Active Learning, Data Selection, Data Auto-Labeling, and Simulation in Autonomous Driving — Part 5
Let's go for another interesting company!
Cruise
Cruise also makes use of active learning. They refer to it as the Continuous Learning Machine (CLM).
Consider the task of prediction. The motion prediction model must be capable of handling both the nominal and longtail cases well. Here is the end-to-end motion prediction model that Cruise uses and announced in the Cruise Under the Hood event recently:
It is critical to note that while these longtail events do occur in the data collected on the road, they are extremely rare and infrequent. As a result, we concentrate on identifying the needle in the haystack of daily driving and use upsampling to teach the models about these events.
A naive approach to identifying rare events would be to manually engineer “detectors” for each of these longtail situations to assist in data sampling. For instance, we could create a “u-turn” detector that generates sample scenarios whenever it is triggered. This approach would enable us to collect targeted data, but quickly fails when scaling up, as it is impossible to write a detector that is sufficiently specific for each unique longtail situation.
