Data augmentation for obstruction learning

A method comprising:

• capturing, with at least one sensor, images of obstacles that are at least partially obstructing an object of interest;
• processing, with at least one processor, the images using a machine learning model that is trained on a dataset that includes portions of augmented images of obstacles that obstruct the object of interest, wherein the portions of augmented images are extracted from randomly scaled images;
• predicting, with the at least one processor, a state of the object of interest based on output of the machine learning model;
• planning, with the at least one processor, a trajectory for a vehicle;
• and
• causing, with a control circuit of the vehicle, the vehicle to travel the trajectory.

A vehicle comprising:

• at least one sensor;
• at least one processor;
• memory storing instructions that when executed by the at least one processor, causes the at least one processor to perform operations comprising:
• capturing, with the at least one sensor, images of obstacles that are at least partially obstructing an object of interest;
• processing, with the at least one processor, the images using a machine learning model that is trained on a dataset that includes selected portions of augmented images of obstacles that obstruct the object of interest, wherein the portions of augmented images are extracted from randomly scaled images;
• predicting, with the at least one processor, a state of the object of interest based on output of the machine learning model;
• planning, with the at least one processor, a trajectory for a vehicle;
• and
• causing, with a control circuit of the vehicle, the vehicle to travel the trajectory.