U of T - Motion Planning for Self-Driving Cars
- Offered byCoursera
- Public/Government Institute
Motion Planning for Self-Driving Cars at Coursera Overview
Duration | 32 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Motion Planning for Self-Driving Cars at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the Self-Driving Cars Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 32 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Motion Planning for Self-Driving Cars at Coursera Course details
- 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. 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).
Motion Planning for Self-Driving Cars at Coursera Curriculum
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
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
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