Systems and methods for intelligent selection of data for building a machine learning model

A system for selecting data for training a machine learning model using active learning, the system comprising:

• a processor;
• and
• a non-transitory computer-readable medium comprising one or more programming instructions that when executed by the processor, cause the processor to:
• receive, from a plurality of sensors of an autonomous vehicle, a plurality of unlabeled sensor data logs corresponding to surroundings of the autonomous vehicle;
• identify one or more trends associated with a training dataset comprising a plurality of labeled data logs, the training dataset being used for training a machine learning model;
• determine, using the one or more trends, a function for assigning an importance score to each of the plurality of unlabeled sensor data logs;
• use the function for assigning the importance score to each of the plurality of unlabeled sensor data logs;
• select a subset of the plurality of sensor data logs that have an importance score greater than a threshold;
• and
• use the subset of the plurality of sensor data logs for further training the machine learning model trained using the training dataset to generate an updated model.