Learning to Drive by Observing the Best and Synthesizing the Worst

ICLR 2019 Mayank BansalAlex KrizhevskyAbhijit Ogale

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough... (read more)

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