Abstract: A tracking system and method for tracking tools in and nearby a transportation vehicle is provided. The tracking system comprises a vehicle-based detection unit for optically acquiring tool sets in the vehicle loading space from different angles and providing digital image data as well as corresponding range information. An electronic main controller unit is operatively coupled to a communication receiving unit and communicates with the vehicle-based detection unit and a cloud-based computer system of the tracking system. A mobile computing unit includes an optical camera and a LIDAR sensor device and wirelessly communicates with the electronic main controller unit and the cloud-based computer system. The tool detection and tracking is accomplished by combining an image-based detection employing an artificial neural network in the cloud-based computer system and a signal-based detection employing the short-range wireless network communication means.

Abstract: A computer-implemented simulation of a virtual hand in a virtual environment can include reading in a hand dataset that is representative of a shape of a real hand of a user, applying the hand dataset to a trained, artificial neural network, and determining an output dataset with the artificial neural network to which the dataset has been applied indicative of forces acting on a virtual object.

Abstract: A method for a driver assistant image customization of vehicle-related images, a data processing circuit, a computer program, a computer-readable medium, and a vehicle, can include, with at least one sensing device of the vehicle, obtaining an input image to be customized. With at least one human-machine-interface of the vehicle or connected thereto an input determining at least one customization scheme to be performed is received. Using at least one data processing circuit of the vehicle applying artificial intelligence the input image is transformed according to the at least one customization scheme into a transformed output image. With at least one smart mirror of the vehicle or a mobile device connected to the at least one data processing circuit the transformed output image is outputted. The at least one customization scheme includes a plurality of different types of adaptation modes.

Abstract: A tracking system for tracking of objects to be transported by at least one transport vehicle is provided. The tracking system includes object sets comprising an object and at least one tagging device with unique identification signal to be permanently sent out and received by a short-range wireless communication unit arrangeable in the transport vehicle. The tracking system further comprises, arrangeable in the transport vehicle, an electronic main controller unit, operatively coupled at least to the short-range wireless communication unit, and a telematics control unit for communicating with a cloud-based computer system.

Abstract: The invention relates to a system for performing a method for performing computer-aided simulations. The system loads an input dataset which is based on user interactions of a user indicating a manipulation of a representation of a component, provides a tracking dataset indicating changes in the representation through the manipulations by evaluating the input dataset, provides an output dataset which is based on evaluation of the tracking dataset by means of machine learning, where the output dataset assigns a model to the representation, and outputs the output dataset.

Abstract: An image data set is generated for output by an infotainment system of a motor vehicle. Input data sets associated with respective functions of the infotainment system are clustered according to at least one predetermined criterion. The clustered input data sets are evaluated to determine at least one parameter of a circular representation for the image data set, which is generated using the at least one determined parameter.

Abstract: A method for operating a motor vehicle includes acquiring current operating data in order to obtain archived operating data during an acquisition phase, evaluating the archived operational data in order to obtain labeled training data for an artificial intelligence during a simulation phase, training the artificial intelligence with the labeled training data during a training phase, and activating or deactivating an ECU or a control function (A, X) of the ECU of the motor vehicle by means of the artificial intelligence during a prediction phase.

Abstract: A tracking system and method for tracking tools in and nearby a transportation vehicle is provided. The tracking system comprises a vehicle-based detection unit for optically acquiring tool sets in the vehicle loading space from different angles and providing digital image data as well as corresponding range information. An electronic main controller unit is operatively coupled to a communication receiving unit and communicates with the vehicle-based detection unit and a cloud-based computer system of the tracking system. A mobile computing unit includes an optical camera and a LIDAR sensor device and wirelessly communicates with the electronic main controller unit and the cloud-based computer system. The tool detection and tracking is accomplished by combining an image-based detection employing an artificial neural network in the cloud-based computer system and a signal-based detection employing the short-range wireless network communication means.

Abstract: A simulation includes loading a first generator of a Cycle Generative Adversarial Network (CycleGAN) with a model dataset during a training phase in order to train the first generator in cooperation with a first discriminator of the CycleGAN assigned to the first generator, and loading a second generator of the CycleGAN with a hardware dataset in order to train the second generator in cooperation with a second discriminator of the CycleGAN assigned to the second generator, and loading the first generator with an input dataset during an operational phase in order to provide an output dataset.

Abstract: Generating a simplified model for an XiL system includes determining a stipulated parameter characterizing model complexity, for a starting model; generating starting model input and output data; training a neural network to generate a simplified model having a lower complexity than the starting model and where a stipulated lower threshold value for a parameter characterizing model reliability is exceeded; generating a simplified model using the trained neural network; determining a parameter characterizing the complexity for the simplified model; if the determined complexity of the generated simplified model is lower than that of the starting model, testing the generated simplified model using a test set of the generated starting model input and output data, which differs from the training set, and determining a parameter of the generated simplified model characterizing reliability; if the determined reliability of the simplified model exceeds the stipulated threshold value, outputting the simplified model.

Abstract: The invention relates to a method for generating an image data set (BDS) for reproduction by means of an infotainment system (4) of a motor vehicle (2), comprising the steps of: (S400) reading in input data sets (EDS) which are associated with respective functions (F1, F2, F3, F4, F5) of the infotainment system (4), (S500) clustering the input data sets (EDS) according to at least one predetermined criterion (K1, K2, K3), (S600) evaluating the clustered input data sets (EDS) in order to determine at least one parameter (P1, P2) which is indicative of a pie-chart representation (6) for the image data set (BDS), and (S700) generating the image data set (BDS) using the at least one determined parameter (P1, P2).

Abstract: An artificial neural network includes an input layer, a first intermediate layer and at least one further intermediate layer, as well as an output layer, wherein the input layer comprises a plurality of neurons, the first intermediate layer a first number of neurons and the further intermediate layer a further number of neurons, wherein the first number is greater than the further number.

Abstract: A computer-implemented simulation of a virtual hand in a virtual environment can include reading in a hand dataset that is representative of a shape of a real hand of a user, applying the hand dataset to a trained, artificial neural network, and determining an output dataset with the artificial neural network to which the dataset has been applied indicative of forces acting on a virtual object.

Abstract: Systems and method for performing an XiL-based simulation are provided. Operating data sets (BDS) are read, An artificial intelligence system is trained with the operating data sets (BDS) Test data sets (TDS) are generated using the trained artificial intelligence system. The test data sets (TDS) are provided for the XiL simulation.

Abstract: An electrified vehicle and associated method for controlling an electrified vehicle having an electric machine powered by a traction battery include an autoencoder trained with training data indicative of a remaining driving range of the traction battery. The trained autoencoder processes vehicle operating data to generate a reference data record and determines a value indicative of a similarity between the vehicle operating data and the reference data record. The autoencoder generates an output data record if the value indicative of the similarity is below a predetermined threshold value. The output data record may be used to display an alert or message to a vehicle occupant and/or control the vehicle to reduce power consumption to increase vehicle driving range.

Abstract: A computer includes a processor and a memory, the memory including instructions executable by the processor to identify a diagnostic test to perform for a vehicle component, identify one of a plurality of XiL platforms on which to perform the diagnostic test, and perform the diagnostic test on the identified XiL platforms.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect control data indicating behavior of a vehicle controlled by a control program, collect virtual control data indicating behaviour of a virtual vehicle controlled by a virtual control program, and determine a difference between the control data and the virtual control data.

Abstract: A plurality of training data sets of user interactions in a real environment can be determined. A machine learning program is trained with the training data sets. A data set of virtual user interactions with a virtual environment is input to the trained machine learning program to output a probability of selection of an object in the virtual environment. The object is identified in the virtual environment selected by a user based on the probability. A manipulation of the object by the user is then identified.

Abstract: A method for generating an image data set for a computer-implemented simulation includes reading kinematic data representative of positions of a light source, velocities of the light source, accelerations of the light source, or a combination thereof. The method further includes displacing the light source according to the kinematic data, acquiring light data of the light source, compiling the light data and the associated kinematic data to form a light data set, and training an artificial neural network using the light data set to generate a supplementary data set. The method includes generating the supplementary data set using the trained artificial neural network, and generating the image data set using a raw image data set according to the computer-implemented simulation and the supplementary data set.

Abstract: A request signal that indicates a quality for a determination of an orientation of a road user is received. The orientation of the road user is determined based on a) image data when the request signal indicates the quality is below a predetermined quality for the determination of the orientation of the road user, or b) on LIDAR data and image data when the request signal indicates the quality is the predetermined quality.