Long tail lidar 3-D object detection improvement with targeted simulation data injection

A system comprising:

• a memory configured to store instructions;
• and
• a processor configured to execute the instructions and cause the processor to:
• determine that a machine learning algorithm performed below a threshold performance score when receiving data descriptive of a 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object;
• determine that the first type of object corresponds to a long-tail object, wherein the long-tail object has a low frequency of occurrence relative to other objects in a training dataset used to train the machine learning algorithm;
• search a database of saved real-world scenarios to identify objects classified as the first type of object in the saved real-world scenarios, wherein the saved real-world scenarios include sensor data associated with the first type of object;
• generate at least one 3-D representation of the first type of object from the sensor data associated with the first type of object;
• modify a plurality of simulated scenes to include the at least one 3-D representation of the first type of object to yield a plurality of modified simulated scenes;
• and
• train the machine learning algorithm with the plurality of modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.

A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to:

• determine that a machine learning algorithm performed below a threshold performance score when receiving data descriptive of a 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object;
• determine that the first type of object corresponds to a long-tail object, wherein the long-tail object has a low frequency of occurrence relative to other objects in a training dataset used to train the machine learning algorithm;
• search a database of saved real-world scenarios to identify objects classified as the first type of object in the saved real-world scenarios, wherein the saved real-world scenarios include sensor data associated with the first type of object;
• generate at least one 3-D representation of the first type of object from the sensor data associated with the first type of object;
• modify a plurality of simulated scenes to include the at least one 3-D representation of the first type of object to yield a plurality of modified simulated scenes;
• and
• train the machine learning algorithm with the plurality of modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.