Udacity Self Driving Car Nanodegree Review 2026: Learn To Make Autonomous Car

Written by Akshay Vikhe. Last updated: 13 October 2026 (full refresh: curriculum, projects, prerequisites, prices and reviews re-checked on Udacity’s nd0013 program page that day). Reviewed by the OnlineCourseing editorial team. See our review methodology.

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Become a Self Driving Car Engineer

Udacity’s Self-Driving Car Engineer Nanodegree (program key nd0013) teaches the software that lets a vehicle see, locate itself, plan a route and steer. In its current form it is an Advanced, 78-hour program with 6 core projects, built with Waymo as the industry partner. When we checked it on 13 October 2026, Udacity’s page showed a 4.2 rating from 46 reviews and a last update of 14 January 2026.

This review explains what the program teaches today, who it suits, what it costs and how it compares with the alternatives. Earlier versions of this page described the original 2017 curriculum: three nine-week terms, lane-line and behavioral-cloning projects, and a final project that ran code on Udacity’s real test car, Carla. That program no longer exists, and we have rewritten every section to match the current syllabus. Where we quote graduates, we say which version they took.

If you are deciding whether this is the right first step into autonomous vehicles, the short version is: it is a strong choice if you already program in Python and C++ and are comfortable with linear algebra; it is the wrong starting point if you are not. In that case our Intro to Self-Driving Cars Nanodegree review covers the on-ramp program.

Udacity Self Driving Car Nanodegree Reviews

We gathered these reviews from graduates when this page was first written. They describe earlier versions of the program (2017 to 2020), so the specific projects they mention have since changed, but their comments on workload, depth and job prospects still apply.

What graduates of earlier versions said

I learned many state-of-the-art algorithms of robotics. In fact, I’m one of the very first graduates of the Refresh version of this brand new Self Driving Car Nanodegree Program.

Juan Carlos Kuri Pinto, Neuro Synergies, rated it 5/5

Great content, engaging UI, good balance of coding and concepts lead to a fun learning experience. Consistent effort week over week to keep up with the course deadlines.

Rohit Athreya Ravikumar, Agro.ai, rated it 5/5

Glad to share that I have finally completed a 6 months long intensive Self-Driving Car Engineer Nanodegree program offered by Udacity. Would highly recommend the program.

Sardhendu Mishra, via LinkedIn (his project write-ups are on GitHub)

It covers the concepts which are needed for a robotics and deep learning approach for autonomous development. I got extra foundations in topics like Sensor Fusion, Deep Learning, Machine Vision and Motion Planning for autonomous systems.

Fabian Klein, researcher at Hochschule Esslingen, via LinkedIn

At the start of the course, it felt like quite some daunting journey that I wasn’t really sure I was going to be able to finish. But now as I graduate it gives me a huge feeling of elation.

Matthew Jones, IBM iX

The critical reviews

I have been disappointed by the changes recently made: they removed every relevant project that could get you a job and removed the communication platform that was the best part of being enrolled in a Nanodegree.

Jeremy Cohen, rated it 3/5 after the 2020 changes

The SDC industry is competitive, and they’re looking for the best of the best engineers, and having taken the SDC Nanodegree alone is not enough. This Nanodegree is more of a jack of all trades, master of none course.

wewtaco, on r/SelfDrivingCars (2017)

What recent learners say on Udacity’s page

Udacity’s page for the current program showed 4.2 from 46 reviews on 13 October 2026, lower than most Udacity programs (the Deep Learning Nanodegree, for comparison, showed 4.7 from 993). The recent written reviews explain why. A June 2026 reviewer gave three stars and said the object detection project needs updating. A 2024 reviewer called it very intense and recommended it for professionals aiming at perception engineering. A 2023 reviewer gave one star because the CARLA simulator kept failing and support took weeks. Across all of Udacity, Trustpilot showed 4.5 from 3,406 reviews on 13 October 2026.

Overview of this self driving car nanodegree

Source: Udacity’s nd0013 program page, read 13 October 2026. Udacity’s outline counts 7 projects; the six above are the graded core, and the seventh slot is the interview-practice and add-on material.

The program follows the order an autonomous vehicle processes the world. First it perceives (computer vision and lidar), then it fuses what its sensors report and tracks moving objects, then it works out exactly where it is on a map, then it decides what to do, and finally it turns that decision into steering and throttle commands. Each of the five technical courses covers one of those stages and ends in a project.

Is Udacity Self Driving Car Nanodegree Worth it?

It is worth it for a specific person: a software or robotics engineer who already writes Python and C++, wants a guided tour of the full autonomous-vehicle software stack, and will use the six projects as portfolio pieces. For that person, 78 hours of structured content plus reviewed projects is a sensible way to spend three or four months of a $249 subscription.

It is poor value in three situations. If you are new to programming or machine learning, you will spend your subscription fighting the prerequisites; take the Intro to Self-Driving Cars Nanodegree or a Python and C++ course first. If you want a job at an autonomy company, the Nanodegree will not be enough by itself: that market hires mostly experienced engineers and graduates with relevant degrees, and the graduates quoted above made the same point. And if you only want the theory, the University of Toronto specialization on Coursera covers similar ground for less.

Our rating of 3.8 out of 5 reflects strong, current content (Waymo data, lidar, scan matching, C++ planning and control) held back by the lowest learner rating among Udacity’s autonomy programs and recurring complaints about the simulator. For a full picture of the platform, see our Udacity Nanodegree review.

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What are self-driving cars?

A self-driving car is a vehicle that senses its surroundings and drives itself with little or no human input. To do that, its software has to solve five problems many times a second, and they map directly onto this Nanodegree’s courses:

1. Perception: cameras and lidar sensors capture the scene, and deep learning models detect vehicles, pedestrians and cyclists in it.
2. Sensor fusion and tracking: each sensor is noisy and has blind spots, so the software combines their readings (often with Kalman filters) into one estimate of where each object is and how it is moving.
3. Localization: GPS alone is accurate to a few meters, which is not enough to stay in a lane, so the car matches what its lidar sees against a detailed map to find its position to within centimeters.
4. Planning: the car decides what to do next (follow, overtake, stop) and generates a safe, comfortable trajectory to do it.
5. Control: a controller turns that trajectory into steering, throttle and brake commands, and corrects for errors as the car moves.

A self-driving car engineer usually specializes in one of these areas (perception engineer, localization engineer, planning engineer, controls engineer). The Nanodegree gives you a working introduction to all five, which is its main strength and, as one graduate put it, also its limit: it makes you a generalist, while employers usually hire specialists.

About Udacity Self Driving Car Engineer Nanodegree

Udacity built its first self-driving car program in 2016, when its co-founder Sebastian Thrun, who previously led Google’s self-driving car project, made autonomy one of the company’s flagship subjects. The current version was rebuilt around industry data: Waymo is the named partner, and the perception and sensor fusion projects use the Waymo Open Dataset, a large public dataset of real driving scenes. The planning course was developed with engineers from the Mercedes-Benz Vehicle Intelligence team, and Sebastian Thrun still teaches the Kalman filter and PID control lessons.

Two changes from the original program matter most. First, the old final project, in which student code was run on Udacity’s real test car (named Carla), is gone; the localization, planning and control projects now run in CARLA, an open-source driving simulator that shares the name. Second, the program is now self-paced on a subscription rather than run in fixed terms with deadlines.

Udacity’s page also marks the Nanodegree as eligible for credit toward its accredited MSc in Artificial Intelligence. We review that degree separately; if you think you may continue to a master’s, check how many credit hours this program carries before you enroll.

Cost and Duration of Self Driving Car Engineer Nanodegree

Udacity no longer sells Nanodegrees one at a time on a term schedule. On 13 October 2026 its nd0013 page offered three options:

The page also showed a banner offering 40% off any plan for new members; Udacity runs offers like this frequently, so check the live price before you pay.

Udacity estimates 78 hours. Graduates of earlier versions spent 15 to 20 hours a week, and most of that went into the projects rather than the videos. A realistic plan for someone working full time is three to four months at 8 to 10 hours a week, which puts the total at roughly $750 on the monthly plan or $846 on the four-month plan. Udacity’s Terms of Use (updated 16 January 2026) give seven days from your first purchase to cancel for a full refund.

See Udacity plans →

Syllabus for Self-Driving Car Engineer Program

These are the current courses and projects, from Udacity’s program outline on 13 October 2026.

Course 1: Computer Vision

You learn the machine learning workflow (framing the problem, choosing metrics, training and improving models), camera calibration, linear and logistic regression, feedforward networks, convolutional neural networks for image classification, and object detection. The project is Object Detection in an Urban Environment: train a CNN with TensorFlow on the Waymo Open Dataset to detect vehicles, pedestrians and cyclists.

Course 2: Sensor Fusion

You learn how lidar works and how to detect objects in 3D point clouds, then Kalman filters (taught by Sebastian Thrun), extended Kalman filters and multi-target tracking. There are two projects: a mid-term 3D Object Detection from Lidar, benchmarked on Waymo data, and a final Sensor Fusion and Object Tracking project that fuses camera and lidar detections to track several objects with an extended Kalman filter. Our Sensor Fusion Engineer Nanodegree review covers the standalone program that goes deeper on this course.

Course 3: Localization

You start with the bicycle motion model and Markov localization in C++, then build two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDT). The project, Scan Matching Localization, localizes a simulated car within a 3D point cloud map taken from the CARLA simulator. A C++ checkpoint quiz at the start of this course tells you whether your C++ is ready.

Course 4: Planning

Behavior planning (deciding what maneuver to make), trajectory generation with C++ and the Eigen linear algebra library, and motion planning. The project, Motion Planning and Decision Making for Autonomous Vehicles, has you build behavior and trajectory planning so the car navigates urban routes and avoids obstacles.

Course 5: Control

How to turn a planned trajectory into throttle and steering commands, starting with PID control. The project, Control and Trajectory Tracking, has you design and tune a C++ PID controller so the car follows its planned path precisely.

Optional additional content

Six extra modules sit outside the graded path: unscented Kalman filters, prediction of other road users’ behavior, vehicle models and model predictive control, an introduction to the Point Cloud Library, deep learning for scene understanding and inference performance, and functional safety (a high-level overview of ISO 26262, including safety plans and hazard analysis). The program ends with autonomous-systems interview practice.

What are the prerequisites for the course?

Udacity lists eight prerequisites: intermediate Python, object-oriented Python, Python data analysis libraries, matplotlib, C++ proficiency, linear algebra, basic calculus and finding derivatives. It also expects fluent written and spoken English.

Use this self-check before you pay. If you cannot do all five comfortably, spend a month on the gaps first:

1. Write a Python class with methods and use NumPy arrays without looking up the syntax.
2. Read and modify a C++ program that uses classes, vectors and pointers, and compile it from the command line.
3. Multiply two matrices by hand and explain what a matrix transpose and an inverse are.
4. Take the derivative of a simple function and explain what a gradient is.
5. Explain, in a sentence or two, how a neural network learns from labeled data.

If C++ is your gap, our Udacity C++ Nanodegree review covers Udacity’s 42-hour program, which is the usual preparation for the C++ projects here. If Python is your gap, the AI Programming with Python Nanodegree teaches Python, NumPy, pandas and the basics of neural networks. Stanford’s free CS231n course notes are a good primer on convolutional networks.

5 Benefits of joining this Self Driving Car Engineer Program from Udacity

1. It covers the whole stack. Few online programs take you from perception through sensor fusion, localization and planning to control in one sequence. That breadth is what makes it useful for engineers moving into autonomy from adjacent fields.
2. The projects use real data. Two projects are built on the Waymo Open Dataset, so you work with the kind of camera and lidar data perception teams use, not toy images.
3. Most of the later work is in C++. Localization, planning and control are written in C++, the language most production autonomy stacks run on. That is harder than Python, and it is the right skill to practice.
4. Every project is reviewed. A human reviewer grades each submission against a rubric and returns line-level feedback, which is what the subscription buys you over free lecture videos.
5. It counts toward a master’s. Udacity’s page says the program can earn credit hours toward its accredited MSc in AI, which gives the work a second use if you continue.

And now the Downsides

• Simulator and workspace problems. Recent reviews on Udacity’s own page report CARLA simulator failures and an object detection project that needs updating. Budget time for setup issues, and contact support early if a workspace will not run.
• A generalist credential in a specialist market. Autonomy employers usually hire perception, planning or controls specialists. You will need to go deeper in one area after the program.
• Heavy prerequisites. The program assumes C++ and math that many learners do not have, and the subscription keeps charging while you catch up.
• Lower rating than its siblings. At 4.2 from 46 reviews it trails Udacity’s Sensor Fusion (4.8), Flying Car (4.7) and Introduction to Self-Driving Cars (4.6) programs on the same day.
• A shrinking job market for generalists. Several well-funded autonomy companies closed or merged in the 2020s, and hiring concentrated in the remaining large players. Udacity’s page shows salary estimates for these roles but does not name a source, so we do not repeat them.

Compare plans for the Self-Driving Car Nanodegree →

Pros and Cons of Self Driving Car Nanodegree

✓Pros:

• Covers perception, sensor fusion, localization, planning and control in one program
• Projects on the Waymo Open Dataset and the CARLA simulator
• Substantial C++ practice in the later projects
• Lessons from Sebastian Thrun and engineers with Waymo and Mercedes-Benz backgrounds
• Eligible for credit toward Udacity’s MSc in AI

✘Cons:

• Advanced prerequisites in Python, C++ and math
• Recent reports of simulator failures and a dated object detection project
• Lower learner rating (4.2) than Udacity’s other autonomy programs
• No job guarantee and no published placement data
• At $249 a month, slow progress gets expensive

Conclusion:

The Self-Driving Car Engineer Nanodegree is still the most complete single online program on autonomous-vehicle software, and its current projects (Waymo data, lidar detection, scan matching, C++ planning and control) are a real improvement on the 2017 lane-line exercises. It is a good fit for a working engineer who already has the prerequisites and wants a structured, reviewed tour of the field.

Go in with clear expectations. Plan three to four months, expect some simulator trouble, and treat the six projects as the start of a portfolio rather than the end of one. If you lack the C++ or math, start with the Intro program or the C++ Nanodegree first.

Enroll in the Self-Driving Car Engineer Nanodegree →

Other Related Nanodegree Programs

Levels, hours and ratings are from each program’s Udacity page on 13 October 2026. All are included in the same subscription, so taking the C++ or Intro program first does not add a separate fee, only extra months.

See the Intro to Self-Driving Cars program · See the Flying Car program · See the Robotics Software Engineer program

FAQ’s

Will this Nanodegree help me land a job?

It can help, but it will not do it on its own. The program gives you six substantial portfolio projects on real autonomous-vehicle problems (Waymo data, lidar point clouds, scan matching, planning and control), and that is useful evidence in an interview. Autonomous-vehicle teams hire mostly experienced engineers and graduates with relevant degrees, so treat the Nanodegree as a way to build and show skills, not as a credential that gets you hired. Udacity does not publish a placement rate for this program.

What will I learn from this self-driving car nanodegree?

Five technical areas, in Udacity’s order: computer vision with deep learning (object detection on the Waymo Open Dataset), sensor fusion (lidar, Kalman and extended Kalman filters, multi-target tracking), localization (Markov localization, ICP and NDT scan matching in the CARLA simulator), planning (behavior planning, trajectory generation, motion planning) and control (PID control and trajectory tracking). Optional extra content covers unscented Kalman filters, prediction, model predictive control, the Point Cloud Library, functional safety (ISO 26262) and interview practice.

What are the alternatives to the self driving car nanodegree?

The closest alternative is the University of Toronto Self-Driving Cars Specialization on Coursera: 4 courses, rated 4.7 from 3,561 reviews, about 3 months at 10 hours a week (Coursera, 13 October 2026). It is cheaper and more academic, with less code review. Inside Udacity, the Introduction to Self-Driving Cars Nanodegree (74 hours) is the on-ramp for people who lack the prerequisites, and the Sensor Fusion Nanodegree (63 hours) goes deeper on one module.

Is the Udacity self-driving car certificate recognized by employers?

It is a Udacity credential, not an academic degree. Employers in autonomy look at what you built and how well you can explain it, so the projects matter more than the certificate. Udacity’s program page does say the Nanodegree can earn credit hours toward its accredited MSc in AI, which may matter if you plan to continue to a master’s.

Frequently asked questions

How long does the Self-Driving Car Engineer Nanodegree take?

Udacity estimates 78 hours of content. Older graduates describe 15 to 20 hours a week on projects, so plan on 3 to 4 months at about 10 hours a week, longer if the C++ or deep learning parts are new to you.

How much does it cost?

On 13 October 2026 Udacity charged $249 a month, $846 for a four-month prepaid plan (about $212 a month), or $999 to buy just this program, with a banner offering 40% off for new members. The subscription includes Udacity’s other programs.

What background do I need?

Udacity lists intermediate and object-oriented Python, Python data libraries and matplotlib, C++ proficiency, linear algebra and basic calculus including derivatives. The localization, planning and control projects are written in C++, so weak C++ is the most common reason people stall.

Is it still the old version with lane lines and Carla the real car?

No. The current program (last updated 14 January 2026) dropped the 2017-era lane-line, traffic-sign, behavioral-cloning and Carla real-car capstone projects. The projects now use the Waymo Open Dataset and the CARLA simulator, which is a different thing from Udacity’s old car named Carla.

Can I get a refund?

Udacity’s Terms of Use (updated 16 January 2026) allow a full refund if you cancel within seven days of your first purchase. After that, fees are not refunded, and canceling a monthly plan keeps access until the end of the paid month.

Is the Self-Driving Car Engineer Nanodegree hard?

Yes. Udacity labels it Advanced, and its own reviews describe it as intense. The hardest parts for most learners are the C++ localization and planning projects and keeping the simulator workspaces running; one 2023 reviewer gave it one star because of repeated CARLA simulator failures.

What Changed Since the 2017 Version

Many reviews online, including the older version of this page, still describe the original program. If a review mentions any of the left-hand items, it is describing a version you cannot buy.

Alternatives Outside Udacity

Coursera figures are from each program’s page on 13 October 2026. Coursera links are affiliate links; CARLA and the Waymo Open Dataset pay us nothing.

A 14-Week Study Plan

This assumes 8 to 10 hours a week. If you are aiming at perception, spend the spare weeks on the optional deep learning content; if you are aiming at planning or controls, take the model predictive control module.

What the Career Data Says

The U.S. Bureau of Labor Statistics does not track self-driving car engineers as a separate occupation; most of these roles fall under software developers, whose median pay was $135,980 in May 2025. Autonomy roles at the large players are usually advertised for engineers with several years of experience or a graduate degree in robotics, computer vision or controls. That is why graduates of every version of this program say the same thing: the Nanodegree is a strong way to learn the field and build projects, and you still need depth in one area and relevant experience to be hired.

Earlier versions of this page quoted a six-figure average salary from a salary website that no longer exists, and a list of hiring partners from 2017. We have removed both because neither can be checked today.

Related Udacity Guides: Every Udacity Nanodegree in 2026 (Hub) | Best Udacity Courses | Udacity Robotics Software Engineer Review | Udacity Flying Car Nanodegree Review

Related: for the platform-wide verdict (cost, project reviews and who Nanodegrees suit), read our complete Udacity Nanodegree review (2026). Learn more about who we are.

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