As a Udactiy SDC student finishing up the program without much real insight into the industry, the program seems very much to me like an entry-level overview of topics you're likely to encounter in this field: computer vision, machine/deep learning, path-finding, sensors, and some basic system integration.

By the end, you'll have a few toy projects and a very basic ability to converse about or study more deeply these topics. Maybe they should have spent a little more time on maps and SLAM instead of other stuff, but hey, it's their first version anyway; I'm sure they'll iterate (as they already have somewhat in response to feedback). They did kind of admit in the CV introduction (lane finding) that this was kind of a super simple, satisfying project to start with. I feel like they often touch on topics acknowledging they have more of a historical relevance and are no longer state-of-the-art tools.

(Edit: I should also note: while I have criticisms of the program, overall I'm very happy that I took it. I'm sure some people are better at seeking out specifically what information is relevant to dig deeper into, but I really appreciate the broad overview kind of pointing me in the right direction. Since starting the program I've amassed quite a list of resources to dig deeper into, and hope to eventually find work in this or related field)

All those subjects are useful. Some version of all those things will appear in the good systems. But it's the difference between building the flight control system for a toy drone and the flight control system for a Boeing 797. You need the "failure is not an option" attitude of aerospace. You need fault tree analysis. What if sensors disagree? What if a sensor is misaligned because someone bumped the vehicle while it was parked? You need enough of an organization to have people working through all those situations. You don't get to blame the user for your mistakes. A "minimum viable product" will kill.

Self-driving is something many people in software haven't seen much - software that isn't feature-oriented. As someone from Google pointed out, the output is just two numbers.

Isn't the practical reality that any course like that will address the theory and essentially deal with more or less toy problems? It would be hard to get into cutting edge research in an online course.

I'm not sure learning about natural language processing or image classification would be all that different--although those problems are arguably better understood and more bounded.

Even a site reliability engineering course isn't really going to give you deep insights into what, say, Google does on a day-to-day basis.

>Even a site reliability engineering course isn't really going to give you deep insights into what, say, Google does on a day-to-day basis.

I think this really gets to the core of the issue. There aren't many courses that will accurately reflect the actual state of the art in any field. Sometimes, graduate courses with a small scope and a relevant professor taught in person can partially address the state of the art, but an online course about "how to self driving car" is not that.

Lectures, tech talks, blogs, random experts in relevant subreddits, but yes, an online course is not going to give more than the fundamentals and some advanced, but already well understood examples.