Self-Driving Cars Specialization

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Self-Driving Cars Specialization

Launch Your Career in Self-Driving Cars. Be at the forefront of the autonomous driving industry.

Instructors: Steven Waslander

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4 course series

Get in-depth knowledge of a subject

4.7

(2,745 reviews)

Advanced level

Designed for those already in the industry

3 months

at 10 hours a week

Flexible schedule

Learn at your own pace

4 course series

Get in-depth knowledge of a subject

4.7

(2,745 reviews)

Advanced level

Designed for those already in the industry

3 months

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Understand the detailed architecture and components of a self-driving car software stack
Implement methods for static and dynamic object detection, localization and mapping, behaviour and maneuver planning, and vehicle control
Use realistic vehicle physics, complete sensor suite: camera, LIDAR, GPS/INS, wheel odometry, depth map, semantic segmentation, object bounding boxes
Demonstrate skills in CARLA and build programs with Python

Skills you'll gain

Details to know

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Taught in English

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Specialization - 4 course series

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).

Applied Learning Project

Introduction to Self-Driving Cars

Course 135 hours

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

Skills you'll gain

Category: Control Systems

Category: Mathematical Modeling

Category: Software Architecture

Category: Automation

Category: Systems Architecture

Category: Hardware Architecture

Category: Risk Management Framework

Category: Computer Hardware

Category: Safety Assurance

Category: Simulations

Category: Mechanics

Category: Embedded Software

Category: Engineering Analysis

Category: Verification And Validation

State Estimation and Localization for Self-Driving Cars

Course 226 hours

What you'll learn

Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares
Develop a model for typical vehicle localization sensors, including GPS and IMUs
Apply extended and unscented Kalman Filters to a vehicle state estimation problem
Apply LIDAR scan matching and the Iterative Closest Point algorithm

Skills you'll gain

Category: Estimation

Category: Global Positioning Systems

Category: Mathematical Modeling

Category: Spatial Data Analysis

Category: Machine Learning Methods

Category: Computer Vision

Category: Linear Algebra

Visual Perception for Self-Driving Cars

Course 331 hours

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

Skills you'll gain

Category: Computer Vision

Category: Deep Learning

Category: Artificial Neural Networks

Category: Image Analysis

Category: Python Programming

Category: Visualization (Computer Graphics)

Category: Computer Graphics

Category: Artificial Intelligence and Machine Learning (AI/ML)

Category: Linear Algebra

Motion Planning for Self-Driving Cars

Course 432 hours

What you'll learn

Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization.

This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You'll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self-driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times. For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You'll face real-world randomness and need to work to ensure your solution is robust to changes in the environment. This is an intermediate course, intended for learners with some background in robotics, and it builds on the models and controllers devised in Course 1 of this specialization. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses) and calculus (ordinary differential equations, integration).

Skills you'll gain

Category: Data Structures

Category: Predictive Modeling

Category: Algorithms

Category: Computer Vision

Category: Spatial Data Analysis

Category: Scenario Testing

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Instructors

Steven Waslander

University of Toronto

4 Courses173,730 learners

Offered by

University of Toronto

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Frequently asked questions

Each course is intended to take 4-6 weeks, roughly one week per module. At this pace, the entire Specialization will take you 4-6 months to complete.

See the list of prerequisites provided in Module 0 of Course 1. The most important ones are familiarity with linear algebra, calculus, probability theory, and kinematic and dynamic modeling. Some exposure to computer vision, AI or robotics is also useful.

The courses are mostly independent and self-paced, so it is possible to mix the order of the courses based on your interests. The only exception is that Course 1 provides a valuable overview of an autonomous vehicle in terms of hardware and software, so we recommend starting with Course 1.

You will be able to develop basic implementations of all the main components of an autonomous car software stack, including localization and mapping solutions, object detection and drivable surface detection methods, motion planning approaches and vehicle controllers. You'll be ready to enter the industry with a strong overview of the core requirements and challenges in self-driving development, and you'll have experience with simulating these vehicles in the CARLA simulator.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.