Motion Planning for Self-Driving Cars

2.4
2.4 rating

Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization.

This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You'll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self-driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times.

For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You'll face real-world randomness and need to work to ensure your solution is robust to changes in the environment.

This is an intermediate course, intended for learners with some background in robotics, and it builds on the models and controllers devised in Course 1 of this specialization. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses) and calculus (ordinary differential equations, integration).

WEEK 1
1 hour to complete
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

2 hours to complete
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.
SHOW ALL SYLLABUS
SHOW ALL
4 videos (Total 54 min) 1 reading 1 quiz

WEEK 2
6 hours to complete
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

WEEK 3
4 hours to complete
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

WEEK 4
2 hours to complete
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


Tham gia đánh giá khóa học

Nếu bạn đã học qua khóa học này thì mời bạn tham gia đóng góp ý kiến và đánh giá để cộng đồng bạn học có thêm thông tin tham khảo.

Cung cấp bởi: Coursera /  University of Toronto

Thời lượng: 32 giờ
Ngôn ngữ giảng dạy: Tiếng Anh
Chi phí: Miễn phí / 0
Đối tượng: Advanced

Thông tin về nhà cung cấp

Coursera (/ kərˈsɛrə /) là một nền tảng học tập trực tuyến toàn cầu được thành lập vào năm 2012 bởi 2 giáo sư khoa học máy tính của đại học Stanford là Andrew NgDaphne Koller, nền tảng này cung cấp các khóa học trực tuyến (MOOC) cho cộng đồng người học online.

Coursera hợp tác với các trường đại học danh tiếng tại Bắc Mỹ và trên khắp thế giới, cùng với nhiều tổ chức khác để cung cấp các khóa học trực tuyến chất lượng, theo chuyên ngành và được cấp chứng chỉ trong nhiều lĩnh vực như kỹ thuật, khoa học dữ liệu, học máy, toán học, kinh doanh, khoa học máy tính, tiếp thị kỹ thuật số, nhân văn, y học, sinh học, khoa học xã hội , và nhiều ngành khác.

Các khóa học cùng chủ đề

Visual Perception for Self-Driving Cars

This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the...

Capstone: Autonomous Runway Detection for IoT

This capstone project course ties together the knowledge from three previous courses in IoT though embedded systems: Development of Real-Time Systems Web Connectivity & Security and Embedded Hardware and Operating...

System Validation (4): Modelling Software Protocols and other behaviour

System Validation is the field that studies the fundamentals of system communication and information processing. It allows automated analysis based on behavioural models of a system to see if a...

Leave a Reply

Your email address will not be published. Required fields are marked *

Scroll to Top