What you'll learn
Welcome to Course 4: Motion Planning for Self-Driving Cars
Module 1: The Planning Problem
Module 2: Mapping for Planning
Module 3: Mission Planning in Driving Environments
Module 4: Dynamic Object Interactions
Module 5: Principles of Behaviour Planning
Module 6: Reactive Planning in Static Environments
Module 7: Putting it all together – Smooth Local Planning
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.
Access to a computer or mobile device with an internet connection.
Motivation to learn!
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).
Who this course is for
Students who have advanced skills in this field
Students willing to put in a couple hours to learn about Motion Planning for Self-Driving Cars
Advanced students wanting to add another skill to their portfolio
Steven Waslander – Associate Professor – Aerospace Studies
Jonathan Kelly – Assistant Professor – Aerospace Studies
This course includes
Option for learning at your own pace
Videos and reading material about the course
Assessed tasks with feedback from other course participants
Evaluated tests with feedback
Evaluated programming tasks