@Scale 2019: Unique challenges and opportunities for self-supervised learning in autonomous driving

Autonomous vehicles generate a lot of raw (unlabeled) data every minute. But only a small fraction of that data can be labeled manually. Ashesh focuses on how we leverage unlabeled data for tasks on perception and prediction in a self-supervised manner. He touches on a few unique ways to achieve this goal in the AV land, including cross-modal self-supervised learning, in which one modality can serve as a learning signal for another modality without the need for labeling. Another approach he touches on is using outputs from large-scale optimization as a learning signal to train neural networks, which is done by mimicking their outputs but running in real-time on the AV. Ashesh further explores how we can leverage the Lyft fleet to oversample the long tail events and, hence, learn the long tail.