Systems and methods for training a data-driven planner using rules-based optimization planner parameters

A method of training an autonomous vehicle, the method comprising:

• receiving a plurality of sets of parameters of an iterative trajectory optimization planner, wherein each set of parameters of the plurality of sets of parameters corresponds with an individual driving preference indicative of a driving preference;
• receiving a plurality of sets of vehicle data of one or more vehicles operating using the iterative trajectory optimization planner, wherein each set of vehicle data of the plurality of sets of vehicle data corresponds with an individual set of parameters of the plurality of sets of parameters;
• and
• training, using the plurality of sets of parameters and the plurality of sets of vehicle data, a data-driven planner such that the data-driven planner outputs a DDP trajectory in accordance with a selected driving preference.

A computing apparatus comprising:

• one or more processors;
• and
• a non-transitory memory component storing instructions that, when executed by the one or more processors, configure the computing apparatus to:
• receive a plurality of sets of parameters of an iterative trajectory optimization planner, wherein each set of parameters of the plurality of sets of parameters corresponds with an individual driving preference;
• receive a plurality of sets of vehicle data of one or more vehicles operating using the iterative trajectory optimization planner, wherein each set of vehicle data of the plurality of sets of vehicle data corresponds with an individual set of parameters of the plurality of sets of parameters;
• and
• train, using the plurality of sets of parameters and the plurality of sets of vehicle data, a data-driven planner such that the data-driven planner outputs a DDP trajectory in accordance with a selected driving preference.