Online Course – Certified Professional Internship in Autonomous Vehicles from the University of Toronto

Start your career in autonomous vehicles. Be at the forefront of the autonomous driving industry.

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קורס אונליין - התמחות מקצועית מוסמכת ברכבים אוטונומיים של אוניברסיטת טורונטו

Professional Certificate

Advanced

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Time management skills
  • Interpersonal communication
  • Creative thinking
  • Troubleshooting
  • Teamwork
  • leadership
  • Analytical skills
  • project management
  • Ability to multitask
  • Continuous learning

What you will learn in the course

Courses for which the course is suitable

  • Autonomous Vehicle Engineer
  • Autonomous vehicle software developer
  • Autonomous Vehicle Data Analyst
  • Autonomous Vehicle Simulation Engineer
  • Autonomous Technologies Expert
  • Artificial intelligence systems developer for autonomous vehicles
  • Autonomous Vehicle Algorithm Engineer
  • Project Manager in the Autonomous Vehicle Field
  • Autonomous vehicle researcher

Internship – Series of 4 courses

Be at the forefront of the autonomous vehicle industry. With market researchers predicting a $42 billion market and more than 20 million autonomous vehicles on the road by 2025, the next big job boom is just around the corner.

What will you get from the internship?

  • Comprehensive understanding of advanced engineering methods in the autonomous vehicle industry.
  • Working with real datasets from autonomous vehicles (AV) through practical projects.
  • Using the open source CARLA simulator.

Industry experts

During your courses, you will hear from industry experts who work at companies such as:

  • Oxbotica
  • Zoox

Experts will share insights on autonomous technologies and how it contributes to job growth in the field.

Realistic driving environment

You will learn in a very realistic driving environment that includes:

  • 3D pedestrian models.
  • Environmental conditions.

When you successfully complete the internship, you will be able to build an integrated software package for an autonomous vehicle and will be ready to apply for jobs in the autonomous vehicle industry.

Prerequisites

It is recommended that you have some background in:

  • Linear algebra
  • probability
  • statistics
  • Infinitesimal calculus
  • Physics
  • Control theory
  • Programming in Python

You will need these requirements to effectively run the CARLA simulator:

  • Windows 7 64-bit version (or later) or Ubuntu 16.04 (or later).
  • Intel or AMD 4-core processor (2.5 GHz or faster).
  • NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD graphics card or better.
  • 8GB RAM.
  • OpenGL 3 or higher (for Linux computers).

Hands-on Learning Project

You will learn in a highly realistic driving environment that includes 3D models of pedestrians and environmental conditions. Upon successful completion of the internship, you will be able to build an integrated software package for an autonomous vehicle and be prepared to apply for jobs in the autonomous vehicle industry.

Details of the courses that make up the specialization

Introduction to autonomous vehicles

Course 1

  • 35 hours
  • 4.7 (2,838 ratings)

Course Details

What you’ll learn:
  • Understand the common hardware used in autonomous vehicles
  • Identify the key components in the autonomous vehicle software suite
  • Program vehicle models and control
  • Analyze current safety frameworks and practices in the automotive development industry

Situation and location estimation for autonomous vehicles

Course 2

  • 26 hours
  • 4.7 (822 ratings)

Course Details

What you’ll learn:
  • Understand the key methods for estimating parameters and situations used in autonomous driving, such as the error minimization method
  • Develop a model for typical vehicle location sensors, including GPS and IMUs
  • Use extended, non-sparse Kalman filters to solve vehicle state estimation problems
  • Apply LIDAR scan matching techniques and the iterative closest point algorithm

Visual perception for autonomous vehicles

Course 3

  • 31 hours
  • 4.7 (571 ratings)

Course Details

What you’ll learn:
  • Work with a corner camera model, and perform internal and external calibration of the camera
  • Discover, describe, and match image features and design your own convolutional neural networks
  • Use these methods in visual odometry, object recognition and tracking
  • Apply semantic slicing to estimate drivable surfaces

Traffic planning for autonomous vehicles

Course 4

  • 32 hours
  • 4.8 (461 ratings)

Course Details

What you’ll learn:
  • Welcome to the Traffic Planning for Autonomous Vehicles course, the fourth course in the University of Toronto’s Autonomous Vehicles specialization.
  • This course will introduce you to the main planning tasks in autonomous driving, including task planning, behavior planning, and local planning.
  • By the end of this course, you will be able to find the shortest path on a graph or road network using Dijkstra’s algorithm and A*
  • Use finite state machines to select safe behaviors to execute
  • Develop optimal routes and smooth speed profiles to safely navigate around obstacles while adhering to traffic laws.
  • Additionally, you will prepare grid maps of occupancy of static elements in the environment and learn how to use them to effectively check for collisions.
  • This course will provide you with the ability to build a fully self-planning solution that will take you from home to work while behaving like a typical driver and maintaining safety at all times.
For the final project in this course:
  • Implement a hierarchical traffic planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a parked vehicle in your lane, following a lead vehicle, and safely navigating an intersection.
  • You will deal with real-world randomness and will need to work to ensure that your solution is robust to changes in the environment.
Course requirements:
  • This is an intermediate-level course, designed for learners with some background in robotics, and is based on the models and controllers created in Course 1 of this specialization.
  • To succeed in this course, you must have experience programming in Python 3.0, and familiarity with linear algebra (matrices, vectors, matrix multiplication, rank, eigenvalues ​​and vectors, and inverses) as well as with differential equations (ordinary differential equations, integration).

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