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 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.