reed.co.uk Courses

Header

Self Driving Car Engineer Nanodegree - With Technical mentor support

Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world.


Udacity

Summary

Price
Save 50%
£890 inc VAT (was £1,788)
Offer ends 25 February 2020
Study method
Online, self-paced
Duration
6 Months
Qualification
No formal qualification
Additional info
  • Tutor is available to students

Add to basket or enquire

Overview

The Self-Driving Car Engineer Nanodegree program is one of the only programs in the world to both teach students how to become a self-driving car engineer, and support students in obtaining a job within the field of autonomous systems. The program’s nine projects equip students with invaluable skills across a wide array of critical topics, including deep learning, computer vision, sensor fusion, localization, controllers, vehicle kinematics, automotive hardware, and more. As part of their capstone project, students have the rare opportunity to run their code on an actual autonomous vehicle owned by Udacity.

Course media

Resources

  • Self Driving Car Detail Syllabus - download

Description

"As a self-driving car engineer, you have the potential to help save over 1 million people per year!"

Our wide-ranging curriculum will prepare you for a variety of roles in the autonomous vehicle industry, including: System Software Engineer, Deep Learning Engineer, Vehicle Software Engineer, Localization and Mapping Engineer and many others. If you elect to work outside of automotive engineering, your foundation in deep learning and robotics will enable you to fill any number of related roles in artificial intelligence, computer vision, machine learning, and more.

Modules:

  • Introduction

    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.


    FINDING LANE LINES ON THE ROAD ADVANCED LANE FINDING
  • 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!


    TRAFFIC SIGN CLASSIFIERBEHAVIORAL CLONING
  • 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.


    EXTENDED KALMAN FILTERS
  • Localization

    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.


    KIDNAPPED VEHICLE
  • Planning

    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.


    HIGHWAY DRIVING
  • Control

    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.


    PID CONTROLLER
  • 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!


    PROGRAMMING A REAL SELF-DRIVING CAR

Who is this course for?

Our wide-ranging curriculum will prepare you for a variety of roles in the autonomous vehicle industry, including: System Software Engineer, Deep Learning Engineer, Vehicle Software Engineer, Localization and Mapping Engineer and many others. If you elect to work outside of automotive engineering, your foundation in deep learning and robotics will enable you to fill any number of related roles in artificial intelligence, computer vision, machine learning, and more.

Requirements

Students should have prior experience with the following:

  • Intermediate Python or C++
  • Basic Linear Algebra
  • Basic Calculus
  • Basic Statistics
  • Basic Physics

You will also need to be able to communicate fluently and professionally in written and spoken English.

Questions and answers

Currently there are no Q&As for this course.Be the first to ask a question.

Rating and reviews

Currently there are no reviews for this course. Be the first to leave a review.

Leave a review

Modals

Mobile Navigation