Udacity Self-Driving Car Engineer Nanodegree program students are taking their newly-mastered skills into the broader world, and their projects are incredible!
The talent and passion of students in Udacityâs Self-Driving Car Engineer Nanodeegree Program regularly astounds me. Here are five independent projects that students did outside of the program to build their skills as autonomous vehicle engineers.
Check out the autonomous hardware package strapped to the top of this tiny red range rover! And the various test track configurations it navigates. Super cool.
Spatial Transformers are modules that can be inserted into convolutional neural network architectures to focus the network on the most important object in the image. This is helpful because scale and rotation make object localization (finding an object within an image) a complex problem.
âThe STN is a differentiable module which can be injected in a convolutional neural network. The default choice is to place it right âafterâ the input layer to make it learn the best transformation matrix theta which minimizes the loss function of the main classifier (in our case, this is IDSIA).â
This is an awesome four-part series on building a miniature self-driving car from scratch, with a big emphasis on hardware and electrical engineering. Part 1 is ROS setup, Part 2 is the sensor suite, Part 3 is the microcontroller, and Part 4 is working with the NVIDIA Jetson TX1. This is quite the hacker project.
âThe goal of this project is to build an autonomous base that can navigate the sidewalks of my subdivision. It will use GPS, LIDAR, and other sensors to navigate to GPS way points, avoid obstacles, and return to the start position.â
I love watching videos that students shoot themselves. Here Karol is applying a Single-Shot Detector (SSD) network to identify other vehicles on the road.
Scaling up from miniature self-driving cars to human-sized self-driving cars, Bogdan outlines a self-driving car development platform accessible for under US$10,000. This does not include the sensor suiteâââjust the drive-by-wire platform. He settled on the Renault Twizy and is looking for partners to work on this with him đ
âOne of the main challenges in the self-driving car industry (among other things like technology itself, policy updates, ethical issues, etc.) is the barrier of entry. If you are a small start up building a local autonomous delivery service or a single engineer trying out latest deep learning approaches for car/traffic sign detection, it is incredible hard (sometimes even impossible) to get things off the ground and test your solution in the real world setting.â
TensorFlow is Googleâs library for deep learning, and one of the most popular tools for building and training deep neural networks. In the previous lesson, MiniFlow, students build their own miniature versions of a deep learning library. But for real deep learning work, an industry-standard library like TensorFlow is essential.
Students learn the differences between regression and classification problems. Then they to build a logistic classifier in TensorFlow. Finally, students use fundamental techniques like activation functions, one-hot encoding, and cross-entropy loss to train feedforward networks.
Most of these topics are already familiar to students from the previous âIntroduction to Neural Networksâ and âMiniFlowâ lessons, but implementing them in TensorFlow is a whole new animal. This lesson provides lots of quizzes and solutions demonstrating how to do that.
Towards the end of the lesson, students walk through a quick tutorial on using GPU-enabled AWS EC2 instances to train deep neural networks. Thank you to our friends at AWS Educate for providing free credits to Udacity students to use for training neural networks!
Deep learning has been around for a long time, but it has only really taken off in the last five years because of the ability to use GPUs to dramatically accelerate the training of neural networks. Students who have their own high-performance GPUs are able to experience this acceleration locally. But many students do not own their own GPUs, and AWS EC2 instances are a cloud tool for achieving the same results from anywhere.
The lesson closes with a lab in which students use TensorFlow to perform the classic deep learning exercise of classifying characters: âAâ, âBâ, âCâ and so on.
Mithi published a three-part series about what she calls âthe most difficult project yetâ of the Nanodegree Program. In Part 1, she outlines the goals and constraints of the project, and decides on how to approach the solution. Part 2 covers the architecture of the solution, including the classes Mithi developed and the math for trajectory generation. Part 3 covers implementation, behavior planning, cost functions, and some extra considerations that could be added to improve the planner. This is a great series to review if youâre just starting the project.
âI decided that I should start with a simple model with many simple assumptions and work from there. If the assumption does not work then I will then make my model more complex. I should keep it simple (stupid!).
A programmer should not add functionality until deemed necessary. Always implement things when you actually need them, never when you just foresee that you need them. A famous programmer said that somewhere.
My design principle is, make everything simple if you can get away with it.â
Mohan takes a different approach to path planning, in which he combines a cost function with a feasibility checklist. He builds a cost function and then ranks each lane by how it does on a cost function. Then he decides whether to move to a lane based on the feasibility checklist.
âThis comes down to two things (and Iâm going to be specific to highway scenario).
Estimating a score for each lane, to determine the best lane for us to be in (efficiency)
Evaluating the feasibility of moving to that lane in the immediate future (safety & comfort)â
The 11th post in Andrewâs series on the Nanodegree Program covers Term 3 broadly and path planning specifically. In particular, Andrew lays out where this path planning project falls in the taxonomy of autonomous driving, and the high-level inputs and outputs of a path planner. This is a great post to review if youâre interested in what a path planner does.
âI found the path planning project challenging, in large part due to fact that we are implementing SAE Level 4 functionality in C++ and the complexity that comes with the interactions required between the various modules.â
These examples make clear the vision, skill, and tenacity our students are applying to even the most difficult challenges, and itâs a real pleasure to share their incredible work. It wonât be long before these talented individuals graduate the program, and begin making significant, real-world contributions to the future of self-driving cars. I know I speak for everyone at Udacity when I say that Iâm very excited for the future theyâre going to help build!
Are you trying to decide which Udacity Nanodegree Program you should enroll in? Hereâs an all-in-one guide to help you determine which program is best for you.
Partner:Google Lead Instructors:Katherine Kuan, Chris Lei Difficulty:Beginner Time:6 months Syllabus:User Interface + User Input + Multiscreen Apps + Networking + Data Storage Prerequisites: None! Cost: $199 / month Best For:Aspiring Android Developers with no programming experience.
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Partners:AT&T, Lyft, Google Difficulty: Intermediate Time: 6 months Syllabus: UIKit Fundamentals + iOS Networking with Swift + iOS Persistence and Core Data + How to Make an iOS App Prerequisites: macOS 10.12 or OS X 10.11.5 Cost: $199 / month Best For:Beginners who want to launch their iOS developer careers.
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Lead Instructors: Ian Goodfellow, Andrew Trask, Mat Leonard Difficulty: Intermediate Time: 6 months Syllabus: Introduction + Neural Networks + Convolutional Neural Networks + Recurrent Neural Networks + Generative Adversarial Networks Prerequisites: Python, basic linear algebra and calculus Best For:Students excited by the potential for deep learning to change the world, and who additionally wish to earn guaranteed entry into Udacityâs Artificial Intelligence, Robotics, or Self-Driving Car Engineer Nanodegree Programs (a special âperkâ of the program for graduates!).
Digital Marketing
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David Silver 