Abstract: A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

Abstract: A computer implemented method for determining a beam vector list for object detection may include the following steps carried out by computer hardware components: receiving radar data from at least one radar sensor; determining range information and velocity information based on the radar data; and determining at least one beam vector list based on the range information and the velocity information using a neural network.

Abstract: A method for producing an electrode separator composite, which is a component of an electrode separator stack for a battery cell, and which electrode separator composite is formed of at least one electrode layer and at least one separator layer. The method comprises the following steps: a laying process, in which the electrode and separator layers are laid on top of one another; and a joining process, with which an adhesive connection is formed between the electrode and separator layers laid on top of one another. The joining process involves a plasma treatment, in which there is a surface activation of the electrode and/or separator layers using plasma, such that an adhesive connection is provided between the respective joining partners.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: A computer implemented method for processing radar reflections includes receiving radar reflections by at least one radar sensor; determining a target angle under which radar reflections related to a potential target are received by the at least one radar sensor; and determining an energy of radar reflections received by the at least one radar sensor under a pre-determined angular region around the target angle.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: A device for holding a stud with a radially flange and an axial shank. The holding device comprising a collet including a clamping section and a hollow insertion section defining an insertion axis such that the stud can be inserted via the insertion section to the clamping section. The holding device further comprising a tubular securing sleeve located radially inward of and concentric with the insertion section. A clearance is defined between a bottom end of the securing sleeve and a top end of the clamping section and is adapted to releasably receive the stud flange. A first air flow channel in a first direction is adapted to blow feed the stud into the collet; and a second airflow channel oriented in a second different direction is adapted to maintain the stud shank substantially in line with the insertion axis.

Abstract: A computer implemented method for determining a direction of arrival of a radar detection comprises the following steps carried out by computer hardware components: acquiring a complex-valued beamvector of the radar detection; processing the complex-valued beamvector by a machine learning module in the complex domain; and obtaining the direction of arrival as an output of the machine learning module.

Abstract: The present disclosure relates to a computer-implemented method for determining a radial velocity of an object in a surrounding of a vehicle, the method comprising the steps of: obtaining measurement data from a radar, the measurement data comprising signal data indicative of a measured radial velocity of the object, mapping the measured radial velocity of the object to a plurality of radial velocity intervals, determining, using an artificial intelligence (AI) engine, a probability value for each interval of the plurality of radial velocity intervals based on supplemental measurement data, and determining the radial velocity of the object by selecting an interval of the plurality of radial velocity intervals based on the probability value. The disclosure further relates to a corresponding apparatus, computer program and vehicle.

Abstract: A computer-implemented method analyzes radar data. The method includes acquiring the radar data from one or more radar sensors. The method includes processing the radar data to derive output data including spatial points with associated features. The method includes receiving the output data as input data. The method includes analyzing the radar data based on the input data.

Abstract: A process was invented which determines how to allocate an individual or household savings budget between multiple future goals by mitigating the risk and/or opportunity cost associated with repurposing such savings at a future date. The process is used to divide a savings budget between multiple goals and retrieve estimated goal funding in a quantitative fashion without resort to qualitative preferences or rules-of-thumb. It is also used to explore potential savings budget amounts and resultant estimated goal funding.

Abstract: The present disclosure relates to a computer-implemented method, apparatus, computer program, and a vehicle that includes the apparatus for determining one or more features of a surrounding of a vehicle. In aspects, a method includes obtaining sensor data comprising measurement points, the measurement points being arranged in a data space; determining an abstract signal processing space based on a distribution of the measurement points in the data space; mapping the measurement points into the abstract signal processing space; and determining the one or more features in the abstract signal processing space based on an arrangement of the measurement points in the abstract signal processing space.

Abstract: A monocell and a method for producing a monocell for a battery cell. In this method, a first separator film, an anode film, a second separator film, and a cathode film are stacked in this sequence to form a film layer arrangement and fed to a thermal cutting process. The cutting of the film layer arrangement is accomplished by a thermal cutting method, wherein a first metal coating on the anode film and a second metal coating on the cathode film vaporize in the cut region. The plastic substrate of the anode melts partially and spreads over the cut surface of the first anode layer and electrically insulates the same. The plastic substrate of the cathode melts partially and spreads over the cut surface of the first cathode layer and electrically insulates the same.

Abstract: A computer implemented method for object detection includes: determining a grid, the grid comprising a plurality of grid cells; determining, for a plurality of time steps, for each grid cell, a plurality of respective radar detection data, each radar detection data indicating a plurality of radar properties; determining, for each time step, a respective radar map indicating a pre-determined radar map property in each grid cell; converting the respective radar detection data of the plurality of grid cells for the plurality of time steps to a point representation of pre-determined first dimensions; converting the radar maps for the plurality of time steps to a map representation of pre-determined second dimensions, wherein the pre-determined first dimensions and the pre-determined second dimensions are at least partially identical; concatenating the point representation and the map representation to obtain concatenated data; and carrying out object detection based on the concatenated data.

Abstract: A computer-implemented method for generating ground truth data may include the following steps carried out by computer hardware components: for a plurality of points in time, acquiring sensor data for a respective point in time; and for at least a subset of the plurality of points in time, determining ground truth data of the respective point in time based on the sensor data of at least one present and/or past point of time and at least one future point of time.

Abstract: A method is provided for training a machine-learning algorithm which relies on primary data captured by at least one primary sensor. Labels are identified based on auxiliary data provided by at least one auxiliary sensor. A care attribute or a no-care attribute is assigned to each label by determining a perception capability of the primary sensor for the label based on the primary data and based on the auxiliary data. Model predictions for the labels are generated via the machine-learning algorithm. A loss function is defined for the model predictions. Negative contributions to the loss function are permitted for all labels. Positive contributions to the loss function are permitted for labels having a care attribute, while positive contributions to the loss function for labels having a no-care attribute are permitted only if a confidence of the model prediction for the respective label is greater than a threshold.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

Abstract: A computer implemented method for determining a direction of arrival of a radar detection comprises the following steps carried out by computer hardware components: acquiring a complex-valued beamvector of the radar detection; processing the complex-valued beamvector by a machine learning module in the complex domain; and obtaining the direction of arrival as an output of the machine learning module.

Abstract: A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.