Road condition deep learning model

A method comprising:

• receiving sensor data including imagery of a roadway, the sensor data having been captured by a vehicle driving on the roadway;
• obtaining map information of the roadway, the map information comprising at least one of geometry information or lane information of the roadway;
• performing regression analysis using a trained neural network using the imagery and the map information of the roadway;
• and
• determining an estimated snow depth for one or more lanes of the roadway based on an output of the regression analysis;
• wherein the neural network is trained with training data including labeled road wetness ground truth examples.

A method comprising:

• obtaining, by one or more processors, sensor data from one or more sensors of a perception system of a vehicle, the one or more sensors being configured to detect objects or conditions in an environment around the vehicle:
• using, by the one or more processors, a stored a road condition deep learning model to generate information associated with a discrete classification or continuous regression/estimation of road wetness based on the obtained sensor data;
• and
• using, by the one or more processors, the generated information to issue a control signal associated with operation of the vehicle.