Informações sobre o curso
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Aprox. 17 horas para completar

Sugerido: 11 hours/week...

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Legendas: Inglês

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 17 horas para completar

Sugerido: 11 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
1 hora para concluir

Welcome to Course 4: Motion Planning for Self-Driving Cars

This module introduces the motion planning course, as well as some supplementary materials.

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4 vídeos ((Total 18 mín.)), 3 leituras
4 videos
Welcome to the Course3min
Meet the Instructor, Steven Waslander5min
Meet the Instructor, Jonathan Kelly2min
3 leituras
Course Readings10min
How to Use Discussion Forums15min
How to Use Supplementary Readings in This Course15min
2 horas para concluir

Module 1: The Planning Problem

This module introduces the richness and challenges of the self-driving motion planning problem, demonstrating a working example that will be built toward throughout this course. The focus will be on defining the primary scenarios encountered in driving, types of loss functions and constraints that affect planning, as well as a common decomposition of the planning problem into behaviour and trajectory planning subproblems. This module introduces a generic, hierarchical motion planning optimization formulation that is further expanded and implemented throughout the subsequent modules.

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4 vídeos ((Total 54 mín.)), 1 leitura, 1 teste
4 videos
Lesson 2: Motion Planning Constraints13min
Lesson 3: Objective Functions for Autonomous Driving9min
Lesson 4: Hierarchical Motion Planning17min
1 leituras
Module 1 Supplementary Reading10min
1 exercício prático
Module 1 Graded Quiz50min
Semana
2
6 horas para concluir

Module 2: Mapping for Planning

The occupancy grid is a discretization of space into fixed-sized cells, each of which contains a probability that it is occupied. It is a basic data structure used throughout robotics and an alternative to storing full point clouds. This module introduces the occupancy grid and reviews the space and computation requirements of the data structure. In many cases, a 2D occupancy grid is sufficient; learners will examine ways to efficiently compress and filter 3D LIDAR scans to form 2D maps.

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5 vídeos ((Total 50 mín.)), 1 leitura, 1 teste
5 videos
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9min
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9min
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9min
Lesson 4: High Definition Road Maps11min
1 leituras
Module 2 Supplementary Reading1h
Semana
3
4 horas para concluir

Module 3: Mission Planning in Driving Environments

This module develops the concepts of shortest path search on graphs in order to find a sequence of road segments in a driving map that will navigate a vehicle from a current location to a destination. The modules covers the definition of a roadmap graph with road segments, intersections and travel times, and presents Dijkstra’s and A* search for identification of the shortest path across the road network.

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3 vídeos ((Total 35 mín.)), 1 leitura, 1 teste
3 videos
Lesson 2: Dijkstra's Shortest Path Search10min
Lesson 3: A* Shortest Path Search13min
1 leituras
Module 3 Supplementary Reading1h
1 exercício prático
Module 3 Graded Quiz50min
Semana
4
2 horas para concluir

Module 4: Dynamic Object Interactions

This module introduces dynamic obstacles into the behaviour planning problem, and presents learners with the tools to assess the time to collision of vehicles and pedestrians in the environment.

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3 vídeos ((Total 36 mín.)), 1 leitura, 1 teste
3 videos
Lesson 2: Map-Aware Motion Prediction11min
Lesson 3: Time to Collision12min
1 leituras
Module 4 Supplementary Reading1h
1 exercício prático
Module 4 Graded Quiz50min

Instrutores

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

Sobre Universidade de Toronto

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

Sobre o Programa de cursos integrados Carros autoguiáveis

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
Carros autoguiáveis

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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