University of Toronto

Introduction to Self-Driving Cars

This course is part of Self-Driving Cars Specialization

Taught in English

Some content may not be translated

Steven Waslander
Jonathan Kelly

Instructors: Steven Waslander

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Course

Gain insight into a topic and learn the fundamentals

4.7

(2,793 reviews)

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96%

Advanced level

Recommended experience

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

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

Details to know

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Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(2,793 reviews)

|

96%

Advanced level

Recommended experience

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

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

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This course is part of the Self-Driving Cars Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 8 modules in this course

This module will introduce you to the main concepts and layout of the specialization and discusses the major advances made in the field over the last two decades, highlighting the most recent progress made by major players in terms of safety and performance metrics, where available.

What's included

10 videos4 readings1 discussion prompt

Self-driving cars present an extremely rich and inter-disciplinary problem. This module introduces the language and structure of the problem definition, defining the most salient elements of the driving task and the driving environment.

What's included

4 videos3 readings3 quizzes

System architectures for self-driving vehicles are extremely diverse, as no standardized solution has yet emerged. This module describes both the hardware and software architectures commonly used and some of the tradeoffs in terms of cost, reliability, performance and complexity that constrain autonomous vehicle design.

What's included

5 videos4 readings1 quiz

As the self-driving domain matures, the requirement for safety assurance on public roads become more critical to self-driving developers. You will evaluate the challenges and approaches employed to date to tackle the immense challenge of assuring the safe operation of autonomous vehicles in an uncontrolled public road driving environment.

What's included

8 videos4 readings1 quiz

The first task for automating an driverless vehicle is to define a model for how the vehicle moves given steering, throttle and brake commands. This module progresses through a sequence of increasing fidelity physics-based models that are used to design vehicle controllers and motion planners that adhere to the limits of vehicle capabilities.

What's included

8 videos7 readings2 programming assignments2 ungraded labs

Longitudinal control of an autonomous vehicle involves tracking a speed profile along a fixed path, and can be achieved with reasonable accuracy using classic control techniques. This week, you will learn how to develop a baseline controller that is applicable for a significant subset of driving conditions, which include most non-evasive or highly-dynamic motions.

What's included

4 videos3 readings1 quiz

This week, you will learn about how lateral vehicle control ensures that a fixed path through the environment is tracked accurately. You will see how to define geometry of the path following control problem and develop both a simple geometric control and a dynamic model predictive control approach.

What's included

4 videos4 readings1 quiz

For the last week of the course, now you will get hands on with a simulation of an autonomous vehicle that requires longitudinal and lateral vehicle control design to track a predefined path along a racetrack with a given speed profile. You are encouraged to modify the speed profile and/or path to improve their lap time, without any requirement to do so. Work and play!

What's included

4 videos2 readings1 programming assignment1 discussion prompt

Instructors

Instructor ratings
4.8 (645 ratings)
Steven Waslander
University of Toronto
4 Courses160,614 learners
Jonathan Kelly
University of Toronto
4 Courses160,614 learners

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