Apparatus and methods for predicting tire temperature levels

A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to:

• receive input data indicating a target route for a target vehicle, one or more attributes associated with the target vehicle, and one or more attributes of the target route;
• and
• cause a machine learning model to generate output data as a function of the input data,
• wherein the output data indicate prediction of one or more tire temperature levels of the target vehicle during a period in which the target vehicle traverses the target route,
• wherein the machine learning model is trained to generate the output data as a function of the input data based on training data, and
• wherein the training data indicate events in which vehicles traversed routes, wherein the training data include tire temperature levels of the vehicles during the events, vehicle data indicating one or more attributes of the vehicles, map data indicating one or more attributes of the routes, and environmental data indicating one or more attributes of environments of the routes during the events.

A method of providing predicted tire temperature levels, the method comprising:

• receiving input data indicating a target route for a target vehicle, one or more attributes associated with the target vehicle, and one or more attributes of the target route;
• and
• causing a machine learning model to generate output data as a function of the input data,
• wherein the output data indicate prediction of one or more tire temperature levels of the target vehicle during a period in which the target vehicle traverses the target route,
• wherein the machine learning model is trained to generate the output data as a function of the input data based on training data, and
• wherein the training data indicate events in which vehicles traversed routes, wherein the training data include tire temperature levels of the vehicles during the events, vehicle data indicating one or more attributes of the vehicles, map data indicating one or more attributes of the routes, and environmental data indicating one or more attributes of environments of the routes during the events.