Abstract: A method for classifying at least one object in the surroundings of a vehicle using an ultrasonic sensor system. The method includes: emitting ultrasonic signals at a plurality of successive time points using the ultrasonic sensor system; and receiving ultrasonic echo signals reflected on the one or more objects in the surroundings of the vehicle, using the ultrasonic sensor system; wherein one or more objects in the surroundings are ascertained as a function of the received ultrasonic echo signals, and wherein it is determined as a function of the received ultrasonic echo signals whether the respective object is classified as dynamic, in particular as a pedestrian. A controller for operating an ultrasonic sensor system, an ultrasonic sensor system, and a vehicle including such an ultrasonic sensor system are also described.

Abstract: A method for providing training datasets for training an object classification model for object classification in an ultrasonic sensor system is disclosed.

Abstract: A method for ascertainment of an approximate object position of a dynamic object in the surroundings of a vehicle. The method includes: detecting sensor data; ascertaining a present reflection origin position of a static or dynamic object as a function of the detected sensor data; detecting a camera image; recognition of the dynamic object as a function of the detected camera image; and ascertaining a present estimated position of the recognized dynamic object relative to the vehicle as a function of the detected camera image. When a position distance between the ascertained estimated position of the recognized dynamic object and the ascertained reflection origin position is less than or equal to a distance threshold value, a classification of the present reflection origin position as belonging to the recognized dynamic object takes place as a function of the underlying sensor data for the ascertainment of the reflection origin position.

Abstract: A method for operating an ultrasonic sensor system having an ultrasonic sensor device for determining an object property of an environment object, in which classification models for determining classification results which specify a modeled object property of the environment object are provided for a plurality of detection situations of the environment object, each classification model trained for evaluation with a different subset of signal characteristics extracted from ultrasonic reception signals to provide a classification result of the corresponding classification model and an associated quality specification, includes: detecting the ultrasonic reception signals; determining the quantity of signal characteristics from the ultrasonic reception signals; determining classification results and associated quality specifications by evaluating the classification models with the corresponding subset of the quantity of the signal characteristics on the basis of the detection situation; and determining the object

Abstract: A system and method is disclosed for classifying one or more objects within a vicinity of a vehicle. Ultra-sonic data may be received from a plurality of ultra-sonic sensors and may comprise echo signals indicating one or more objects that are proximally located within a vicinity of a vehicle. One or more features may be calculated from the ultra-sonic data using one or more signal processing algorithms unique to each of the plurality of ultra-sonic sensors. The one more features may be combined using a second-level signal processing algorithm to determine geometric relations for the one or more objects. The one or more features may then be statistically aggregated at an object level. The one or more objects may then be classified using a machine learning algorithm that compares an input of each of the one or more features to a trained classifier.

Abstract: A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.

Abstract: A method and system is disclosed for creating a machine learning model that is reconfigurable. A fixed parameter model is created to include fixed feature values obtained during a training process for the machine learning model. The fixed parameter model may include a fixed base classifier used by the machine learning model to classify objects detected by an ultra-sonic system within a vicinity of a vehicle. A configurable parameter model may be created to include feature values that are different from the fixed feature values, the configurable parameter model including a modified base classifier. A vehicle controller may receive and update the fixed parameter model with the configurable parameter model. The machine learning model may be updated to use the configurable parameter model to classify the objects detected by the ultra-sonic system.

Abstract: A method for classifying objects in the surroundings of a vehicle using ultrasonic sensors which emit ultrasonic pulses and receive ultrasonic echoes reflected by objects. Distances between the sensors and objects reflecting ultrasonic pulses are ascertained via at least two ultrasonic sensors including overlapping fields of vision, and a position determination of the reflecting objects taking place using lateration and the assignment of the received ultrasonic echoes to object hypotheses for distinguishing between extensive objects and point-like objects.