By Mayank Bansal and Abhijit Ogale — Waymo Research

At Waymo, we are focused on building the world’s most experienced driver. And just like any good driver, our vehicle needs to perceive and understand the world around it by recognizing surrounding objects and predicting what they might do next, before deciding how to drive safely while obeying the traffic rules.

In recent years, the supervised training of deep neural networks using large amounts of labeled data has rapidly improved the state-of-the-art in many fields, particularly in the area of object perception and prediction, and these technologies are used extensively at Waymo. Following the success of neural networks for perception, we naturally asked ourselves the question: given that we had millions of miles of driving data (i.e., expert driving demonstrations), can we train a skilled driver using a purely supervised deep learning approach?

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.