课程信息

第 4 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为21 小时

建议:9 hours/week...

英语(English)

字幕:英语(English)

第 4 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为21 小时

建议:9 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 1 小时

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

This module introduces the motion planning course, as well as some supplementary materials....
4 个视频 (总计 18 分钟), 3 个阅读材料
4 个视频
Welcome to the Course3分钟
Meet the Instructor, Steven Waslander5分钟
Meet the Instructor, Jonathan Kelly2分钟
3 个阅读材料
Course Readings10分钟
How to Use Discussion Forums15分钟
How to Use Supplementary Readings in This Course15分钟
完成时间为 2 小时

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 个视频 (总计 54 分钟), 1 个阅读材料, 1 个测验
4 个视频
Lesson 2: Motion Planning Constraints13分钟
Lesson 3: Objective Functions for Autonomous Driving9分钟
Lesson 4: Hierarchical Motion Planning17分钟
1 个阅读材料
Module 1 Supplementary Reading10分钟
1 个练习
Module 1 Graded Quiz50分钟
2
完成时间为 6 小时

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 个视频 (总计 38 分钟), 1 个阅读材料, 1 个测验
4 个视频
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9分钟
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9分钟
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9分钟
1 个阅读材料
Module 2 Supplementary Reading
3
完成时间为 4 小时

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 个视频 (总计 35 分钟), 1 个阅读材料, 1 个测验
3 个视频
Lesson 2: Dijkstra's Shortest Path Search10分钟
Lesson 3: A* Shortest Path Search13分钟
1 个阅读材料
Module 3 Supplementary Reading
1 个练习
Module 3 Graded Quiz50分钟
4
完成时间为 2 小时

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 个视频 (总计 36 分钟), 1 个阅读材料, 1 个测验
3 个视频
Lesson 2: Map-Aware Motion Prediction11分钟
Lesson 3: Time to Collision12分钟
1 个阅读材料
Module 4 Supplementary Reading
1 个练习
Module 4 Graded Quiz50分钟

讲师

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

关于 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. ...

关于 自动驾驶汽车 专项课程

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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