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推奨:11 hours/week...

英語

字幕:英語

次における1の1コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

上級レベル

約17時間で修了

推奨:11 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

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件のビデオ (合計18分), 3 readings
4件のビデオ
Welcome to the Course3 分
Meet the Instructor, Steven Waslander5 分
Meet the Instructor, Jonathan Kelly2 分
3件の学習用教材
Course Readings10 分
How to Use Discussion Forums15 分
How to Use Supplementary Readings in This Course15 分
2時間で修了

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件のビデオ (合計54分), 1 reading, 1 quiz
4件のビデオ
Lesson 2: Motion Planning Constraints13 分
Lesson 3: Objective Functions for Autonomous Driving9 分
Lesson 4: Hierarchical Motion Planning17 分
1件の学習用教材
Module 1 Supplementary Reading10 分
1の練習問題
Module 1 Graded Quiz50 分
2
6時間で修了

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件のビデオ (合計50分), 1 reading, 1 quiz
5件のビデオ
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9 分
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9 分
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9 分
Lesson 4: High Definition Road Maps11 分
1件の学習用教材
Module 2 Supplementary Reading1 時間
3
4時間で修了

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件のビデオ (合計35分), 1 reading, 1 quiz
3件のビデオ
Lesson 2: Dijkstra's Shortest Path Search10 分
Lesson 3: A* Shortest Path Search13 分
1件の学習用教材
Module 3 Supplementary Reading1 時間
1の練習問題
Module 3 Graded Quiz50 分
4
2時間で修了

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件のビデオ (合計36分), 1 reading, 1 quiz
3件のビデオ
Lesson 2: Map-Aware Motion Prediction11 分
Lesson 3: Time to Collision12 分
1件の学習用教材
Module 4 Supplementary Reading1 時間
1の練習問題
Module 4 Graded Quiz50 分
5
3時間で修了

Module 5: Principles of Behaviour Planning

This module develops a basic rule-based behaviour planning system, which performs high level decision making of driving behaviours such as lane changes, passing of parked cars and progress through intersections. The module defines a consistent set of rules that are evaluated to select preferred vehicle behaviours that restrict the set of possible paths and speed profiles to be explored in lower level planning.

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5件のビデオ (合計53分), 1 reading, 1 quiz
5件のビデオ
Lesson 2: Handling an Intersection Scenario Without Dynamic Objects9 分
Lesson 3: Handling an Intersection Scenario with Dynamic Objects12 分
Lesson 4: Handling Multiple Scenarios7 分
Lesson 5: Advanced Methods for Behaviour Planning11 分
1件の学習用教材
Module 5 Supplementary Reading1 時間
1の練習問題
Module 5 Graded Quiz50 分
6
2時間で修了

Module 6: Reactive Planning in Static Environments

A reactive planner takes local information available within a sensor footprint and a global objective defined in a map coordinate frame to identify a locally feasible path to follow that is collision free and makes progress to a goal. In this module, learners will develop a trajectory rollout and dynamic window planner, which enables path finding in arbitrary static 2D environments. The limits of the approach for true self-driving will also be discussed.

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4件のビデオ (合計38分), 1 reading, 1 quiz
4件のビデオ
Lesson 2: Collision Checking12 分
Lesson 3: Trajectory Rollout Algorithm11 分
Lesson 4: Dynamic Windowing7 分
1件の学習用教材
Module 6 Supplementary Reading1 時間
1の練習問題
Module 6 Graded Quiz50 分
7
11時間で修了

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.

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9件のビデオ (合計71分), 2 readings, 1 quiz
9件のビデオ
Lesson 2: Path Planning Optimization12 分
Lesson 3: Optimization in Python5 分
Lesson 4: Conformal Lattice Planning10 分
Lesson 5: Velocity Profile Generation12 分
Final Project Overview4 分
Final Project Solution [LOCKED]7 分
Congratulations for completing the course!2 分
Congratulations on Completing the Specialization!3 分
2件の学習用教材
Module 7 Supplementary Reading1 時間
CARLA Installation Guide45 分

講師

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

トロント大学(University of Toronto)について

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. ...

自動運転車の専門講座について

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)....
自動運転車

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