If you want to explore different areas of computer vision, you should check out these awesome posts by Udacity students on different ways to use OpenCV to find lane lines.
And if you want to learn about the Udacity Self-Driving Car Engineer Nanodegree Program, there’s a post on that, too!
Plus a post on world travel, for good measure 🙂
The Udacity Self Driving Car Nanodegree — Term 1
Upon completing all of his Term 1 projects, Arnaldo wrote a high-level overview of all of the projects, and reflected on what he learned:
“It had a very practical focus: theory enough to understand the core concepts, and then, the practical application. It is a reason why it requires a lot of background. It is not a course on basic python, or basic neural networks, but how to apply it in real cases.”
Advanced Lane Finding Project
Sujay has an incredibly thorough analysis of his computer vision pipeline for lane-finding, including a great debugging tool:
“This project involves fine tuning of lot of parameters like color thresholding, gradient thresholding values to obtain the best lane detection. This can be trickier if the pipeline fails for few video frames. To efficiently debug this I had to build a frame that captures multiple stages of the pipeline, like the color transformation, gradient thresholding, line fitting on present and averaged past frames.”
Self-Driving Car Engineer Diary — 6
Andrew has posts fun images from his lane-finding pipeline, but what really caught my eye was his analysis of Udacity’s Career Services:
“The SDC Engineer course emphasises job readiness and the Udacity team provides an excellent Careers Service built right in. We were asked to search for an advertised job that interested us, provide a resume tailored to either Entry Level, Prior Experience or Career Change and the associated Cover Letter. For a ‘career changer’ like me, I was surprised by the amount of self-reflection this caused.”
10 weeks, 3 countries and 5 self-driving car projects
Morgane is completing CarND while on a nine-month world tour, starting in Ecuador!
“Our next stop was Cuenca, Ecuador, where my fiance’s immediate family lives. I had an amazing time there, visiting the city and Cajas National Park. The only issue is that people did not understand why I was spending so much time on my laptop! They expected me to be free all the time since I was on holidays. I had set up a routine where I’d work for a few hours in the morning while waiting for people to get up and at night. After explaining to them what my goal was and showing them what I was doing, they were definitely more understanding. They got particularly interested when I showed them how I had trained a neural network to drive a car in a simulator, and how I used computer vision and machine learning to recognize lanes and other vehicles on the road.”
Temporal Smoothing to Remove Jitter in Detected Lane Lines
Liam took the introductory Udacity Lane-Finding Project and optimized it:
“I created a buffer to store the slope and y-intercept values for each line detected in the last N frames. The actual line drawn on the current frame is simply the average slope/intercept of all these lines. By continuously pushing the latest detected line onto this buffer and simultaneously dropping the oldest line, I can calculate a rolling mean of the lines over time, or what I call “temporal smoothing”.”