Abstract: A computer-implemented method for training an artificial intelligence module for determining an object in an environment of a vehicle. The method includes: providing a measured value dataset on a data carrier, wherein the measured value dataset comprises at least one data entry about a reflection of an ultrasonic signal in an airborne sound range and at least one data entry about a class of an object; generating a modified training dataset based on the measured value dataset, wherein generating the modified training dataset comprises the following steps: creating an input dataset based on the data entry about the reflection of the ultrasonic signal in the airborne sound range, creating an output dataset based on the data entry about the class of the object, wherein the method further comprises training an AI module based on the modified training dataset.

Abstract: A method for ultrasound-based object classification. The method includes generating a time reflection signal at a first ultrasonic sensor; receiving and/or generating secondary signals of at least one or more ultrasonic sensors adjacent to the first ultrasonic sensor, and extracting predetermined features from the secondary signals, which are generated at the adjacent ultrasonic sensors when the time reflection signal is received; transmitting the time reflection signal and the predetermined features from the secondary signals to a classifier device; fusing the time reflection signal and the predetermined features by the classifier device, wherein a training data set for a present sensor arrangement is taken into account; and outputting an object classification by the classifier device.

Abstract: A method for classifying an object to be detected with at least one ultrasonic sensor. The method includes: transmitting a first signal using the ultrasonic sensor to the object; receiving a second signal using the ultrasonic sensor, wherein the second signal is a backscattered signal from the object; processing the second signal into a digital signal; extracting a selected signal portion from the digital signal, the selected signal portion representing a relevant and time-limited time segment from the digital signal; transforming the selected signal portion into a two-dimensional feature vector; feeding the two-dimensional feature vector into a neural network as at least one input variable; determining object class information for the object using the neural network, wherein, based on the at least one input variable, the neural network produces an output variable which indicates a probability value for at least one defined object class for the one object.