Latest Courses
Business Analytics: Use Data Analysis for Financial IndustryCheck course
Akka Streams with Scala | Rock the JVMCheck course
Android app development course from Beginner to ProfessionalCheck course
Akka Serialization with Scala | Rock the JVMCheck course
Akka Remoting and Clustering with Scala | Rock the JVMCheck course
Learn C++ from scratchCheck course
Learn Dart & Flutter for Native Mobile Apps DevelopmentCheck course
Java Multithreading & Concurrency - Interview Practice ExamsCheck course
Programming Bootcamp for Kids and BeginnersCheck course
java EE : Practice Tests for Java EE CertificationCheck course
Business Analytics: Use Data Analysis for Financial IndustryCheck course
Akka Streams with Scala | Rock the JVMCheck course
Android app development course from Beginner to ProfessionalCheck course
Akka Serialization with Scala | Rock the JVMCheck course
Akka Remoting and Clustering with Scala | Rock the JVMCheck course
Visual Perception for Self-Driving Cars

Visual Perception for Self-Driving Cars

FREE

Add your review
Add to wishlistAdded to wishlistRemoved from wishlist 0
Add to compare
8.6/10 (Our Score)
Product is rated as #40 in category Artificial Intelligence

Welcome to Visual Perception for Self–Driving Cars, the third course in University of Toronto’s Self–Driving Cars Specialization. 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 end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks. You’ll apply these methods to visual odometry, object detection and tracking, and semantic segmentation for drivable surface estimation. These techniques represent the main building blocks of the perception system for self–driving cars. For the final project in this course, you will develop algorithms that identify bounding boxes for objects in the scene, and define the boundaries of the drivable surface. You’ll work with synthetic and real image data, and evaluate your performance on a realistic dataset. This is an advanced course, intended for learners with a background in computer vision and deep learning. 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). …

Instructor Details

Prof. Steven Waslander is a leading authority on autonomous aerial and ground vehicles, including multirotor drones and autonomous driving, Simultaneous Localization and Mapping (SLAM) and multi-vehicle systems. He received his B.Sc.E.in 1998 from Queen’s University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics, where as a graduate student he created the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), the world’s most capable outdoor multi-vehicle quadrotor platform at the time. He was recruited to Waterloo from Stanford in 2008, where he founded and directs the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous drones and autonomous driving through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination. In 2018, he joined the University of Toronto Institute for Aerospace Studies (UTIAS), and founded the Toronto Robotics and Artificial Intelligence Laboratory (TRAILab). Prof. Waslander’s innovations in drone research were recognized by the Ontario Centres of Excellence Mind to Market award for the best Industry/Academia collaboration (2012, with Aeryon Labs), best paper and best poster awards at the Computer and Robot Vision Conference (2018), and through two Outstanding Performance Awards, and two Distinguished Performance Awards while at the University of Waterloo. His work on autonomous vehicles has resulted in the Autonomoose, the first autonomous vehicle created at a Canadian University to drive over 100 km on public roads. His insights into autonomous driving have been featured in the Globe and Mail, Toronto Star, National Post, the Rick Mercer Report, and on national CBC Radio. He is Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems, has served as the General Chair for the International Autonomous Robot Racing Competition (IARRC 2012-15), as the program chair for the 13th and 14th Conference on Computer and Robot Vision (CRV 2016-17), and as the Competitions Chair for the International Conference on Intelligent Robots and Systems (IROS 2017).

Specification: Visual Perception for Self-Driving Cars

Duration

20 hours

Year

2019

Level

Expert

Certificate

Yes

Quizzes

No

18 reviews for Visual Perception for Self-Driving Cars

4.4 out of 5
11
5
1
0
1
Write a review
Show all Most Helpful Highest Rating Lowest Rating
  1. Avatar

    Levente K

    Good intro for those with not much experience w/ image processing/computer vision w.r.t. autonomous driving.

    Helpful(0) Unhelpful(0)You have already voted this
  2. Avatar

    haozhen3

    I do not understand why this course just have 4.3 ratting. Personally I think this course is very very helpful. It provides many practical advice and makes feel that I have got a up to date understanding of this fiels. There is no doubt that this is one of the best courses on Coursera.

    Helpful(0) Unhelpful(0)You have already voted this
  3. Avatar

    Camilo A A B

    It is an amazing course. Really good information and projects related with Visual Perception

    Helpful(0) Unhelpful(0)You have already voted this
  4. Avatar

    Miguel

    Excelent Course, extremely recommended.

    Helpful(0) Unhelpful(0)You have already voted this
  5. Avatar

    Joachim S

    Like the previous two course I found this one well structured and presented. Basically my comments from course 1 and 2 still hold. I found the coding assignment for week 2 rather challenging but with the help of the discussion forum there should be no problem to pass it. In contrast the final coding project was less difficult. I really loved the content of the final assignment as it provided a detailed look under the hood of a perception stack guiding you through the various stages. The multiples pictures generated as part of your code are a great help to understand the various aspects.

    Helpful(0) Unhelpful(0)You have already voted this
  6. Avatar

    River L

    I had a great time taking this course. Thank you!

    Helpful(0) Unhelpful(0)You have already voted this
  7. Avatar

    Jon H

    While the lectures were fairly well done they in no way prepared you for the projects. Way too much time was spent deciphering exactly what was to be done in the project sections. Very disappointing was the complete lack of any support on the forum none zero. A little feedback and support here would haver made all the difference. Absolutely do NOT recommend this course and will not be taking the following on. Just not worth the time. I am better off learning on my own.

    Helpful(2) Unhelpful(0)You have already voted this
  8. Avatar

    Aref A

    Content is great but lack of instructor support makes the course hard to understand.

    Helpful(2) Unhelpful(0)You have already voted this
  9. Avatar

    Sruti B

    Useful

    Helpful(0) Unhelpful(0)You have already voted this
  10. Avatar

    Chen L

    Some programming assignments need to be provided with more guidance and clarification.

    Helpful(1) Unhelpful(0)You have already voted this
  11. Avatar

    Abdelrahman M

    need more example in lesson for programming tasks and for equations

    Helpful(1) Unhelpful(0)You have already voted this
  12. Avatar

    Remon G

    Many thanks for this amazing course!!!! was very hard to me but I have learned a lot!!! Thanks!!!

    Helpful(0) Unhelpful(0)You have already voted this
  13. Avatar

    Igor S

    Lots of errors in assignments. I had to read forums for almost every graded assignment, that’s disappointing.

    Helpful(1) Unhelpful(0)You have already voted this
  14. Avatar

    PRASHANT K R

    superb, the assignment was quite tough but the overall experience was amazing. thanks to instructors, TAs, Coursera, and fellow learners!

    Helpful(0) Unhelpful(0)You have already voted this
  15. Avatar

    Svetoslav V

    I know this is a tough topic, but I was expecting more in depth and practical coverage of the object detection and segmentation CNNs. Week 1&2 gave a good overview of visual perception and feature detection. Week 3 6 were pretty shallow. The final project was reasonably well put, but the outputs of the object detector and the segmentation CNNs were just given for use and to me personally those are the most interesting aspects of the autonomous vehicle vision system.

    Helpful(1) Unhelpful(0)You have already voted this
  16. Avatar

    REVANTH B

    I am really surprised at the depth of topics discussed. I believe i spent around 5 8 hours researching topics on ANN and Machine learning.

    Helpful(0) Unhelpful(0)You have already voted this
  17. Avatar

    Sen Y

    I feel disappointed. Programming assignments are neither for 2D object detection nor for semantic segmentation.

    Helpful(0) Unhelpful(0)You have already voted this
  18. Avatar

    Eric H

    This course is excellent! It covers a broad range of basics of computer vision to in depth image detection and object collision estimation. I’d recommend this to anyone looking for a thorough introduction to visual perception for self driving cars.

    Helpful(0) Unhelpful(0)You have already voted this

    Add a review

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

    This site uses Akismet to reduce spam. Learn how your comment data is processed.

    Price tracking

    Java Code Geeks
    Logo
    Register New Account
    Reset Password
    Compare items
    • Total (0)
    Compare
    Shopping cart