Lesson 3: Optimization in Python - Motion Planning for Self-Driving Cars
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Lesson 3: Optimization in Python - Motion Planning for Self-Driving Cars
Self-Driving Cars Specialization
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).
It was really well informative course and the assignments and projects were really helped me to understand the in real scenario implementation. Thanks.,Excellent Course with more practical insights. Also the assignments provided helps to understand the concept more practically.
Parameterized curves are widely used to define paths through the environment for self-driving. This module introduces continuous curve path optimization as a two point boundary value problem which minimized deviation from a desired path while satisfying curvature constraints.
Lesson 3: Optimization in Python - Motion Planning for Self-Driving Cars
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