Self-driving cars are transformational technology, on the cutting-edge of robotics, machine learning, and engineering. Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world.
Self-Driving Car Engineer
You'll first apply computer vision and deep learning to automotive problems, including detecting lane lines, predicting steering angles, and more. Next, you'll learn sensor fusion, which you'll use to filter data from an array of sensors in order to perceive the environment. Then, you'll work with a team to program Carla, Udacity’s real self-driving car.
6 months to complete
Students should have experience with Python, C++, Linear Algebra, and Calculus.
Learn about how self-driving cars work and about the services available to you as part of the Nanodegree program.
- Computer Vision
Use a combination of cameras and software to find lane lines on difficult roads and to track vehicles.
- Deep Learning
Deep learning has become the most important frontier in both machine learning and autonomous vehicle development. Experts from NVIDIA will teach you to build deep neural networks and train them with data from the real world and from the Udacity simulator. You’ll train convolutional neural networks to classify traffic signs, and then train a neural network to drive a vehicle in the simulator!
- Sensor Fusion
Tracking objects over time is a major challenge for understanding the environment surrounding a vehicle. Sensor fusion engineers from Mercedes-Benz will show you how to program fundamental mathematical tools called Kalman filters. These filters predict and determine with certainty the location of other vehicles on the road. You’ll even learn to do this with difficult-to-follow objects by using an extended Kalman filter, an advanced technique.
Localization is how we determine where our vehicle is in the world. GPS is only accurate to within a few meters. We need single-digit centimeter-level accuracy! To achieve this, Mercedes-Benz engineers will demonstrate the principles of Markov localization to program a particle filter, which uses data and a map to determine the precise location of a vehicle.
The Mercedes-Benz team will take you through the three stages of planning. First, you’ll apply model-driven and data-driven approaches to predict how other vehicles on the road will behave. Then you’ll construct a finite state machine to decide which of several maneuvers your own vehicle should undertake. Finally, you’ll generate a safe and comfortable trajectory to execute that maneuver.
Ultimately, a self-driving car is still a car, and we need to send steering, acceleration, and brake commands to move the car through the world. Uber ATG will walk you through building a proportional-integral-derivative (PID) controller to actuate the vehicle.
- System Integration
This is the capstone of the entire Self-Driving Car Engineer Nanodegree Program! We’ll introduce Carla, the Udacity self-driving car, and the Robot Operating System that controls her. You’ll work with a team of Nanodegree students to combine what you’ve learned over the course of the entire Nanodegree Program to drive Carla, a real self-driving car, around the Udacity test track! NOTE: Due to the ongoing coronavirus pandemic, capstone projects will be tested in simulation only - this will not affect the graduation process or requirements.