Practical advances in machine learning and artificial intelligence (AI) are ushering in a new era of digital automation. In the next ten to fifteen years drones driverless vehicles and artificial intelligence will be used to transport goods send packages perform agricultural tasks and transport people in an efficient and safe way. In this course students learn the algorithms that underlie an autonomous vehicle's understanding of itself and the world around it. They learn how a car can use unreliable sensor data to make accurate predictions of its location in the world. This algorithm called SLAM (simultaneous localization and mapping) relies on Bayesian inference tracking algorithms Kalman filtering and sensor fusion. Students learn how to use an algorithm that employs a map and traffic information to find the quickest route between two points. Students also use code that helps them simulate visualize test and debug the trajectories that comes from the search and control algorithms using the most popular visualization libraries. Finally students learn the system architecture of the autonomous navigation vehicles and how to integrate all the algorithms.
