Humans are great pattern recognizers and fickle gamers would cry foul at anything that doesn’t analog real life. The virtual world inside GTA V is fantastically realistic.
But those earliest drivers have already spent a lifetime observing the real world and watching parents drive. It’s pretty easy to teach the “rules of the road” - we do with 16-year-olds all the time. In addition to scaling up the amount of data, researchers can manipulate weather, traffic, pedestrians and more to create complex conditions with which to train AI. The idea is this: the virtual world provides a far more efficient solution to supplying enough data to these programs compared to the time-consuming task of annotating object data from real-world images. The hard problem with this approach is getting a large enough sample for the machine learning to be viable. So, why not use the wildly successful virtual world of Grand Theft Auto V to teach machine learning programs to operate a vehicle? When it comes to AI, teaching machine learning algorithms how to drive in a virtual world makes sense when the real one is packed full of squishy humans and other potential catastrophes. For all the complexity involved in driving, it becomes second nature to respond to pedestrians, environmental conditions, even the basic rules of the road.