Abstract: A lighting system (1) for a vehicle light is disclosed, including a first illuminant (L1), a second illuminant (L2) and a light guide (4), which is arranged relative to the first illuminant and the second illuminant in such a way that light emitted by the two illuminants enters the light guide and exits via a light exit section (4c) of the light guide, wherein the lighting system has a light guiding channel element (6), which is arranged between the second illuminant and the light guide, wherein the light guiding channel element has a plurality of light guiding channels (7) arranged next to one another, wherein each light guiding channel has a wall (8), which is designed in such a way that a light beam from the second illuminant gets wider in a first plane (E1) and narrower in a second plane (E2) orthogonal to the first plane.

Abstract: A method for ascertaining a physical property of an object. The method includes detecting, for each input point in time of a sequence of input points in time, sensor data including information about a physical object, using an event-based sensor; for each subsequence of a breakdown of the sequence of input points in time into multiple subsequences including: feeding the sensor data detected for the input points in time of the subsequence to a pulsed neural network which generates a first processing result of the subsequence; feeding the processing result of the subsequence to a non-pulsed neural network; and processing the processing result of the subsequence by non-pulsed neurons of one or multiple first layer(s) of the non-pulsed neural network for generating a second processing result of the subsequence; and feeding the second processing results of the multiple subsequences to one or multiple second layer(s) of the non-pulsed neural network.

Abstract: A lighting system (1) for a vehicle light is disclosed, including a first illuminant (L1), a second illuminant (L2) and a light guide (4), which is arranged relative to the first illuminant and the second illuminant in such a way that light emitted by the two illuminants enters the light guide and exits via a light exit section (4c) of the light guide, wherein the lighting system has a light guiding channel element (6), which is arranged between the second illuminant and the light guide, wherein the light guiding channel element has a plurality of light guiding channels (7) arranged next to one another, wherein each light guiding channel has a wall (8), which is designed in such a way that a light beam from the second illuminant gets wider in a first plane (E1) and narrower in a second plane (E2) orthogonal to the first plane.

Abstract: A housing and a circuit arrangement includes a housing body that features an electrically conductive housing base element and a housing wall element. The housing body is coated with an electrically insulating cladding layer. The housing includes a fastening device that has an electrically conductive contacting unit and a mechanical attaching unit. The electrically conductive contacting unit, in the intended installation position, is directly electrically contacted with the housing body at the electrical contacting point, extends into the circuit board compartment and is also arranged so as to be electrically conductively contacted in the circuit board compartment with a corresponding contacting unit of the circuit board. The mechanical attaching unit is arranged to be mechanically connected in the circuit board compartment to a corresponding attaching unit of the circuit board in order to mechanically affix the circuit board in the circuit board compartment.

Abstract: A semiconductor-based fuse for the disconnecting of a charging-current path for the charging of a battery of a vehicle-side high voltage electrical system of a battery-electric vehicle includes one or more semiconductor-switch elements that are switchable in a charging-current path of the battery-electric vehicle, to enable a charging of the battery via the charging-current path. The semiconductor-based fuse includes a measurement sensor system that is connectable in the charging-current path of the battery-electric vehicle, and is to detect a current flowing through the one or more semiconductor-switch elements. The semiconductor-based fuse includes a control system, which is to switch off the one or more semiconductor-switch elements upon the reaching of a threshold value of the detected current, in order to disconnect the vehicle-side high voltage electrical system from the charging-current path.

Abstract: A method for identifying potentially hazardous or at-risk objects in the surroundings of a vehicle. The method includes detecting an area of the surroundings using at least one event-based sensor, the event-based sensor including light-sensitive pixels, and a relative change of the light intensity incident upon a pixel by at least a predefined percentage prompting the sensor to output an event assigned to this pixel. The method also includes assigning events output by the sensor to objects in the area; analyzing, for at least one object to which events are assigned, the events assigned to the object with respect to present movements of the object; and ascertaining an impending movement of the object, and/or an impending change of state, of the object from the present movements. An associated computer program is also described.

Abstract: A generator for generating two- or multi-dimensional frequency representations of synthetic radar signals from a set of radar signals measured by a physical radar sensor. The generator includes a random number generator and a first AI module, which, as input, receives vectors or tensors of random values from the random number generator and uses an internal processing chain to map each such vector, respectively each such tensor, onto a two- or multi-dimensional frequency representation of a synthetic radar signal. The internal processing chain of the first AI module is parameterized by a multiplicity of parameters which are set in such a way that the two- or multi-dimensional frequency representation of the radar signal and/or at least one characteristic derived therefrom have the same distribution for the synthetic radar signals as for the measured radar signals.

Abstract: A system (1) for detecting dynamic secondary objects (55) that have a potential to intersect the trajectory (51) of a moving primary object (50), comprising a vision sensor (2) with a light-sensitive area (20) that comprises event-based pixels (21), so that a relative change in the light intensity impinging onto an event-based pixel (21) of the vision sensor (2) by at least a predetermined percentage causes the vision sensor (2) to emit an event (21a) associated with this event-based pixel (21), wherein the system (1) further comprises a discriminator module (3) that gets both the stream of events (21a) from the vision sensor (2) and information (52) about the heading and/or speed of the motion of the primary object (50) as inputs, and is configured to identify, from said stream of events (21a), based at least in part on said information (52), events (21b) that are likely to be caused by the motion of a secondary object (55), rather than by the motion of the primary object (50).

Abstract: A method for detecting an anomalous image among a dataset of images using an Adversarial Autoencoder includes training an Adversarial Autoencoder in a first training with a training dataset of images, with the Adversarial Autoencoder being optimized such that a distribution of latent representations of images of the training dataset of images approaches a predetermined prior distribution and that a reconstruction error of reconstructed images of the training dataset of images is minimized. Subsequently, anomalies are detected in the latent representation and the Adversarial Autoencoder is trained in a second training with the training dataset of images, but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder dependent on at least one of the reconstruction error of the image and a probability density under the predetermined prior distribution.

Abstract: A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.

Abstract: A method for ascertaining a physical property of an object. The method includes detecting, for each input point in time of a sequence of input points in time, sensor data including information about a physical object, using an event-based sensor; for each subsequence of a breakdown of the sequence of input points in time into multiple subsequences including: feeding the sensor data detected for the input points in time of the subsequence to a pulsed neural network which generates a first processing result of the subsequence; feeding the processing result of the subsequence to a non-pulsed neural network; and processing the processing result of the subsequence by non-pulsed neurons of one or multiple first layer(s) of the non-pulsed neural network for generating a second processing result of the subsequence; and feeding the second processing results of the multiple subsequences to one or multiple second layer(s) of the non-pulsed neural network.

Abstract: A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

Abstract: A method for identifying potentially hazardous or at-risk objects in the surroundings of a vehicle. The method includes detecting an area of the surroundings using at least one event-based sensor, the event-based sensor including light-sensitive pixels, and a relative change of the light intensity incident upon a pixel by at least a predefined percentage prompting the sensor to output an event assigned to this pixel. The method also includes assigning events output by the sensor to objects in the area; analyzing, for at least one object to which events are assigned, the events assigned to the object with respect to present movements of the object; and ascertaining an impending movement of the object, and/or an impending change of state, of the object from the present movements. An associated computer program is also described.

Abstract: A neural network conversion method and a recognition apparatus that implements the method are provided. A method of converting an analog neural network (ANN) to a spiking neural network (SNN) normalizes first parameters of a trained ANN based on a reference activation that is set to be proximate to a maximum activation of artificial neurons included in the ANN, and determines second parameters of an SNN based on the normalized first parameters.

Abstract: A generator for generating two- or multi-dimensional frequency representations of synthetic radar signals from a set of radar signals measured by a physical radar sensor. The generator includes a random number generator and a first AI module, which, as input, receives vectors or tensors of random values from the random number generator and uses an internal processing chain to map each such vector, respectively each such tensor, onto a two- or multi-dimensional frequency representation of a synthetic radar signal. The internal processing chain of the first AI module is parameterized by a multiplicity of parameters which are set in such a way that the two- or multi-dimensional frequency representation of the radar signal and/or at least one characteristic derived therefrom have the same distribution for the synthetic radar signals as for the measured radar signals.

Abstract: A generator for generating three-dimensional point clouds of synthetic LIDAR signals from a set of LIDAR signals measured with the aid of a physical LIDAR sensor. The generator includes a random generator and a first machine learning system, which receives vectors or tensors of random values from the random generator as input, and maps each such vector, or each such tensor, onto a three-dimensional point cloud of a synthetic LIDAR signal with the aid of an internal processing chain. The internal processing chain of the first machine learning system is parameterized by a plurality of parameters which are set in such a way that the three-dimensional point cloud of the LIDAR signal, and/or at least one characteristic variable derived from this point cloud, essentially has/have the same distribution for the synthetic LIDAR signals as for the measured LIDAR signals.

Abstract: A system (1) for detecting dynamic secondary objects (55) that have a potential to intersect the trajectory (51) of a moving primary object (50), comprising a vision sensor (2) with a light-sensitive area (20) that comprises event-based pixels (21), so that a relative change in the light intensity impinging onto an event-based pixel (21) of the vision sensor (2) by at least a predetermined percentage causes the vision sensor (2) to emit an event (21a) associated with this event-based pixel (21), wherein the system (1) further comprises a discriminator module (3) that gets both the stream of events (21a) from the vision sensor (2) and information (52) about the heading and/or speed of the motion of the primary object (50) as inputs, and is configured to identify, from said stream of events (21a), based at least in part on said information (52), events (21b) that are likely to be caused by the motion of a secondary object (55), rather than by the motion of the primary object (50).

Abstract: A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.

Abstract: A recurrent neural network including an input layer, a hidden layer, and an output layer, wherein the hidden layer includes hybrid memory cell units, each of the hybrid memory cell units including a first memory cells of a first type, the first memory cells being configured to remember a first cell state value fed back to each of gates to determine a degree to which each of the gates is open or closed, and configured to continue to update the first cell state value, and a second memory cells of a second type, each second memory cell of the second memory cells including a first time gate configured to control a second cell state value of the second memory cell based on phase signals of an oscillatory frequency, and a second time gate configured to control an output value of the second memory cell based on the phase signals, and each second memory cell of the second memory cells being configured to remember the second cell state value.

Abstract: A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.