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 Specialization
Launch Your Career in Self-Driving Cars. Be at the forefront of the autonomous driving industry.
Offered By
What you will 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
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 4 Courses in this Specialization
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - 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 For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software 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).
State Estimation and Localization for Self-Driving Cars
Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to: - 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 - Understand LIDAR scan matching and the Iterative Closest Point algorithm - Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks. You'll apply these methods to visual odometry, object detection and tracking, and semantic segmentation for drivable surface estimation. These techniques represent the main building blocks of the perception system for self-driving cars. For the final project in this course, you will develop algorithms that identify bounding boxes for objects in the scene, and define the boundaries of the drivable surface. You'll work with synthetic and real image data, and evaluate your performance on a realistic dataset. This is an advanced course, intended for learners with a background in computer vision and deep learning. 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).
Motion Planning for Self-Driving Cars
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).
Instructors
Steven Waslander
Associate Professor
Jonathan Kelly
Assistant ProfessorAbout University of Toronto
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
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.
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
Will I earn university credit for completing the Specialization?
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
How long does it take to complete the Specialization?
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.
What background knowledge is necessary?
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.
Do I need to take the courses in a specific order?
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.
What will I be able to do upon completing the Specialization?
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.
More questions? Visit the Learner Help Center.
