Systems and methods for multi-modal data augmentation for perception tasks in autonomous driving

1. A method for performing at least one perception task associated with autonomous control of a vehicle, the method comprising:

receiving a first dataset, the first dataset including corresponding to at least one environment of the vehicle;

plurality of images identifying a first object category of objects associated with the plurality of images, the first object category including a plurality of object types;

identifying a current statistical distribution of a first object type of the plurality of object types;

determining a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution associated with the first object category;

in response to a determination that the first distribution difference is greater than a threshold, generating first object type data corresponding to the first object type;

configuring at least one attribute of the first object type data;

generating a second dataset by augmenting the first dataset using the first object type data:

training a machine learning model using the second dataset;

performing, using the machine learning model, at least one perception task associated with autonomous control of a vehicle;

receiving, from the machine learning model, an output associated with the at least one perception task; and

providing the output associated with the at least one perception task to a controller of the vehicle, wherein the controller of the vehicle performs at least one autonomous vehicle control operation of the vehicle based on the output associated with the at least one perception task.

2. The method of claim 1 , wherein the at least one attribute of the first object type data includes a location attribute.

3. The method of claim 1 , wherein the at least one attribute of the first object type data includes an orientation attribute.

4. The method of claim 1 , further comprising generating two-dimensional object data based on the first object type data.

5. The method of claim 4 , wherein augmenting the first dataset using the first object type data includes augmenting the first dataset to include the two-dimensional object data.

6. The method of claim 1 , further comprising generating three-dimensional object data based on the first object type data.

7. The method of claim 6 , wherein augmenting the first dataset using the first object type data includes augmenting the first dataset to include the three-dimensional object data.

8. The method of claim 1 , further comprising fusing two-dimensional object data associated with the first object type data with corresponding three-dimensional object data associated with the first object type data.

9. The method of claim 8 , wherein augmenting the first dataset using the first object type data includes augmenting the first dataset based on the fused two-dimensional object data and the three-dimensional object data.

10. The method of claim 1 , wherein the standard statistical distribution corresponds to a data distribution of the first object category.

11. A system for performing at least one perception task associated with autonomous control of a vehicle, the system comprising:

a processor; and

a memory including instructions that, when executed by the processor, cause the processor to:

receive a first dataset, the first dataset including plurality of images corresponding to at least one environment of the vehicle;

identify a first object category of objects associated with the plurality of images, the first object category including a plurality of object types;

identify a current statistical distribution of a first object type of the plurality of object types;

determine a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution associated with the first object category;

in response to a determination that the first distribution difference is greater than a threshold, generate first object type data corresponding to the first object type;

configure at least one attribute of the first object type data;

generate a second dataset by augmenting the first dataset using the first object type data;

train a machine learning model using the second dataset;

perform, using the machine learning model, at least one perception task associated with autonomous control of a vehicle;

receive, from the machine learning model, an output associated with the at least one perception task; and

provide the output associated with the at least one perception task to a controller of the vehicle, wherein the controller of the vehicle performs at least one autonomous vehicle control operation of the vehicle based on the output associated with the at least one perception task.

12. The system of claim 11 , wherein the at least one attribute of the first object type data includes a location attribute.

13. The system of claim 11 , wherein the at least one attribute of the first object type data includes an orientation attribute.

14. The system of claim 11 , wherein the instructions further cause the processor to augment the first dataset further using two-dimensional object data associated with the first object type data.

15. The system of claim 11 , wherein the instructions further cause the processor to augment the first dataset further using three-dimensional object data associated with the first object type data.

16. The system of claim 11 , wherein the instructions further cause the processor to augment the first dataset further using fused two-dimensional object data and three-dimensional object data associated with the first object type data.

17. The system of claim 11 , wherein the standard statistical distribution corresponds to a data distribution of the first object category.

18. An apparatus for performing at least one perception task associated with autonomous control of a vehicle, the apparatus comprising:

a processor, and

a memory including instructions that, when executed by the processor, cause the processor to:

receive a first dataset, the first dataset including plurality of images corresponding to at least one environment of the vehicle;

identify a first object category of objects associated with the plurality of images, the first object category including a plurality of object types;

identify a current statistical distribution of a first object type of the plurality of object types;

determine a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution that corresponds to a data distribution of the first object category;

in response to a determination that the first distribution difference is greater than a threshold, generate first object type data corresponding to the first object type;

configure at least one attribute of the first object type data;

generate a second dataset by augmenting the first dataset using the first object type data;

train a machine learning model trained using the second dataset, the machine learning model being configured to perform at least one perception task associated with autonomous control of the vehicle; and

perform, using the machine learning model at least one perception task of associated with autonomous control of the vehicle;

receive, from the machine learning model an output associated with the at least one perception task, and

provide the output associated with the at least one perception task to a controller of the vehicle, wherein the controller of the vehicle performs at least one autonomous vehicle control operation of the vehicle based on the output associated with the at least one perception task.