About this Course
최근 조회 30,985

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 17시간 필요

권장: 13 hours/week...

영어

자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 17시간 필요

권장: 13 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

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

This module introduces the motion planning course, as well as some supplementary materials....
4 videos (Total 18 min), 3 readings
4개의 동영상
Welcome to the Course3m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
3개의 읽기 자료
Course Readings10m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
완료하는 데 2시간 필요

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 videos (Total 54 min), 1 reading, 1 quiz
4개의 동영상
Lesson 2: Motion Planning Constraints13m
Lesson 3: Objective Functions for Autonomous Driving9m
Lesson 4: Hierarchical Motion Planning17m
1개의 읽기 자료
Module 1 Supplementary Reading10m
1개 연습문제
Module 1 Graded Quiz50m
2
완료하는 데 6시간 필요

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. ...
5 videos (Total 50 min), 1 reading, 1 quiz
5개의 동영상
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9m
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9m
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9m
Lesson 4: High Definition Road Maps11m
1개의 읽기 자료
Module 2 Supplementary Reading
3
완료하는 데 4시간 필요

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 videos (Total 35 min), 1 reading, 1 quiz
3개의 동영상
Lesson 2: Dijkstra's Shortest Path Search10m
Lesson 3: A* Shortest Path Search13m
1개의 읽기 자료
Module 3 Supplementary Reading
1개 연습문제
Module 3 Graded Quiz50m
4
완료하는 데 2시간 필요

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 videos (Total 36 min), 1 reading, 1 quiz
3개의 동영상
Lesson 2: Map-Aware Motion Prediction11m
Lesson 3: Time to Collision12m
1개의 읽기 자료
Module 4 Supplementary Reading
1개 연습문제
Module 4 Graded Quiz50m
5
완료하는 데 3시간 필요

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....
5 videos (Total 53 min), 1 reading, 1 quiz
5개의 동영상
Lesson 2: Handling an Intersection Scenario Without Dynamic Objects9m
Lesson 3: Handling an Intersection Scenario with Dynamic Objects12m
Lesson 4: Handling Multiple Scenarios7m
Lesson 5: Advanced Methods for Behaviour Planning11m
1개의 읽기 자료
Module 5 Supplementary Reading
1개 연습문제
Module 5 Graded Quiz50m
6
완료하는 데 2시간 필요

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. ...
4 videos (Total 38 min), 1 reading, 1 quiz
4개의 동영상
Lesson 2: Collision Checking12m
Lesson 3: Trajectory Rollout Algorithm11m
Lesson 4: Dynamic Windowing7m
1개의 읽기 자료
Module 6 Supplementary Reading
1개 연습문제
Module 6 Graded Quiz50m
7
완료하는 데 11시간 필요

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. ...
9 videos (Total 71 min), 2 readings, 1 quiz
9개의 동영상
Lesson 2: Path Planning Optimization12m
Lesson 3: Optimization in Python5m
Lesson 4: Conformal Lattice Planning10m
Lesson 5: Velocity Profile Generation12m
Final Project Overview4m
Final Project Solution [LOCKED]7m
Congratulations for completing the course!2m
Congratulations on Completing the Specialization!3m
2개의 읽기 자료
Module 7 Supplementary Reading
CARLA Installation Guide45m

강사

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

토론토 대학교 정보

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. ...

자율 주행 자동차 전문 분야 정보

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)....
자율 주행 자동차

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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