Model hyperparameter adjustment using vehicle driving context classification

A system for autonomous driving of a vehicle, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• obtain information relating to an environment in which the vehicle is located;
• determine, using a machine learning model:
• a current driving context of the vehicle based at least in part on the information relating to the environment, and
• a plurality of hyperparameters, comprising a tree width, a tree depth, a time length for a step in a tree, and a reward function, wherein each hyperparameter, of the plurality of hyperparameters, is determined based at least in part on the current driving context, for a model, wherein the model is used to determine an autonomous driving behavior for the vehicle;
• configure the model with the plurality of hyperparameters based at least in part on determining the plurality of hyperparameters;
• determine, using the model configured with the plurality of hyperparameters, the autonomous driving behavior, wherein the autonomous driving behavior is determined based at least in part on the reward function for the model;
• and
• cause autonomous operation of the vehicle in accordance with the autonomous driving behavior.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• obtain information relating to an environment in which a vehicle is located;
• determine, using a machine learning model:
• a current driving context of the vehicle based at least in part on the information relating to the environment, and
• a plurality of hyperparameters, comprising a tree width, a tree depth, a time length for a step in a tree, and a reward function, wherein each hyperparameter, of the plurality of hyperparameters, is determined based at least in part on the current driving context, for a model, wherein the model is used to determine an autonomous driving behavior for the vehicle;
• configure the model with the plurality of hyperparameters based at least in part on determining the plurality of hyperparameters;
• determine, using the model configured with the plurality of hyperparameters, the autonomous driving behavior, wherein the autonomous driving behavior is determined based at least in part on the reward function for the model;
• and
• cause autonomous operation of the vehicle in accordance with the autonomous driving behavior.

An apparatus, comprising:

• means for obtaining information relating to an environment in which a vehicle is located;
• means for determining, using a machine learning model,
• a current driving context of the vehicle based at least in part on the information relating to the environment, and
• a plurality of hyperparameters, comprising a tree width, a tree depth, a time length for a step in a tree, and a reward function, wherein each hyperparameter, of the plurality of hyperparameters, is determined based at least in part on the current driving context, for a model, wherein the model is used to determine an autonomous driving behavior for the vehicle;
• means for configuring the model with the plurality of hyperparameters based at least in part on determining the plurality of hyperparameters;
• means for determining, by the apparatus and using the model configured with the plurality of hyperparameters, the autonomous driving behavior, wherein the autonomous driving behavior is determined based at least in part on the reward function for the model;
• and
• means for causing, by the apparatus, autonomous operation of the vehicle in accordance with the autonomous driving behavior.