Machine-learned cost estimation in tree search trajectory generation for vehicle control

A method comprising:

• receiving environment state data indicating static characteristics of an environment associated with a vehicle;
• receiving dynamic object data indicating at least one of a historical or a current state of at least one of a dynamic object or the vehicle;
• determining, by a first machine-learned model and based at least in part on the environment state data, environment features;
• determining, by a second machine-learned model and based at least in part on the dynamic object data, dynamic features;
• initializing a third machine-learned model based at least in part on the dynamic features;
• determining, by the third machine-learned model and based at least in part on the environment features and prediction node data indicating one or more actions performed by the vehicle to reach a future state indicated by a prediction node of a tree search, a first output;
• aggregating, by an encoder, the first output and the dynamic features as an encoded output;
• determining, by a decoder and based at least in part on the encoded output, an estimated cost;
• and
• determining a trajectory for controlling a vehicle based at least in part on the estimated cost.

A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause one or more processors to perform operations comprising:

• receiving environment state data indicating static characteristics of an environment associated with a vehicle;
• receiving dynamic object data indicating at least one of a historical or a current state of at least one of a dynamic object or the vehicle;
• determining, by a first machine-learned model and based at least in part on the environment state data, environment features;
• determining, by a second machine-learned model and based at least in part on the dynamic object data, dynamic features;
• initializing a third machine-learned model based at least in part on the dynamic features;
• determining, by the third machine-learned model and based at least in part on the environment features and prediction node data indicating one or more actions performed by the vehicle to reach a future state indicated by a prediction node of a tree search, a first output;
• aggregating, by an encoder, the first output and the dynamic features as an encoded output;
• determining, by a decoder and based at least in part on the encoded output, an estimated cost;
• and
• determining a trajectory for controlling a vehicle based at least in part on the estimated cost.