Udacity Self-Driving Car students have been writing about the Self Racing Cars track day, the Didi Challenge, and building their own deep learning machines!
Self Racing Cars 2017 Photo Gallery — The Day Before
Udacity students were sponsored by PolySync to compete in the Self-Racing Cars track day at Thunderhill last weekend, and these photos show what it was like!
Self Racing Cars 2017 Photo Gallery — Day 1
Self Racing Cars 2017 Video Gallery — Shot on iPhone 6
Deep Learning PC Build
Here’s how Tim built his own GPU-enabled deep learning machine. He provides helpful instructions, a bill of materials, links to graphs comparing the value of different NVIDIA GPUs.
“The GPU is the main component of our system, and hopefully comprises a significant fraction of the cost of the system. ServeTheHome has a nice article in which they show the following graph of GPU compute per unit price.”
part.1: Didi Udacity Challenge 2017 — Car and pedestrian Detection using Lidar and RGB
This is one student’s journal of tackling the Udacity-Didi Challenge. Pay attention to the different neural network architectures he uses!
“Just from these 2 simple steps, I observed the following possible issues:
Small object detection. This is a well-known weakness in the original plain faster rcnn net.
Creation of 2d top view image could be slow. There are quite a number of 3d points needs to be processed
Now that I am sure that the implementation is correct, the next step will be to start training with the actual dataset, which contains many images.”