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專項課程

第 4 門課程(共 4 門)

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專項課程

第 4 門課程(共 4 門)

100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

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根據您的日程表重置截止日期。
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可選語言

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 1 小時

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

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....
Reading
3 個視頻 (總計 14 分鐘), 2 個閱讀材料
Video3 個視頻
Meet the Instructor, Steven Waslander5分鐘
Meet the Instructor, Jonathan Kelly2分鐘
Reading2 個閱讀材料
How to Use Discussion Forums15分鐘
How to Use Supplementary Readings in This Course15分鐘

Module 1: The Planning Problem

...
Reading
1 個測驗
Quiz1 個練習
Module 1 Summative Quiz
2
完成時間(小時)
完成時間為 1 小時

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. ...
Reading
3
完成時間(小時)
完成時間為 7 小時

Module 7: Putting it all together - Smooth Local Planning

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. ...
Reading
1 個閱讀材料, 1 個測驗
Reading1 個閱讀材料
CARLA Installation Guide45分鐘

講師

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

關於 多伦多大学

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. ...

關於 Self-Driving Cars 專項課程

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)....
Self-Driving Cars

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