Systems and methods for fine-tuning a machine learning model of an autonomous vehicle during specific events

A system for fine-tuning a machine learning model of an autonomous vehicle during specific events, comprising:

• at least one memory;
• at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to:
• monitor a vehicle navigating in an environment using a pre-trained machine learning model that is configured to make navigation decisions during autonomous driving of the vehicle;
• identify a first simulated trajectory generated by the pre-trained machine learning model and a first real trajectory driven by the vehicle;
• in response to determining that a trajectory type of the first real trajectory is one of a plurality of trajectory types that trigger fine-tuning of the pre-trained machine learning model, calculate a discrepancy value between the first real trajectory and the first simulated trajectory;
• in response to determining that the discrepancy value is greater than a threshold discrepancy value, update weights of the pre-trained machine learning model;
• and
• execute the pre-trained machine learning model with the updated weights.

A non-transitory computer readable medium storing thereon computer executable instructions for fine-tuning a machine learning model of an autonomous vehicle during specific events, including instructions for:

• monitoring a vehicle navigating in an environment using a pre-trained machine learning model that is configured to make navigation decisions during autonomous driving of the vehicle;
• identifying a first simulated trajectory generated by the pre-trained machine learning model and a first real trajectory driven by the vehicle;
• in response to determining that a trajectory type of the first real trajectory is one of a plurality of trajectory types that trigger fine-tuning of the pre-trained machine learning model, calculating a discrepancy value between the first real trajectory and the first simulated trajectory;
• in response to determining that the discrepancy value is greater than a threshold discrepancy value, updating weights of the pre-trained machine learning model;
• and
• executing the pre-trained machine learning model with the updated weights.