Using sliced data to evaluate machine learning models used for autonomous vehicle operation

Abstract

Machine learning models for controlling AV operations may be evaluated based on sliced data. A feature category critical for an environmental condition may be identified. The environmental condition is a condition in an environment where an AV performs an operation. A sub-dataset, which comprises sensor data capturing the environmental feature dataset, may be extracted from a dataset that comprises sensor data capturing the environment. The sub-dataset may be input into machine learning models that can classify the feature category. Performances of the machine learning models can be evaluated based on their outputs, which are generated based on the sub-dataset, and a ground-truth classification of the environmental feature. The output of each machine learning model comprises a classification of the environmental feature. A machine learning model may be selected based on the evaluated performances of the machine learning models and may be used to control driverless operations of AVs.