課程信息
專項課程

第 3 門課程(共 4 門)

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高級

高級

完成時間(小時)

完成時間大約為19 小時

建議:9 hours/week...
可選語言

英語(English)

字幕:英語(English)
專項課程

第 3 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

可靈活調整截止日期

根據您的日程表重置截止日期。
高級

高級

完成時間(小時)

完成時間大約為19 小時

建議:9 hours/week...
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 2 小時

Welcome to Course 3: Visual Perception for Self-Driving Cars

This module introduces the main concepts from the broad field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations....
Reading
3 個視頻 (總計 14 分鐘), 3 個閱讀材料
Video3 個視頻
Meet the Instructor, Steven Waslander5分鐘
Meet the Instructor, Jonathan Kelly2分鐘
Reading3 個閱讀材料
CARLA Installation Guide45分鐘
How to Use Discussion Forums15分鐘
How to Use Supplementary Readings in This Course15分鐘
完成時間(小時)
完成時間為 3 小時

Module 1: Basics of 3D Computer Vision

...
Reading
1 個測驗
2
完成時間(小時)
完成時間為 5 小時

Module 2: Visual Features - Detection, Description and Matching

Visual features are used to track motion through an environment and to recognize places in a map. This module describes how features can be detected and tracked through a sequence of images and fused with other sources for localization as described in Course 2. Feature extraction is also fundamental to object detection and semantic segmentation in deep networks, and this module introduces some of the feature detection methods employed in that context as well....
Reading
1 個測驗
3
完成時間(小時)
完成時間為 1 小時

Module 3: Feedforward Neural Networks

Deep learning is a core enabling technology for self-driving perception. This module briefly introduces the core concepts employed in modern convolutional neural networks, with an emphasis on methods that have been proven to be effective for tasks such as object detection and semantic segmentation. Basic network architectures, common components and helpful tools for constructing and training networks are described....
Reading
1 個測驗
Quiz1 個練習
Feed-Forward Neural Networks30分鐘
4
完成時間(小時)
完成時間為 1 小時

Module 4: 2D Object Detection

The two most prevalent applications of deep neural networks to self-driving are object detection, including pedestrian, cyclists and vehicles, and semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. This module presents baseline techniques for object detection and the following module introduce semantic segmentation, both of which can be used to create a complete self-driving car perception pipeline....
Reading
1 個測驗
Quiz1 個練習
Object Detection For Self-Driving Cars30分鐘

講師

Avatar

Steven Waslander

Associate 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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