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.

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.

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

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

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

need more example in lesson for programming tasks and for equations

This course is good organised and contain the general representation of the perception task. But I'd like to notice several cons:

1 The material about neural networks doesn't have any practical exercises with NN training or inference.

2 The second assessment gave me a lot of nervous ours/days/months. It hasn't any expected results during the long code and compare only the final result at the submitting. But the most strange thing is that during this assessment student is supposed to do matrix equations that are not desired in lecture or supplementary material

3 As one of the basic neural networks VGG is considered. But in practice the VGG is very time consuming to use. This is important fact, as in self-driving vehicles we need to stay in real-time. VGG is definitely bad choice for that. It would be nice to add some overview of the fast NNs for object detection.

1.The professor's video only has very rough and basic introduction to the topics, while the first programming homework needs much more self_learning on many aspects. What disappointed me is that I often cannot get enough help from Coursera's resources, including forums.

2. I started learn this course aiming to apply what I have learned of CNN to some practical applications, but this course neither had in-depth introduction to the build of self-driving car CNN architecture nor relevant homework to practice it. It is the most disappointing part to me.

3. The final homework does not include most of the knowledge taught in this session but only focus on the semantic segmentation. I hope the final project could be more comprehensive..

Overall, I graded this course 3 stars, for the missing 2 stars due to very shallow introduction of CNN and superficial video contents.

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.

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.

Great delivery! Mr. Waslander explains the most complex technical stack in such a easy-to-understand way. It is most beneficial for students who have no experience in the visual perception field but want to have a general grasp of the overall workflow. Much learnt for me personally.

Though not dive into training neural net. But for me who have taken deep learning specialization, I fully respect this and find it amazing that this course introduces quite a lot of the application of deep learning output and provides programming exercises on them, which is great.

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.

The course has proved to another milestone in furthering my understanding of robotics, computer vision, machine learning and autonomous driving vehicles.

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.

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

Wonderful course. I knew little things about gps and imu sensors. Camera is a complete new concept to me. Thank you.

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

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

It was very challenging and worthy for me. An amazing way to learn very powerful skills.

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

Excelent Course, extremely recommended.

Great course, I learn a lot!

Excellent course material

Useful