Image annotation is an interesting and surprising problem that many autonomous vehicle researchers are struggling with.
The issue is that its easy to send a car and a camera out into the world to collect data, but its time-consuming and expensive to label that data.
And the labeling is necessary in order to train the machine to read the data.
Think about lane lines, for example. Lots of companies can now capture millions of images of roads, in all sorts of conditions. But in order to find the lane lines, the computer models have to be trained. And training the models involves telling them where the lane lines are in the sample images.
Lots of academic researchers use Amazon’s Mechanical Turk, a job system where cheap workers overseas can pick up manual tasks from companies in the US and elsewhere. But that is both expensive – even the world’s cheapest workers become expensive when asked to perform billions of tasks – and slow.
There doesn’t seem to be a solution yet to this problem.