À propos de ce cours
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Sous-titres : Anglais

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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

Approx. 17 heures pour terminer

Recommandé : 11 hours/week...

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
1 heures pour terminer

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

This module introduces the motion planning course, as well as some supplementary materials.

...
4 vidéos (Total 18 min), 3 lectures
4 vidéos
Welcome to the Course3 min
Meet the Instructor, Steven Waslander5 min
Meet the Instructor, Jonathan Kelly2 min
3 lectures
Course Readings10 min
How to Use Discussion Forums15 min
How to Use Supplementary Readings in This Course15 min
2 heures pour terminer

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 vidéos (Total 54 min), 1 lecture, 1 quiz
4 vidéos
Lesson 2: Motion Planning Constraints13 min
Lesson 3: Objective Functions for Autonomous Driving9 min
Lesson 4: Hierarchical Motion Planning17 min
1 lectures
Module 1 Supplementary Reading10 min
1 exercices pour s'entraîner
Module 1 Graded Quiz50 min
Semaine
2
6 heures pour terminer

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 vidéos (Total 50 min), 1 lecture, 1 quiz
5 vidéos
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 1)9 min
Lesson 2: Populating Occupancy Grids from LIDAR Scan Data (Part 2)9 min
Lesson 3: Occupancy Grid Updates for Self-Driving Cars9 min
Lesson 4: High Definition Road Maps11 min
1 lectures
Module 2 Supplementary Reading1 h
Semaine
3
4 heures pour terminer

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 vidéos (Total 35 min), 1 lecture, 1 quiz
3 vidéos
Lesson 2: Dijkstra's Shortest Path Search10 min
Lesson 3: A* Shortest Path Search13 min
1 lectures
Module 3 Supplementary Reading1 h
1 exercices pour s'entraîner
Module 3 Graded Quiz50 min
Semaine
4
2 heures pour terminer

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 vidéos (Total 36 min), 1 lecture, 1 quiz
3 vidéos
Lesson 2: Map-Aware Motion Prediction11 min
Lesson 3: Time to Collision12 min
1 lectures
Module 4 Supplementary Reading1 h
1 exercices pour s'entraîner
Module 4 Graded Quiz50 min
Semaine
5
3 heures pour terminer

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 vidéos (Total 53 min), 1 lecture, 1 quiz
5 vidéos
Lesson 2: Handling an Intersection Scenario Without Dynamic Objects9 min
Lesson 3: Handling an Intersection Scenario with Dynamic Objects12 min
Lesson 4: Handling Multiple Scenarios7 min
Lesson 5: Advanced Methods for Behaviour Planning11 min
1 lectures
Module 5 Supplementary Reading1 h
1 exercices pour s'entraîner
Module 5 Graded Quiz50 min
Semaine
6
2 heures pour terminer

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 vidéos (Total 38 min), 1 lecture, 1 quiz
4 vidéos
Lesson 2: Collision Checking12 min
Lesson 3: Trajectory Rollout Algorithm11 min
Lesson 4: Dynamic Windowing7 min
1 lectures
Module 6 Supplementary Reading1 h
1 exercices pour s'entraîner
Module 6 Graded Quiz50 min
Semaine
7
11 heures pour terminer

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 vidéos (Total 71 min), 2 lectures, 1 quiz
9 vidéos
Lesson 2: Path Planning Optimization12 min
Lesson 3: Optimization in Python5 min
Lesson 4: Conformal Lattice Planning10 min
Lesson 5: Velocity Profile Generation12 min
Final Project Overview4 min
Final Project Solution [LOCKED]7 min
Congratulations for completing the course!2 min
Congratulations on Completing the Specialization!3 min
2 lectures
Module 7 Supplementary Reading1 h
CARLA Installation Guide45 min

Enseignants

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Steven Waslander

Associate Professor
Aerospace Studies
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Jonathan Kelly

Assistant Professor
Aerospace Studies

À propos de Université de Toronto

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. ...

À propos de la Spécialisation Voiture autonome

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)....
Voiture autonome

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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