About this Specialization
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 modeling 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 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).
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 modeling 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 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
You’ll learn from a highly realistic driving environment that features 3D pedestrian modeling 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.
Welcome
to Visual Perception for Self-Driving Cars, the third course in
University of Toronto’s Self-Driving Cars Specialization.
Welcome to Motion Planning for Self-Driving Cars, the fourth course at the University of Toronto’s Self-Driving Cars Specialization.
There are 4 Courses in this Specialization
Introduction to Self-Driving Car
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
This
the 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 designs 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
This
the 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
the shortest path over a graph or road network using Dijkstra's and the A*
an 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
the 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).
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