Identifying new classes of objects in environments of vehicles

A system, comprising:

• at least one processor;
• and
• at least one memory storing instructions, which when executed by the at least one processor, result in operations comprising:
• applying a first machine learning model trained to perform an open-set detection by at least identifying, based at least on image data indicative of one or more objects present in at least one environment in which one or more vehicles operate, at least one new class of objects;
• updating, using the at least one processor, a dataset comprising a plurality of classes of objects to comprise the at least one new class of objects identified by the first machine learning model;
• and
• training, using the at least one processor and based at least on the updated dataset comprising the at least one new class of objects identified by the first machine learning model, a second machine learning model to determine a trajectory for a vehicle.

A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

• applying a first machine learning model trained to perform an open-set detection by at least identifying, based at least on image data indicative of one or more objects present in at least one environment in which one or more vehicles operate, at least one new class of objects;
• updating, using the at least one processor, a dataset comprising a plurality of classes of objects to comprise the at least one new class of objects identified by the first machine learning model;
• and
• training, using the at least one processor and based at least on the updated dataset comprising the at least one new class of objects identified by the first machine learning model, a second machine learning model to determine a trajectory for a vehicle.