cars classes, follow these steps:

Step 1: Visit the website.

https://www.coursera.org/learn/visual-perception-self-driving-cars?action=enroll&authMode=signup#about

Step 2: Click on “Enrol For Free”.

Step 3: Candidates will be requested to sign up or log in. Do sign up or log in with your Google/ Facebook/ Apple account.

Step 4: Candidates can audit the course for free or subscribe to the whole specialisation for getting a certificate. Select the option accordingly.

Step 5: Candidates will get a 7-day free trial. Payment has to be done only after 7 days. You can cancel the free trial anytime you want. No penalties will be charged.

The Syllabus

Videos

Welcome to the Self-Driving Cars Specialization!
Welcome to the course
Meet the Instructor, Steven Waslander
Meet the Instructor, Jonathan Kelly

Readings

Course Prerequisites
How to Use Discussion Forums
How to Use Supplementary Readings in This Course
Recommended Textbooks

Videos

Lesson 1 Part 1: The Camera Sensor
Lesson 1 Part 2: Camera Projective Geometry
Lesson 2: Camera Calibration
Lesson 3 Part 1: Visual Depth Perception - Stereopsis
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity
Lesson 4: Image Filtering

Readings

Supplementary Reading: The Camera Sensor
Supplementary Reading: Camera Calibration
Supplementary Reading: Visual Depth Perception
Supplementary Reading: Image Filtering

Practice Exercise

Module 1 Graded Quiz

Videos

Lesson 1: Introduction to Image features and Feature Detectors
Lesson 2: Feature Descriptors
Lesson 3 Part 1: Feature Matching
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching
Lesson 4: Outlier Rejection
Lesson 5: Visual Odometry

Readings

Supplementary Reading: Feature Detectors and Descriptors
Supplementary Reading: Feature Matching
Supplementary Reading: Feature Matching
Supplementary Reading: Outlier Rejection
Supplementary Reading: Visual Odometry

Videos

Lesson 1: Feed Forward Neural Networks
Lesson 2: Output Layers and Loss Functions
Lesson 3: Neural Network Training with Gradient Descent
Lesson 4: Data Splits and Neural Network Performance Evaluation
Lesson 5: Neural Network Regularizatio
Lesson 6: Convolutional Neural Networks

Readings

Supplementary Reading: Feed-Forward Neural Networks
Supplementary Reading: Output Layers and Loss Functions
Supplementary Reading: Neural Network Training with Gradient Descent
Supplementary Reading: Data Splits and Neural Network Performance Evaluation
Supplementary Reading: Neural Network Regularization
Supplementary Reading: Convolutional Neural Networks

Practice Exercise

Feed-Forward Neural Networks

Videos

Lesson 1: The Object Detection Problem
Lesson 2: 2D Object detection with Convolutional Neural Networks
Lesson 3: Training vs. InferenceLesson 4
Using 2D Object Detectors for Self-Driving Cars

Readings

Supplementary Reading: The Object Detection Problem
Supplementary Reading: 2D Object detection with Convolutional Neural Networks
Supplementary Reading: Training vs. Inference
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars

Practice Exercise

Object Detection For Self-Driving Cars

Videos

Lesson 1: The Semantic Segmentation Problem
Lesson 2: ConvNets for Semantic Segmentation
Lesson 3: Semantic Segmentation for Road Scene Understanding

Readings

Supplementary Reading: The Semantic Segmentation Problem
Supplementary Reading: ConvNets for Semantic Segmentation
Supplementary Reading: Semantic Segmentation for Road Scene Understanding

Practice Exercise

Semantic Segmentation For Self-Driving Cars

Videos

Project Overview: Using CARLA for object detection and segmentation5m
Final Project Hints6m
Final Project Solution [LOCKED]
Congratulations for completing the course!

Instructors

University of Toronto, Toronto Frequently Asked Questions (FAQ's)

1: Is university credit provided to candidates?

This course doesn’t come with university credit. Certification is done by Coursera itself.

2: What are the benefits of the subscription plan?

Candidates opting for a subscription plan have unlimited access to the course. They will get access to all contents including assignments and will get a certificate after the completion.

3: Can I learn this course for free?

A free audit option is available. Candidates will have access to all the course contents. Contents like assignments are not included in it. There will not be certification after completion also.

4: When will I get access to the content of the course?

Access will be enabled after the enrolment and it will vary according to the way of enrolment. Only subscribed candidates will gamer access to all the contents.

5: Will I get a scholarship for studying the Visual Perception for Self-Driving Cars online course?

Coursera provides financial aid. If candidates can’t afford the fee, do apply for availing financial aid. It will take 15 days at least to complete the review process.