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

1. A method for selecting data for building a machine learning model using active learning, the method comprising:

receiving, from a plurality of sensors, 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, the training dataset being used for training a machine learning model;

determining, using the one or more trends, a function for assigning an importance score to each of the plurality of unlabeled sensor data logs;

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.

2. The method of claim 1 , further comprising:

identifying, using the machine learning model trained using the training dataset, within each of the plurality of sensor data logs, a bounding box and an associated label;

determining a confidence score of the machine learning model trained using the training dataset for identifying the associated label; and

using the confidence score in determining the function.

3. The method of claim 2 , wherein the machine learning model is an object detection model.

4. The method of claim 2 , wherein the confidence score is determined based on at least one of the following associated with the machine learning model trained using the training dataset: an actual accuracy; a desired accuracy; a false positive rate; a false negative rate; a convergence; an output; a statistical fit; identification of a problem being solved; or a training status.

5. The method of claim 2 , further comprising:

receiving metadata associated with each of the plurality of labeled data logs in the training data sets; and

using the metadata for identifying the one or more trends.

6. The method of claim 5 , wherein the metadata comprises at least one of the following: a time of capture of a data log; a location of capture of a data log; information relating to environmental conditions at a location or time of capture of a data log; or information relating to one or more events a location or time of capture of a data log.

7. The method of claim 2 , wherein the one or more trends are used to identify one or more characteristics in the plurality labeled data logs of the training dataset that are underrepresented.

8. The method of claim 7 , wherein the one or more characteristics are selected from at least one of the following: a label class count; a label count by time; a label count by location; an event class count; an event class count by time; an event class count by location; an instance count by environmental conditions; or a machine leaning model uncertainty.

9. The method of claim 2 , further comprising receiving metadata associated with the sensor data logs, the metadata comprising at least one of the following: a time of capture of a data log; a location of capture of a data log; information relating to environmental conditions at a location or time of capture of a data log; or information relating to one or more events a location or time of capture of a data log.

10. The method of claim 9 , further comprising using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs by assigning a score to one or more of a plurality of characteristics of the data log, the plurality of characteristics comprising at least one of the following: a label class count; a label count by time; a label count by location; an event class count; an event class count by time; an event class count by location; an instance count by environmental conditions; or a machine leaning model uncertainty.

11. The method of claim 2 , further comprising discarding one or more of the plurality of sensor data logs that have an importance score less than the threshold.

12. 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.

13. The system of claim 12 , further comprising programming instructions that when executed by the processor, cause the processor to:

identify, using the machine learning model trained using the training dataset, within each of the plurality of sensor data logs, a bounding box and an associated label;

determine a confidence score of the machine learning model trained using the training dataset for identifying the associated label; and

use the confidence score in determining the function.

14. The system of claim 13 , wherein the machine learning model is an object detection model.

15. The system of claim 13 , wherein the confidence score is determined based on at least one of the following associated with the machine learning model trained using the training dataset: an actual accuracy; a desired accuracy; a false positive rate; a false negative rate; a convergence; an output; a statistical fit; identification of a problem being solved; or a training status.

16. The system of claim 13 , further comprising programming instructions that when executed by the processor, cause the processor to:

receive metadata associated with each of the plurality of labeled data logs in the training data sets; and

use the metadata for identifying the one or more trends.

17. The system of claim 16 , wherein the metadata comprises at least one of the following: a time of capture of a data log; a location of capture of a data log; information relating to environmental conditions at a location or time of capture of a data log; or information relating to one or more events a location or time of capture of a data log.

18. The system of claim 13 , wherein the one or more trends are used to identify one or more characteristics in the plurality labeled data logs of the training dataset that are underrepresented.

19. The system of claim 18 , wherein the one or more characteristics are selected from at least one of the following: a label class count; a label count by time; a label count by location; an event class count; an event class count by time; an event class count by location; an instance count by environmental conditions; or a machine leaning model uncertainty.

20. The system of claim 12 , further comprising programming instructions that when executed by the processor, cause the processor to receive metadata associated with the sensor data logs, the metadata comprising at least one of the following: a time of capture of a data log; a location of capture of a data log; information relating to environmental conditions at a location or time of capture of a data log; or information relating to one or more events a location or time of capture of a data log.

21. The system of claim 20 , further comprising programming instructions that when executed by the processor, cause the processor to use the function for assigning the importance score to each of the plurality of unlabeled sensor data logs by assigning a score to one or more of a plurality of characteristics of the data log, the plurality of characteristics comprising at least one of the following: a label class count; a label count by time; a label count by location; an event class count; an event class count by time; an event class count by location; an instance count by environmental conditions; or a machine leaning model uncertainty.

22. The system of claim 12 , further comprising programming instructions that when executed by the processor, cause the processor to discard one or more of the plurality of sensor data logs that have an importance score less than the threshold.