Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The course Motion Planning for Self-Driving Cars is the final course among four courses in Self-driving cars specialisation offered by the University of Toronto. The level of the course is advanced and is designed for the learners having a background of robotics and knowledge of controllers and models taught in course 1 of the specialisation.
In the course, the participant will be introduced to major planning related tasks in an autonomous mode of driving along with various other types of planning like behaviour planning, local planning, and mission planning. At the end of the programme, the participant will be able to find the shortest and direct path over a road network or a graph by making the use of A* algorithm and Dijkstra’s. They will be using machines in the finite state for selection of smooth paths, optimal designs, and identification of velocity profiles required for the navigation around the obstacles with safety and in accordance to the traffic rules. The participants will also get a chance to develop occupancy grid maps for the static objects prevailing in the environment and use them to check efficient collision.
The course will provide the opportunity to develop a complete self-driving planning solution that will provide a contemporary driving experience as well as safety while taking an individual from home to work. As a part of the final project, the candidate will be implementing the planner of hierarchical motion by navigating through the sequence of scenarios in the CARLA simulator, safe navigation during intersection, and avoiding the already parked vehicles in the lane.
The Highlights
- Provision of resetting the deadlines of submissions as per the feasibility of the participant.
- The programme is offered by the University of Toronto.
- Approx. 32 hours to complete
- The course delivers an advanced level of learning.
- Online shareable certificate provided after successful completion of the course.
Programme Offerings
- Course Readings
- quizzes
- videos
- Supplementary Reading
- Discussion forum.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | University of Toronto, TorontoCoursera |
To pursue the course, students need to pay the fee that has been mentioned in the table below:
Motion Planning for Self-Driving Cars Fees Structure
| Course | Fees in INR |
| Motion Planning for Self-Driving Cars (audit only) | Free |
| Motion Planning for Self-Driving Cars - 1 month | Rs, 6,634 /- |
| Motion Planning for Self-Driving Cars - 3 months | Rs. 13,268 /- |
| Motion Planning for Self-Driving Cars - 6 months | Rs. 19,903 /- |
Eligibility Criteria
Work experience
Candidates applying for a programme in Motion Planning for Self-Driving Cars must possess an experience of Python 3.0 programming language.
Education
Candidates must have background knowledge in Robotics.
They should have knowledge about Linear Algebra (inverses, vectors, Eigenvalues, rank, matrix multiplication, vectors, matrices).
They should also know about the basics of Calculus (integration, differential equations).
Certification Qualifying Details
In order to get the certificate of completion, it is essential for candidates to complete the programme and pay for it as well.
What you will learn
The participants will gain mastery in the following skills:
- Learn the application of A* algorithm and Dijkstra’s.
- Understand the use of finite state machines.
- Gain knowledge about navigation through velocity profiles and smooth paths.
- Building grid maps based on the occupancy of the static elements in the surrounding environment.
- Learn the usage of efficient collision checking.
- Construct an autonomous planning solution.
Who it is for
Admission Details
A simple and easy to follow the process for seeking admission in this programme is given below:
Step 1: Visit the course page. https://www.coursera.org/learn/motion-planning-self-driving-cars
Step 2: Click ‘Enroll for free’ and get free access to the course material for 7 days.
Step 3: A window will appear that contains the details about information and policy. Read the same and click on the hyperlink ‘Start free trial’.
Step 4: Candidates need to enter the details such as card number, name on the card, CVV, card number, expiration date, and name of the country.
Step 5: Enter the details and get started.
The Syllabus
Videos
- Welcome to the Self-Driving Cars Specialization!
- Welcome to the Course
- Meet the Instructor, Steven Waslander
- Meet the Instructor, Jonathan Kelly
Readings
- Course Readings
- How to Use Discussion Forums
- How to Use Supplementary Readings in This Course
Videos
- Lesson 1: Driving Missions, Scenarios, and Behaviour
- Lesson 2: Motion Planning Constraints
- Lesson 3: Objective Functions for Autonomous Driving
- Lesson 4: Hierarchical Motion Planning
Reading
- Module 1 Supplementary Reading
Quiz
- Module 1 Graded Quiz
Videos
- Lesson 1: Occupancy Grids
- Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)
- Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)
- Lesson 3: Occupancy Grid Updates for Self-Driving Cars9m
- Lesson 4: High Definition Road Maps
Reading
- Module 2 Supplementary Reading
Videos
- Lesson 1: Creating a Road Network Graph
- Lesson 2: Dijkstra's Shortest Path Search
- Lesson 3: A* Shortest Path Search
Reading
- Module 3 Supplementary Reading
Quiz
- Module 3 Graded Quiz
Videos
- Lesson 1: Motion Prediction
- Lesson 2: Map-Aware Motion Prediction
- Lesson 3: Time to Collision
Reading
- Module 4 Supplementary Reading
Quiz
- Module 4 Graded Quiz
Videos
- Lesson 1: Behaviour Planning
- Lesson 2: Handling an Intersection Scenario Without Dynamic Objects
- Lesson 3: Handling an Intersection Scenario with Dynamic Objects
- Lesson 4: Handling Multiple Scenarios
- Lesson 5: Advanced Methods for Behaviour Planning
Reading
- Module 5 Supplementary Reading
Quiz
- Module 5 Graded Quiz
Videos
- Lesson 1: Trajectory Propagation
- Lesson 2: Collision Checking
- Lesson 3: Trajectory Rollout Algorithm
- Lesson 4: Dynamic Windowing
- Module 6 Supplementary Reading
Quiz
- Module 6 Graded Quiz
Videos
- Lesson 1: Parametric Curves
- Lesson 2: Path Planning Optimization
- Lesson 3: Optimization in Python
- Lesson 4: Conformal Lattice Planning
- Lesson 5: Velocity Profile Generation
- Final Project Overview
- Final Project Solution [LOCKED]
- Congratulations for completing the course!
- Congratulations on Completing the Specialization!
Readings
- Module 7 Supplementary Reading
- CARLA Installation Guide
Instructors
University of Toronto, Toronto Frequently Asked Questions (FAQ's)
Yes, there is a provision of financial aid for which the candidate needs to apply. After filling the required details, the candidate needs to wait for about 15 days for the review and intimation.
There is no refund or money-back guarantee after you subscribe to the course. The participant may stop the subscription anytime so that the fee deduction can be prevented from the next month.
The course is of advanced level.
The participants get reading material, discussion forum, videos, and supplementary reading along with quizzes at regular intervals.
The course is 32 hours long which can be pursued with complete dedication towards it.
Some universities may provide a credit on the specialisation certificate. The participant needs to confirm with the institution.
The participants have to take on the quizzes and discussion forums to fetch the grades in the course.
There is a provision wherein the candidate can take up the course for free by choosing to audit the course. However, the candidates don’t have access to graded assignments and certificates.