Self-learning vehicle performance optimization

A system of an autonomous or semi-autonomous vehicle comprising:

• one or more sensors;
• one or more processors;
• and
• a memory storing instructions that, when executed by the one or more processors, causes the system to perform, without intervention from a human:
• selecting a trajectory from potential trajectories along a route to be travelled by the autonomous or semi-autonomous vehicle;
• predicting a trajectory of another object along the route;
• adjusting the selected trajectory based on a current traffic density, a current lighting condition, and a predicted change, in response to adjusting the selected trajectory, to the predicted trajectory of the another object, the predicted change to the predicted trajectory of the another object being stored in a machine learning model and being based on instances of historical data of previous changes;
• determining an actual change, in response to adjusting the selected trajectory, to the trajectory of the another object, based on a difference between the predicted trajectory of the another object and an actual trajectory of the another object in response to an interaction between the autonomous or semi-autonomous vehicle and the another object;
• updating the machine learning model based on the determined actual change to the trajectory of the another object, the updating comprising:
• determining weights, corresponding to the respective instances of the historical data, of the machine learning model, wherein the weights are based on degrees of similarity between:
• previous lighting conditions corresponding to the historical data, and
• the current lighting condition during which the actual change took place;
• and
• adjusting the predicted change to the predicted trajectory of the another object according to a weighted average of the predicted change to the predicted trajectory of the another object and the actual change to the trajectory of the another object, the weighted average being based on adjusting the weights to reduce respective deviations between the predicted change to the predicted trajectory of the another object and the actual change to the predicted trajectory of the another object;
• and
• selecting a future trajectory based on the updated machine learning model.