The virtual course "Movement Planning for Autonomous Vehicles - Virtual Course - Coursera" is a course with different contents and offers video classes from . Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Welcome to Motion Planning for Self-Driving Cars, the fourth course in the University of Toronto's Self-Driving Specialization. This course will introduce you to the main planning tasks of autonomous driving, including mission planning, behavior planning, and local planning. By the end of this course, you will be able to find the shortest route on a road graph or network using the Dijkstra and A* algorithm, use finite state machines to select safe running behaviors, and design optimal and consistent routes and speed profiles. safely navigate around obstacles while obeying traffic laws. You will also create occupancy grid maps of static elements in the environment and learn how to use them to efficiently check for collisions. This course will give you the ability to build a complete autonomous driving planning solution, to get you from home to work behaving like a typical driver and keeping the vehicle safe at all times. For the final project of this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a parked vehicle in your lane, following a leading vehicle, and safely navigating an intersection. . You'll be faced with the randomness of the real world, and you'll need to work to ensure that your solution is robust against changes in the environment. This is an intermediate course, intended for students with some experience in robotics, and is based on the models and controllers designed in Course 1 of this specialization. To be successful in this course, you must have programming experience in Python 3.0 and be familiar with linear algebra (matrices, vectors, matrix multiplication, range,
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