"Ultimately, actually designing all the different heuristics for when it's okay to lane change is actually a little bit intractable, I think, in the general case. And so ideally, you actually want to use fleet learning to guide those decisions. So, when do humans lane change, in what scenarios? And when do they feel it's not safe to lane change? And let's just look at a lot of the data and train machine learning classifiers for distinguishing when it is safe to do so. And those machine learning classifiers can can write much better code than humans because they have the massive amount of data backing that. So, they can really tune all the right thresholds and agree with humans and do something safe."​

Question asker: "...in terms of platooning, do you think the system is geared? Because somebody asked about when there is snow on the road, but if you have platooning feature, you can just follow the car in front. Is your system capable of doing that?"



Andrej Karpathy: "So, you're asking about platooning. So, I think, like, we could absolutely build those features. But, again, if you just train neural networks, for example, on imitating humans, humans already, like, follow the car ahead. And so that neural neural network actually incorporates those patterns internally. It figures out that there's a correlation between the way the car ahead of you faces and the path that you are going to take. That's all done internally in the net. So, you're just concerned with getting enough data and tricky data. And the neural network training process, actually, it's quite magical, does all the other stuff automatically. So, you turn all the different problems into just the one problem: just collect your data set and use neural network training."​