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#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 17 hours to complete

Suggested: 11 hours/week...

#### English

Subtitles: English

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 17 hours to complete

Suggested: 11 hours/week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
1 hour to complete

## Welcome to Course 4: Motion Planning for Self-Driving Cars

This module introduces the motion planning course, as well as some supplementary materials.

...
4 videos (Total 18 min), 3 readings
4 videos
Welcome to the Course3m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
2 hours to complete

## Module 1: The Planning Problem

This module introduces the richness and challenges of the self-driving motion planning problem, demonstrating a working example that will be built toward throughout this course. The focus will be on defining the primary scenarios encountered in driving, types of loss functions and constraints that affect planning, as well as a common decomposition of the planning problem into behaviour and trajectory planning subproblems. This module introduces a generic, hierarchical motion planning optimization formulation that is further expanded and implemented throughout the subsequent modules.

...
4 videos (Total 54 min), 1 reading, 1 quiz
4 videos
Lesson 2: Motion Planning Constraints13m
Lesson 3: Objective Functions for Autonomous Driving9m
Lesson 4: Hierarchical Motion Planning17m
1 practice exercise
Week
2
6 hours to complete

## Module 2: Mapping for Planning

The occupancy grid is a discretization of space into fixed-sized cells, each of which contains a probability that it is occupied. It is a basic data structure used throughout robotics and an alternative to storing full point clouds. This module introduces the occupancy grid and reviews the space and computation requirements of the data structure. In many cases, a 2D occupancy grid is sufficient; learners will examine ways to efficiently compress and filter 3D LIDAR scans to form 2D maps.

...
5 videos (Total 50 min), 1 reading, 1 quiz
5 videos
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9m
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9m
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9m
Lesson 4: High Definition Road Maps11m
Week
3
4 hours to complete

## Module 3: Mission Planning in Driving Environments

This module develops the concepts of shortest path search on graphs in order to find a sequence of road segments in a driving map that will navigate a vehicle from a current location to a destination. The modules covers the definition of a roadmap graph with road segments, intersections and travel times, and presents Dijkstra’s and A* search for identification of the shortest path across the road network.

...
3 videos (Total 35 min), 1 reading, 1 quiz
3 videos
Lesson 2: Dijkstra's Shortest Path Search10m
Lesson 3: A* Shortest Path Search13m
1 practice exercise
Week
4
2 hours to complete

## Module 4: Dynamic Object Interactions

This module introduces dynamic obstacles into the behaviour planning problem, and presents learners with the tools to assess the time to collision of vehicles and pedestrians in the environment.

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3 videos (Total 36 min), 1 reading, 1 quiz
3 videos
Lesson 2: Map-Aware Motion Prediction11m
Lesson 3: Time to Collision12m
1 practice exercise

## Instructors

### Steven Waslander

Associate Professor
Aerospace Studies

### Jonathan Kelly

Assistant Professor
Aerospace Studies

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

## About the Self-Driving Cars 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 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)....

• Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.