課程信息

第 4 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

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高級

完成時間大約為21 小時

建議:9 hours/week...

英語(English)

字幕:英語(English)

第 4 門課程(共 4 門)

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

根據您的日程表重置截止日期。

高級

完成時間大約為21 小時

建議:9 hours/week...

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
完成時間為 1 小時

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

This module introduces the motion planning course, as well as some supplementary materials....
4 個視頻 (總計 18 分鐘), 3 個閱讀材料
4 個視頻
Welcome to the Course3分鐘
Meet the Instructor, Steven Waslander5分鐘
Meet the Instructor, Jonathan Kelly2分鐘
3 個閱讀材料
Course Readings10分鐘
How to Use Discussion Forums15分鐘
How to Use Supplementary Readings in This Course15分鐘
完成時間為 2 小時

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. ...
4 個視頻 (總計 54 分鐘), 1 個閱讀材料, 1 個測驗
4 個視頻
Lesson 2: Motion Planning Constraints13分鐘
Lesson 3: Objective Functions for Autonomous Driving9分鐘
Lesson 4: Hierarchical Motion Planning17分鐘
1 個閱讀材料
Module 1 Supplementary Reading10分鐘
1 個練習
Module 1 Graded Quiz50分鐘
2
完成時間為 6 小時

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. ...
4 個視頻 (總計 38 分鐘), 1 個閱讀材料, 1 個測驗
4 個視頻
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9分鐘
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9分鐘
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9分鐘
1 個閱讀材料
Module 2 Supplementary Reading
3
完成時間為 4 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. ...
3 個視頻 (總計 35 分鐘), 1 個閱讀材料, 1 個測驗
3 個視頻
Lesson 2: Dijkstra's Shortest Path Search10分鐘
Lesson 3: A* Shortest Path Search13分鐘
1 個閱讀材料
Module 3 Supplementary Reading
1 個練習
Module 3 Graded Quiz50分鐘
4
完成時間為 2 小時

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. ...
3 個視頻 (總計 36 分鐘), 1 個閱讀材料, 1 個測驗
3 個視頻
Lesson 2: Map-Aware Motion Prediction11分鐘
Lesson 3: Time to Collision12分鐘
1 個閱讀材料
Module 4 Supplementary Reading
1 個練習
Module 4 Graded Quiz50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 自动驾驶汽车 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)....
自动驾驶汽车

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