This post — based on research we’ve just published* — describes one exploration to push the boundaries of how we can employ expert data to create a neural network that is not only able to drive the car in challenging situations in simulation, but also reliable enough to drive a real vehicle at our private testing facility. As described below, simple imitation of a large number of expert demonstrations is not enough to create a capable and reliable self-driving technology. Instead, we’ve found it valuable to bootstrap from good perception and control to simplify the learning task, to inform the model with additional losses, and to simulate the bad rather than just imitate the good.
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