Method for determining a road surface condition, method for controlling a vehicle, data processing apparatus, vehicle, computer program, computer-readable storage medium, and method for training a combination of artificial neural networks

A method for controlling a vehicle, the method comprising:

• determining a road surface condition using a method comprising:
• obtaining first data, wherein the first data comprises a representation of the road surface and wherein the first data originates from a sensor of a first type,
• obtaining second data, wherein the second data comprises a representation of the road surface and wherein the second data originates from a sensor of a second type,
• generating third data by applying a feature extraction technique on the first data,
• generating fourth data by applying a feature extraction technique on the second data,
• generating fifth data by fusing the third data and the fourth data, and
• determining the road surface condition by classifying the fifth data in at least one class of a set of predefined classes, and
• causing adjustment of a driving parameter and/or causing a warning based on the determined road surface condition (16).

A method for training a combination of artificial neural networks, the method comprising:

• providing a first artificial neural network configured for generating third data by applying a feature extraction technique on first data, wherein the first data comprises a representation of a road surface and wherein the first data originates from a sensor of a first type,
• providing a second artificial neural network configured for generating fourth data by applying a feature extraction technique on second data, wherein the second data comprises a representation of a road surface and wherein the second data originates from a sensor of a second type,
• providing a third artificial neural network configured for generating fifth data by fusing the third data and the fourth data,
• providing a fourth artificial neural network configured for classifying the fifth data in at least one of a set of predefined classes, and
• training the combination of the first artificial neural network, the second artificial neural network, the third artificial neural network, and the fourth artificial neural network in an end-to-end manner using training data comprising first data annotated with at least one of the predefined classes and comprising second data annotated with at least one of the predefined classes.