Abstract: This document discloses system, method, and computer program product embodiments for managing data generated by one or more systems of a vehicle. In various embodiments, a processor onboard a vehicle receives messages generated by one or more onboard systems of the vehicle. The system saves a first set of the messages to a first storage location on the vehicle according to a first data logging policy. The system processes a second set of the messages to reduce data elements and yield offboard data that is designated for offboard use. The first and second sets of messages may or may not overlap with each other. The system saves the offboard data to a second storage location that is onboard the vehicle and subject to a second data logging policy. The second data logging policy differs from the first data logging policy.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.

Abstract: This document discloses system, method, and computer program product embodiments for managing data generated by one or more systems of a vehicle. In various embodiments, a processor onboard a vehicle receives messages generated by one or more onboard systems of the vehicle. The system saves a first set of the messages to a first storage location on the vehicle according to a first data logging policy. The system processes a second set of the messages to reduce data elements and yield offboard data that is designated for offboard use. The first and second sets of messages may or may not overlap with each other. The system saves the offboard data to a second storage location that is onboard the vehicle and subject to a second data logging policy. The second data logging policy differs from the first data logging policy.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle, identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs, determining a function for assigning an importance score to each of the plurality of unlabeled sensor data logs, using the one or more trends, using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs, selecting a subset of the plurality of sensor data logs that have an importance score greater than a threshold, and using 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.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle, identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs, determining a function for assigning an importance score to each of the plurality of unlabeled sensor data logs, using the one or more trends, using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs, selecting a subset of the plurality of sensor data logs that have an importance score greater than a threshold, and using 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.