Course Summary
This course covers the basics of motion planning, including search algorithms, motion primitives, and planning under uncertainty. It is designed for anyone interested in self-driving cars, robotics, and autonomous systems.Key Learning Points
- Learn the fundamentals of motion planning for autonomous vehicles
- Understand how to design and implement search algorithms
- Explore different motion primitives and their applications
- Develop skills in planning under uncertainty
- Apply your knowledge to real-world scenarios
Related Topics for further study
Learning Outcomes
- Understand the basics of motion planning for autonomous vehicles
- Develop skills in designing and implementing search algorithms
- Apply your knowledge to real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Robotics: Perception
- Introduction to Self-Driving Cars
- Autonomous Navigation for Flying Robots
Related Education Paths
- Self-Driving Cars Specialization
- Robotics Specialization
- Artificial Intelligence for Robotics Specialization
Notable People in This Field
- Founder and President of Udacity
- Director of Strategy at OpenAI
Related Books
Description
Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization.
Outline
- Welcome to Course 4: Motion Planning for Self-Driving Cars
- Welcome to the Self-Driving Cars Specialization!
- Welcome to the Course
- Meet the Instructor, Steven Waslander
- Meet the Instructor, Jonathan Kelly
- Course Readings
- How to Use Discussion Forums
- How to Use Supplementary Readings in This Course
- Module 1: The Planning Problem
- Lesson 1: Driving Missions, Scenarios, and Behaviour
- Lesson 2: Motion Planning Constraints
- Lesson 3: Objective Functions for Autonomous Driving
- Lesson 4: Hierarchical Motion Planning
- Module 1 Supplementary Reading
- Module 1 Graded Quiz
- Module 2: Mapping for Planning
- 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 Cars
- Lesson 4: High Definition Road Maps
- Module 2 Supplementary Reading
- Module 3: Mission Planning in Driving Environments
- Lesson 1: Creating a Road Network Graph
- Lesson 2: Dijkstra's Shortest Path Search
- Lesson 3: A* Shortest Path Search
- Module 3 Supplementary Reading
- Module 3 Graded Quiz
- Module 4: Dynamic Object Interactions
- Lesson 1: Motion Prediction
- Lesson 2: Map-Aware Motion Prediction
- Lesson 3: Time to Collision
- Module 4 Supplementary Reading
- Module 4 Graded Quiz
- Module 5: Principles of Behaviour Planning
- 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
- Module 5 Supplementary Reading
- Module 5 Graded Quiz
- Module 6: Reactive Planning in Static Environments
- Lesson 1: Trajectory Propagation
- Lesson 2: Collision Checking
- Lesson 3: Trajectory Rollout Algorithm
- Lesson 4: Dynamic Windowing
- Module 6 Supplementary Reading
- Module 6 Graded Quiz
- Module 7: Putting it all together - Smooth Local Planning
- 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!
- Module 7 Supplementary Reading
- CARLA Installation Guide
Summary of User Reviews
Discover how to plan and execute motion in self-driving cars with this course. Students have been impressed with the content, delivery, and practical insights provided. One key aspect that many users thought was good is the course's comprehensive coverage of the subject matter.Pros from User Reviews
- Great insight into self-driving car technology
- Course material is well-organized and easy to understand
- Instructors are knowledgeable and engaging
- Real-world examples make the material applicable to industry
- Practical assignments help to reinforce learning
Cons from User Reviews
- Some users found the course to be too basic
- The course could benefit from more hands-on exercises
- There is a lack of interaction with other students
- The course may not be suitable for those without a technical background
- Some users found the course to be too theoretical
Steven Waslander
4.8 Raiting
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Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization. To succeed in this course, you should have programming experience in Python 3....
