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Self Driving Car Engineer

Nanodegree Program

Work on the future of autonomous vehicles and help make the self-driving car revolution a reality!

Work on the future of autonomous vehicles and help make the self-driving car revolution a reality!

Built in collaboration with



5 months

Real-world Projects

Completion Certificate

Last Updated November 22, 2023

Skills you'll learn:
Robot behavior planning • Object motion models • Robot motion planning • Eigen library

No experience required

Courses In This Program

Course 1 1 hour

Welcome to Self Driving Car Engineer Nanodegree

Welcome to the Self-Driving Car Engineer Nanodegree program! Learn about the Nanodegree experience, as well as hear from Waymo, one of Udacity's partners for the program.

Course 2 4 weeks

Computer Vision

In this course, you will develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. With this course, you will be exposed to the whole Machine Learning workflow and get a good understanding of the work of a Machine Learning Engineer and how it translates to the autonomous vehicle context.

Course 3 4 weeks

Sensor fusion

Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability, using sensor fusion. You will learn about the lidar sensor, different lidar types, and relevant criteria for sensor selection. Also, you will learn how to detect objects in a 3D lidar point cloud using a deep-learning approach, and then evaluate detection performance using a set of metrics. In the second half of the course, you will learn how to fuse camera and lidar detections and track objects over time with an Extended Kalman Filter. You will get hands-on experience with multi-target tracking, where you will initialize, update and delete tracks, assign measurements to tracks with data association techniques, and manage several tracks simultaneously.

Course 4 4 weeks


In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. Then, you’ll move onto using Markov localization in order to do 1D object tracking. From there, you will learn how to implement two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data. Finally, you will utilize these scan matching algorithms in the Point Cloud Library (PCL) to localize a simulated car with lidar sensing, using a 3D point cloud map obtained from the CARLA simulator.

Taught By The Best

Photo of David Silver

David Silver

Staff Software Engineer at Kodiak Robotics

David works on Self-driving trucks at Kodiak. As a Staff Software Engineer, David writes planning, control, simulation and mapping software for self-driving trucks.

Photo of Thomas Hossler

Thomas Hossler

Sr Deep Learning Engineer

Thomas is originally a geophysicist but his passion for Computer Vision led him to become a Deep Learning engineer at various startups. By creating online courses, he is hoping to make education more accessible. When he is not coding, Thomas can be found in the mountains skiing or climbing.

Photo of Antje Muntzinger

Antje Muntzinger

Self-Driving Car Engineer

Antje Muntzinger is a technical lead for sensor fusion at Mercedes-Benz. She wrote her PhD about sensor fusion for advanced driver assistance systems and holds a diploma in mathematics. By educating more self-driving car engineers, she hopes to realize the dream of fully autonomous driving together in the future.

Photo of Andreas Haja

Andreas Haja


Andreas Haja is an engineer, educator, and autonomous vehicle enthusiast. Andreas now works as an engineering professor in Germany. Previously, he developed computer vision algorithms and autonomous vehicle prototypes using C++.

Photo of Aaron Brown

Aaron Brown

Senior Software Engineer

Aaron has over 7 years in the autonomous vehicle field, starting as a Udacity content developer then instructor, focusing on Lidar in the Sensor Fusion and Self-Driving Car Nanodegree Program. For the last 5 years, he's been with Mercedes-Benz R&D North America, working in functional testing, Sensor Fusion, and Machine Learning driven by a passion for technological evolution and problem-solving.

Photo of Munir Jojo Verge

Munir Jojo Verge

Lead Autonomous & AI Systems Developer at MITRE

Before MITRE, Munir was a Motion Planning & Decision-Making Manager at Amazon. He also worked for a 2 Self-driving car companies and for WaltDisney Shanghai building TronLightcycle. Munir holds a B.Eng. in Aerospace, a M.S. in Physics, and a M.S. in Space Studies.

Photo of Mathilde Badoual

Mathilde Badoual

Fifth year PhD student at UC Berkeley

Mathilde has a strong background in optimization and control, including reinforcement learning and has an engineering diploma from the electrical engineering school Supelec, in France. Previously she worked at Tesla in the energy and optimization team.

Ratings & Reviews

Average Rating: 4.2 Stars

(34 Reviews)

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About Self Driving Car Engineer

Udacity's Self-Driving Car Engineer Nanodegree program is an advanced self-driving car course designed for those aspiring to become leaders in autonomous vehicle technology. The program covers critical areas such as machine learning, computer vision, and sensor fusion, providing hands-on experience in building and testing self-driving car systems. Our expert instructors, drawn from leading companies in the autonomous vehicle industry, guide students through real-world projects. This offers a unique opportunity to apply learning in practical scenarios, preparing students for exciting careers in this innovative field.