This programme has a genuine place in the history of online technical education. When it launched, self-driving cars were the most exciting application in applied machine learning, Udacity had Sebastian Thrun and credible industry partnerships, and the nanodegree put serious autonomy engineering in front of people who had no route into it otherwise. A number of people did use it to enter the field. That was a real achievement and it is worth saying before the criticism.
The engineering content is still the strongest thing about it. The projects are not notebook exercises. You implement an extended Kalman filter. You write a particle filter for localisation.
You build a path planner that has to handle traffic. You implement PID and model predictive control. Much of it is in C++, which almost no online machine learning programme asks for and which is exactly what the industry actually uses for anything running on a vehicle. Getting a control loop to behave in a simulator is a different order of difficulty from getting a classifier to converge, and the people who finish this come out having built things that work under constraints.
The curriculum covers the full stack: computer vision and deep learning for perception, sensor fusion across camera, lidar and radar, localisation, planning, and control. The sensor fusion material in particular is taught better here than in most places, and the topic is genuinely hard to learn well from scattered sources. Now the problems, and they are substantial. The first is price.
Udacity's subscription model puts the full programme comfortably over a thousand dollars for most people, and more if you take longer than planned, which many do. Against that, a great deal of comparable material is free: university robotics and autonomy courses, the ROS ecosystem's documentation and tutorials, open simulators, and published implementations of every algorithm taught here. The nanodegree's advantages are sequencing, project review and a certificate. Whether those are worth a thousand dollars is a question I would answer differently depending on whose money it is.
The second is currency. Parts of the material have not moved with the field. The perception content in particular reflects an earlier generation of approaches, and the industry's shift towards learned end to end components and transformer based perception is not well represented. You will learn the classical stack thoroughly and the modern research direction hardly at all.
For a foundational education that is defensible, and it does mean the programme is less current than its price implies. The third is the job market, and this is the one I would weigh most heavily. The autonomous vehicle sector consolidated hard. Several well funded companies closed or were absorbed, hiring contracted, and the number of entry level autonomy roles is a fraction of what it was when this programme was designed.
The skills remain valuable in robotics, drones, industrial automation and adjacent areas, and the specific path this nanodegree was built to serve is much narrower than it was. Anyone enrolling should be clear-eyed about that rather than working from the programme's historical reputation. Mentor and project review quality is inconsistent, which is a long standing complaint across Udacity's catalogue. Some reviews are detailed and genuinely improve your work, others are cursory.
For a programme whose main claimed advantage over free material is human feedback, that inconsistency undercuts the value proposition directly. My three point four reflects genuinely substantial engineering content, taught with more rigour than most paid programmes, weighed against a high price, ageing curriculum sections, uneven support and a target job market that has shrunk considerably. If autonomy is specifically your goal and your employer is paying, it is a reasonable structured path. For anyone else, the same money and five months spent on robotics fundamentals and open source projects will take you further.