Qué necesitas saber antes de
comenzar
Inicio 4 June 2026 03:41
Fin 4 June 2026
Motion Planning for Self-Driving Cars
National Taiwan University
10 Cursos
La Universidad Nacional de Taiwán (NTU) es una universidad de investigación de clase mundial ubicada en Taipei, Taiwán. Cuenta con un personal docente de primer nivel, programas académicos exhaustivos y un ambiente amigable y dinámico que la convierte en el lugar perfecto para estudiar e investigar.
No especificado
Actualización opcional disponible
Todos los niveles
Avanza a tu propio ritmo
Free
Actualización opcional disponible
Resumen
Welcome to the "Motion Planning for Self-Driving Cars" course, offered as part of the University of Toronto's comprehensive Self-Driving Cars Specialization on Coursera. This intermediate-level course is designed for individuals with a background in robotics, eager to delve into the intricacies of autonomous driving.
Throughout the course, participants will explore the core planning tasks pivotal to self-driving technology, including mission planning, behavior planning, and local planning.
Learners will gain hands-on experience with algorithms essential for navigating self-driving cars safely, such as Dijkstra's and the A* algorithm, for finding the most efficient paths. The course also covers the use of finite state machines for selecting safe driving behaviors, designing optimal paths and velocity profiles for maneuvering around obstacles while adhering to traffic regulations, and constructing occupancy grid maps for effective collision checking.
By the culmination of this course, participants will have the skills to develop a comprehensive motion planning solution, capable of guiding a self-driving car from point A to B safely and efficiently, simulating typical human driving behavior.
An exciting opportunity awaits in the final project, where learners will apply their knowledge to implement a hierarchical motion planner in the CARLA simulator. This project challenges students to navigate through various scenarios, such as avoiding a parked vehicle, following a lead vehicle, and safely crossing an intersection, all while adapting to the unpredictability of real-world environments.
This course is ideal for those with programming knowledge in Python 3.0, along with a good understanding of Linear Algebra and calculus, looking to make significant strides in autonomous vehicle technology.
Offered by the prestigious University of Toronto and available on Coursera, this course falls under both Autonomous Vehicles and Dijkstra's Algorithm categories, promising a rich learning experience in the field of self-driving cars.
University:
National Taiwan University. Provider:
Coursera.
Categories:
Autonomous Vehicles Courses, Dijkstra's Algorithm Courses.
Impartido por
Steven Waslander and Jonathan Kelly
