One of the most fun things about the Udacity Self-Driving Car Engineer Nanodegree Program is the student community. Thousands of students are active on Slack and in the forums, helping each other complete projects, understand concepts, and get jobs in the autonomous vehicle industry.
Students also write online about their experiences in the program and what they’ve accomplished. Here are five, in their own words.
Behavioral Cloning for Autonomous Vehicles
It often pays to explore your data with relatively few constraints before diving in to build and train the actual model. One may gain insights that help guide you to better models and strategies, and avoid pitfalls and dead-ends.
A transforming reality — Udacity’s Self Driving Car Nano-degree
I was so much into this program that I started talking about it with senior folks at my workplace. They encouraged me, supported me and appreciated my love for the technology and research. They got interested in knowing more about the program. They got stunned about this state-of-the-art program and they wanted to help me pursue my dream of research. They extended a helping hand to me by arranging an online fundraiser to support me.
Studying for the Udacity SDCND, or How I got my law license
Udacity has a great program, but they refer you to other sources as you go, both inside and outside of Udacity. Hurrying through it won’t help you, because people need time to develop and strengthen the neural pathways that help you really learn something.
Why I enrolled in Udacity Self-Driving Car Classes
I’m currently in a very different industry than tech and automotive (I’m actually in the oil & gas industry). However I’ve always tried to apply latest innovations to assist me in my daily activities. This has helped me in automating the “boring tasks” to let me focus on more fun challenges, leading to new creative solutions.
Experiment Using Deep Learning to find Road Lane Lines
I modified a neural network that I had used in the SDCND BehavioralCloning lab (5 CNN layers followed by 3 FCNN layers), and added 5 new outputs to it. So now the network looks like 5 CNN layers with 6x 3 FCNN layers. The outputs are generating lane polynomial coefficients for both the left and right lanes, i.e. a*y² + b*y + c where I’m predicting a, b & c for each lane.