No worries :) the drone basically generates a big dataset of "crashing" and "not crashing" video clips from the camera. It then feeds all that into a convolutional neural net, which can (after training is complete) give control decisions based on the camera which avoid obstacles. This is very "black box" in the sense that it's hard to say exactly how the system is working.

The car, on the other hand, uses hand written algorithms to forward simulate various controls. Based on the forward simulations, it can pick controls which are predicted to give good results. Forward simulation relies on a model of how the car reacts to any possible control. However, this model is complicated because of the nonlinear dynamics going on (inertia, wheel slip, etc). Therefore, they use ml techniques to identify the model.