Motion Planning for Self-Driving Cars Offered By University of Toronto
Welcome to Motion Planning for Self-Driving Cars, the fourth course in the Self-Driving Cars Specialization at the University of Toronto.
The primary planning tasks in autonomous driving, such as mission planning, behavior planning, and local planning, will be covered in this course. You will be able to use Dijkstra's and the A* algorithms to find the shortest path over a graph or road network, 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 by the end of this course. You'll also learn how to employ occupancy grid maps of static components in the environment for quick collision checks. This course will teach you how to build a complete self-driving planning solution that will transport you from home to work while behaving normally and keeping the vehicle safe at all times.
You will use a hierarchical motion planner to navigate through a succession of scenarios in the CARLA simulator for the final project in this course, including avoiding a car parked in your lane, following a lead vehicle, and safely navigating an intersection. You'll have to deal with real-world randomness, and you'll need to make sure your solution can withstand changes in the environment.
This course offers:
- Flexible deadlines: Reset deadlines in accordance to your schedule.
- Certificate : Earn a Certificate upon completion
- 100% online
- Beginner Level
- Approx. 32 hours to complete
- Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
- Course 4 of 4 in the Self-Driving Cars Specialization
Coursera Rating: 4.8/5.0
Enroll here: https://www.coursera.org/learn/motion-planning-self-driving-cars
