Abstract: A method for the chronological correction of multimodal data includes: receiving a first data set from a reference sensor with measurements at different measurement timepoints, receiving a second data set of a second sensor with measurements at different measurement timepoints, each not exactly matching those of the reference sensor, reading the first and the second data sets by a neural network and identifying a respective plurality of feature vectors for the first and second data set at the respective measurement timepoints, merging and comparing the respective feature vectors, which refer to corresponding, not exactly matching measurement timepoints, by the neural network so that parameters of a chronological correction are identified, and identifying a chronological offset between the respective measurement timepoints of the reference sensor and the second sensor, and/or a corrected data set from the second sensor based on the measurement timepoints of the reference sensor.

Abstract: Learning extraction of movement information from sensor data includes providing a time series of frames of sensor data recorded by physical observation of an object, providing a time series of object boundary boxes each encompassing the object in sensor data frames, supplying the object boundary box at a time t, as well as a history of sensor data from the sensor data time series, and/or a history of object boundary boxes from the time series of object boundary boxes, prior to time t to a trainable machine learning model which predicts an object boundary box for a time t+k, comparing the predicted object boundary box with a comparison box obtained from the time series of object boundary boxes for the time t+k, evaluating a deviation between the predicted object boundary box and the comparison box using a predetermined cost function, and optimizing parameters which characterize the behavior of the model.

Abstract: A method is for tracking an object using an environment sensor. The object is represented by an object status. The method includes detecting a sensor value of the environment sensor, predicting a future object status of the object, and updating the object status using a Bayesian filter. The updating includes using an artificial intelligence module (“AI module”). The AI module is trained such that the detected sensor value is associated with the object and the object status of the object is updated based on the predicted future object status of the object and the detected sensor value.

Abstract: A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.

Abstract: A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.

Abstract: A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Abstract: A method for creating a training dataset, a validation dataset, and/or a test dataset for an AI module from measurement data includes dividing the measurement data into divided portions based on time periods, applying a mathematical function to the divided portions of the measurement data in order to obtain signatures representing the divided portions, determining a measure of a frequency of occurrence of a respective signature of the obtained signatures, and creating the training dataset, the validation dataset, and/or the test dataset from the measurement data based on the determined measure of the frequency.

Abstract: A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Abstract: A method is described for segmenting an occupancy grid for a surroundings model of a driver assistance system for a vehicle. The method encompasses a step of reading in an occupancy grid having a plurality of grid cells, each grid cell of the plurality of grid cells having assigned to it a grid cell information item that encompasses an information item about a degree of occupancy and at least one additional information item about the grid cell; and a step of assigning at least one object and/or a plurality of objects to the plurality of grid cells using the grid cell information item, in order to segment the occupancy grid for a surroundings model.

Abstract: A method for situation analysis for a driver assistance system on board a motor vehicle includes steps of providing a representation of the surroundings of the motor vehicle, the representation including a system of cells that are reversibly unambiguously associated with predetermined sections of the surroundings, scanning the surroundings of the motor vehicle with the aid of at least one sensor in order to detect an obstacle, and marking cells, based on the scan, in such a way that a cell that is associated with an obstacle-free section of the surroundings carries a first marker, and a cell that is associated with a section of the surroundings in which an obstacle has been detected carries a second marker. A morphological filter is subsequently used on the representation in order to provide cells, which are not accessible by the motor vehicle, with the second marker.

Abstract: In a method for evaluating the behavior of the driver in a vehicle, sensor data are ascertained and the driver behavior is evaluated on the basis of the sensor data. A distinction is made among various behavior categories and the sensor data are allocated to each behavior category with a probability. The behavior category is detected as applicable if the probability exceeds a threshold value.

Abstract: A method for situation analysis for a driver assistance system on board a motor vehicle includes steps of providing a representation of the surroundings of the motor vehicle, the representation including a system of cells that are reversibly unambiguously associated with predetermined sections of the surroundings, scanning the surroundings of the motor vehicle with the aid of at least one sensor in order to detect an obstacle, and marking cells, based on the scan, in such a way that a cell that is associated with an obstacle-free section of the surroundings carries a first marker, and a cell that is associated with a section of the surroundings in which an obstacle has been detected carries a second marker. A morphological filter is subsequently used on the representation in order to provide cells, which are not accessible by the motor vehicle, with the second marker.

Abstract: A method is described for segmenting an occupancy grid for a surroundings model of a driver assistance system for a vehicle. The method encompasses a step of reading in an occupancy grid having a plurality of grid cells, each grid cell of the plurality of grid cells having assigned to it a grid cell information item that encompasses an information item about a degree of occupancy and at least one additional information item about the grid cell; and a step of assigning at least one object and/or a plurality of objects to the plurality of grid cells using the grid cell information item, in order to segment the occupancy grid for a surroundings model.

Abstract: A robotic controller using schemata, the schemata being a set of parameterized sequences of motor commands in order to make a robot to achieve a set goal, the parameters of the sequences being gained from the state variables of the robotic controller, a robotic controller comprising an interface for supplying sensory input to the robotic controller. A schemata state memory (1) structure supplied with either input from a schemata recognition module (4) or input from an inverse model module (2) or combinations of them. An inverse model module (2) for generating motor commands based on state variables and stored schemata, a forward model module (3) for predicting state variables based on state variables and stored schemata, and a schemata recognition module (4) for selecting a schemata based on supplied state variables of the robot controlled by the robotic controller.

Abstract: The invention relates to the field of automated processing of speech signals and particularly to a method for tracking the formant frequencies in a speech signal, comprising the steps of: obtaining an auditory image of the speech signal; sequentially estimating formant locations; segmenting the frequency range into sub-regions; smoothing the obtained component filtering distributions; and calculating exact formant locations.

Abstract: A robotic controller using schemata, the schemata being a set of parameterized sequences of motor commands in order to make a robot to achieve a set goal, the parameters of the sequences being gained from the state variables of the robotic controller, a robotic controller comprising an interface for supplying sensory input to the robotic controller. A schemata state memory (1) structure supplied with either input from a schemata recognition module (4) or input from an inverse model module (2) or combinations of them. An inverse model module (2) for generating motor commands based on state variables and stored schemata, a forward model module (3) for predicting state variables based on state variables and stored schemata, and a schemata recognition module (4) for selecting a schemata based on supplied state variables of the robot controlled by the robotic controller.

Abstract: The invention relates to the field of automated processing of speech signals and particularly to a method for tracking the formant frequencies in a speech signal, comprising the steps of: obtaining an auditory image of the speech signal; sequentially estimating formant locations; segmenting the frequency range into sub-regions; smoothing the obtained component filtering distributions; and calculating exact formant locations.