Driving in San Francisco is hard. Teaching an AV to drive the city’s chaotic streets is even harder. In fact, an AV driving the streets of SF experiences a challenging situation 46x more often than in suburban areas. One of the roots of this challenge is prediction: Will that pedestrian cross the road in front of us? Is that car about to cut us off? Will the car in front of us run the yellow light or hit the brakes?
The difficulty of prediction can be deceivable. One can write a single rule, i.e. cars follow their current lanes, and be correct most of the time. However, the real challenge is to predict the long tail, which are the events that the AV sees very infrequently (and are often the most dangerous): U-turns or K-turns, bicycles swerving into traffic, or pedestrians jaywalking unexpectedly.
At Cruise, we have built a Continuous Learning Machine in order to predict the unpredictable. This session will go inside the inner workings of Cruise’s Continuous Learning Machine and explore how generalizing across the long tail is the foundation of solving all machine learning problems. Tianshi will cover how self-supervised learning addresses dataset imbalance challenges by automatically labeling data and sampling error situations, as well as the testing and metrics pipelines needed to ensure new models exceed performance and generalize well to the nearly infinite variety of scenarios– all without requiring human intervention!