Deep neural networks actually have quite a long history of powering self-driving cars. Way back in the 1980s, researchers at CMU used a basic two-layer neural network to power a truck.
We almost covered ALVINN (Autonomous Land Vehicle in a Neural Network) within the Deep Neural Networks module of the Udacity Self-Driving Car Nanodegree Program, but we cut it for time.
Recently, Udacity’s work on neural networks and self-driving cars has reminded people about what a breakthrough ALVINN was.
This proto-driverless vehicle [ALVINN] came up recently in a Twitter discussion between two engineers: Oliver Cameron, who heads an open-source self-driving car project at Udacity, and Dean Pomerleau, a CMU professor who ran the self-driving car project that gave birth to ALVINN. Cameron tweeted a video shared by some of his students of a car steering itself autonomously using only a camera.
This prompted Pomerleau to ask a few questions about deep learning and neural networks. After some back and forth, Pomerleau brought up ALVINN, which had an operating system of 100 million floating point-operations per second, or about one-tenth the processing power of the Apple Watch. The vehicle’s CPU was the size of a refrigerator and was powered by a 5,000 watt generator, he added. Nonetheless, ALVINN was able to hit 70 mph by the early 1990s.
From The Verge. Read the whole thing.
David Silver 