Best Self Driving Courses

Last updated: October 2026. Written by Josh Hutcheson. Every course below was opened on its platform on 14 October 2026 to confirm it is still sold and to read its rating, length and last update. We also read Reddit threads from learners and engineers who took these programs. See our review methodology. If you enroll through our links we may earn a commission, which never changes the ranking.

Self-driving car engineering combines computer vision, sensor fusion (camera, lidar and radar), localization, motion planning and vehicle control. Online courses cannot replace an engineering degree for most roles in the field, but they are the most direct way to learn how a full autonomous-vehicle software stack fits together and to build projects you can show an employer. Udacity’s program page lists Waymo, Zoox, Tesla, Aurora, Nuro, Motional and Nvidia among the companies hiring for these skills.

We compared the four courses below on depth, how current they are, the projects you build, and price. They suit different starting points: the Udemy courses for a first hands-on taste, Coursera for a rigorous university treatment, and Udacity for an advanced, project-heavy program.

Best Self-Driving Car Courses at a Glance

See the Self-Driving Cars Specialization on Coursera →

1. Self-Driving Cars Specialization (Coursera)

Built by the University of Toronto and taught by Steven Waslander, this four-course specialization is the most rigorous self-driving curriculum you can take online. More than 86,000 learners have enrolled, and the courses average 4.7 from 3,561 reviews. You work in the open-source CARLA simulator with a full simulated sensor suite: camera, lidar, GPS/INS, wheel odometry, depth maps and semantic segmentation.

Who it is for: engineers and STEM graduates. Coursera rates it advanced and recommends a background in linear algebra, probability, calculus, physics, control theory and Python. CARLA also needs a capable computer: Coursera lists a quad-core processor, 8 GB of RAM and a dedicated graphics card.

Time and cost: about three months at 10 hours a week. You can enroll in the first course for free to preview it; the graded assignments and the University of Toronto certificate need a Coursera subscription, and the specialization is included in Coursera Plus.

Start the Self-Driving Cars Specialization →

2. Udacity School of Autonomous Systems

Udacity’s School of Autonomous Systems sets out a three-step path for the job title: Introduction to Self-Driving Cars (74 hours), C++ (42 hours), then the advanced Self Driving Car Engineer Nanodegree (78 hours). The flagship Nanodegree was updated on 14 January 2026 and has six graded projects, each reviewed by a mentor:

• Object detection in an urban environment, training a CNN on the Waymo Open Dataset
• 3D object detection from lidar point clouds
• Sensor fusion and multi-object tracking with an Extended Kalman Filter
• Scan-matching localization in a 3D lidar map (ICP and NDT)
• Motion planning and decision making for urban routes
• A C++ PID controller for trajectory tracking

Who it is for: working engineers moving into autonomous vehicles. Udacity lists intermediate Python, C++ proficiency, calculus and derivatives among the prerequisites. If you are not there yet, start with Introduction to Self-Driving Cars, which covers the probability, localization and C++ basics first.

Related programs: the Sensor Fusion Nanodegree (63 hours, 4.8 from 186 reviews, updated August 2026), built with Mercedes-Benz engineers, goes deeper on lidar, camera and radar. Our Sensor Fusion Nanodegree review covers it in detail.

Price: Udacity sells access by subscription; on 14 October 2026 its pricing page offered a 4-month bundle for $846, after which plans go month to month. For the full program breakdown, read our Self Driving Car Engineer Nanodegree review and our Introduction to Self-Driving Cars review.

See the Self Driving Car Engineer Nanodegree →

3. Applied Deep Learning: The Complete Self-Driving Car Course (Udemy)

Now titled The Complete Self-Driving Car Course – Applied Deep Learning, Rayan Slim’s course is the most accessible starting point here: 18 hours and 169 lectures, rated 4.7 from 4,319 ratings by more than 27,000 students, and last updated in February 2026. It assumes no prior programming or math experience and builds up to training a neural network that steers a car in a simulator.

What you will learn: neural networks from the perceptron up, convolutional networks in Keras, lane-line detection with computer vision, a traffic-sign classifier for 43 sign types, and a deep learning model that drives a simulated car. Who it is for: beginners who want to see whether the field interests them before committing to a longer program. It is about deep learning for driving, not the full stack: localization, planning and control are not its focus.

See The Complete Self-Driving Car Course on Udemy →

4. Autonomous Cars: Deep Learning and Computer Vision in Python (Udemy)

Where the previous course is an end-to-end introduction, this one concentrates on perception, the part of the stack that turns camera images into an understanding of the road. It is taught by Frank Kane, Ryan Ahmed and Mitchell Bouchard of Sundog Education, rated 4.5 from 1,552 ratings, has nearly 14,000 students, and was updated in July 2026.

What you will learn: image processing in OpenCV, camera calibration, edge detection, lane-marking detection, car and pedestrian detection with HOG features and an SVM classifier, template matching, and traffic-sign classification with convolutional networks in TensorFlow and Keras. Who it is for: Python developers who want to specialize in perception, or anyone adding a computer-vision layer to the Coursera or Udacity programs. For broader computer-vision options, see our best computer vision courses.

See Autonomous Cars: Deep Learning and Computer Vision on Udemy →

How to Choose the Right Self-Driving Car Course

• New to the field: start with The Complete Self-Driving Car Course on Udemy, or enroll free in Coursera’s Introduction to Self-Driving Cars to preview the first university course.
• Engineering or CS background: the University of Toronto specialization gives you the theory employers expect for localization, perception and planning.
• Ready to build a portfolio: Udacity’s Nanodegree gives you six reviewed projects on real datasets, which are easier to show an employer than a certificate.
• Targeting one part of the stack: pick by specialty: computer vision (course 4), sensor fusion (Udacity Sensor Fusion), or planning and control (Coursera courses 3 and 4).

Prerequisites You Should Have

• Python: every course here uses it. If you need a refresher, see our free Python courses.
• Linear algebra, calculus and probability: matrices, derivatives and Bayes’ rule appear in localization, Kalman filters and planning.
• Machine learning basics: neural networks, gradient descent and training pipelines. Our machine learning courses list has good starting points.
• C++ for Udacity: the Nanodegree’s localization and control projects are in C++, which is also the language most production AV software runs in.

Frequently Asked Questions

What programming language is used in self-driving car development?

Python is used for prototyping, data work and deep learning models. C++ is used in production systems where real-time performance matters, such as sensor processing, localization and control. Most online courses teach Python; industry roles usually want both.

Can I get a job in autonomous vehicles with an online course?

An online course on its own rarely lands a role at a major AV company. Engineers in the field on Reddit point out that most hires have an engineering or computer science degree, often a master’s, plus deep skill in one area such as perception or sensor fusion. Courses help by teaching the stack and giving you projects to show; combine them with your own projects and open-source work on Autoware or Apollo.

How long does it take to learn self-driving car technology?

A Udemy course takes about 18 hours. The Coursera specialization takes about three months at 10 hours a week (around 125 hours). Udacity lists 78 hours for its Nanodegree. Reaching job-ready skill usually takes six to twelve months of study and projects for someone who already has an engineering background.

Is deep learning necessary for self-driving cars?

For perception, yes: object detection, lane recognition and sign classification rely on deep learning. Localization, mapping, planning and control still rely heavily on classical methods such as Kalman filters and search algorithms, so a well-rounded engineer needs both.

Is there a free self-driving car course?

You can enroll free in Coursera’s Introduction to Self-Driving Cars to preview it, and the CARLA simulator, Autoware and Apollo are free and open source with their own documentation. Udacity and Udemy courses are paid.

Is the Udacity Self-Driving Car Nanodegree still available?

Yes. On 14 October 2026 Udacity sold the Self Driving Car Engineer Nanodegree (78 hours, last updated January 2026) as the third step in its School of Autonomous Systems, after Introduction to Self-Driving Cars and C++.

Which is better, Coursera or Udacity for self-driving cars?

Coursera’s University of Toronto specialization is stronger on theory and carries a university certificate. Udacity’s Nanodegree is more applied, with six mentor-reviewed projects on the Waymo Open Dataset and C++ work. Theory-first learners should start with Coursera; portfolio-first learners with Udacity.

What hardware do I need for a self-driving car course?

The Udemy and Udacity courses run in the browser or on an ordinary laptop. Coursera’s CARLA projects need more: Coursera lists a quad-core processor, 8 GB of RAM and a dedicated graphics card.

Free Self-Driving Car Resources

Once you know the basics, these free open-source projects are where people build portfolio work:

• CARLA: an open-source driving simulator for training and testing autonomous systems, used in the Coursera specialization. Its latest releases include a version built on Unreal Engine 5.5, and a ROS bridge connects it to robotics software.
• Autoware: an open-source autonomous-driving software stack built on ROS, hosted by the Autoware Foundation. Contributing to it is a credible portfolio project.
• Baidu Apollo: an open autonomous-driving platform on GitHub whose versions run from GPS waypoint following up to driving on urban roads.
• Waymo Open Dataset: large real-world camera and lidar datasets, the same data Udacity’s detection projects use.
• Duckietown: a hands-on robotics and vehicle-autonomy curriculum that grew out of an MIT class.

Is a Self-Driving Car Course Worth It in 2026?

It depends on your goal. Reddit threads from people working in the industry make the same two points. First, the courses are good learning resources, but real AV work is more complicated: one engineer with five years in the field said they had used little of the course material directly, and that systems engineering, safety and controls knowledge mattered more. Second, the courses do not replace a degree for core autonomy roles. Where they pay off is for engineers who already have a technical background and want to move into perception, sensor fusion, simulation, testing or tooling, and for anyone working near AVs who needs to understand how the stack works.

Our advice: spend a weekend on the free Coursera preview or the Udemy course first. If you still want more, choose Coursera for depth of theory or Udacity for projects, and build at least one project of your own in CARLA or on the Waymo Open Dataset.