Methods and apparatus for automatic collection of under-represented data for improving a training of a machine learning model

A system, comprising:

• at least one camera disposed at a vehicle and configured to capture a first plurality of images;
• at least one localization sensor disposed at the vehicle and configured to capture localization data;
• a memory operatively coupled to the at least one camera and the at least one localization sensor, the memory configured to store the first plurality of images and the localization data;
• and
• a processor operatively coupled to the memory, the processor configured to:
• execute a machine learning model to detect at least one lane in each image from the first plurality of images;
• determine an estimate location of the vehicle for each image from the first plurality of images, based on the localization data;
• select lane geometry data for each image from the first plurality of images, from a map and based on the estimate location of the vehicle;
• execute a localization model to generate a plurality of offset values for the first plurality of images based on the lane geometry data and the at least one lane in each image from the first plurality of images;
• selecting a second plurality of images from the first plurality of images based on the plurality of offset values and a previously-determined offset threshold;
• and
• training the machine learning model based on the second plurality of images.

A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:

• receive a first plurality of images;
• execute a first machine learning model to detect at least one lane in each image from the first plurality of images;
• determine an estimate location of a vehicle for each image from the first plurality of images, based on localization data captured using at least one localization sensor disposed at the vehicle;
• select lane geometry data for each image from the first plurality of images, from a map and based on the estimate location of the vehicle;
• execute a localization model to generate a plurality of offset values for the first plurality of images based on the lane geometry data and the at least one lane in each image from the first plurality of images;
• select a second plurality of images from the first plurality of images based on the plurality of offset values and a previously-determined offset threshold;
• and
• collect training data based on the second plurality of images.