Kyle Vogt (GM/Cruise): Accelerating machine learning for driverless vehicles

[strangecosmos2]

 

[strangecosmos2]

Why scale of training data matters for autonomous driving, according to Kyle Vogt (13:45):

“The reason we want lots of data and lots of driving is to try to maximize the entropy and diversity of the datasets we have.”​

Kyle Vogt on automatic labelling or auto-labelling (22:27):

“What we’re doing today ... is a lot more auto-labelling. …basically, what I mean is you take the human labelling step out of the loop. …what I think is really interesting about driving is there’s a lot of things you can infer from the way a vehicle drives. If it didn’t make any mistakes, then you can sort of implicitly assume a lot of things were correct about the way that vehicle drove. … When the AVs are basically driving correctly and the people in the car are saying ‘you did a good job’, that, to me, is a very rich source of information.”​