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 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 training a trainable module that maps input variables onto output variables through an internal processing chain. A learning data set is provided including learning values of the input variables and associated learning values of the output variables. A list of discrete values is provided from which the parameters characterizing the internal processing chain are to be selected, the discrete values being selected such that they can be stored without loss of quality. The learning values are mapped by the trainable module onto assessment values of the output variables. A cost function is evaluated that characterizes deviations of the assessment values of the output variables from the learning values and of at least one parameter of the internal processing chain from at least one discrete value in the list. At least one parameter of the internal processing chain is adjusted to improve the value of the cost function.

Abstract: A method for compressing a neural network. The method includes: defining a maximum complexity of the neural network; ascertaining a first cost function; ascertaining a second cost function, which characterizes a deviation of a current complexity of the neural network in relation to the defined complexity; training the neural network in such a way that a sum of a first and a second cost function is optimized as a function of parameters of the neural network; and removing those weightings whose assigned scaling factor is smaller than a predefined threshold value.

Abstract: A method for determining the spatial orientation of an object from at least one measuring signal which includes the response of the object to electromagnetic interrogation radiation. A method for predicting the trajectory of at least one object from at least one measuring signal which includes the response of the object to electromagnetic interrogation radiation, in conjunction with a scalar velocity of the object. A method for training a classifier and/or a regressor.

Abstract: A method for determining a state of the surrounding area of a vehicle includes: receiving sensor data of at least one surrounding-area sensor of the vehicle; feeding at least a first portion of the sensor data into at least one first classifier; generating an intermediate probability from the first portion of the sensor data, using the first classifier; feeding at least a second portion of the sensor data and the at least one intermediate probability into a second classifier; generating a final probability of the state of the surrounding area from the second portion of the sensor data and the at least one intermediate probability, using the second classifier.

Abstract: A method for generating radar reflection points comprising the steps of: providing a plurality of predefined radar reflection points of at least one first object detected by a radar and at least one first scenario description describing a first environment related to the detected first object; converting the predefined radar reflection points into at least one first power distribution pattern image related to a distribution of a power returning from the detected first object; training a model based on the first power distribution pattern image and the first scenario description; providing at least one second scenario description describing a second environment related to a second object; generating at least one second power distribution pattern image related to a distribution of a power returning from the second object based on the trained model and the second scenario description; and sampling the second power distribution pattern image.

Abstract: A method for determining a state of the surrounding area of a vehicle includes: receiving sensor data of at least one surrounding-area sensor of the vehicle; feeding at least a first portion of the sensor data into at least one first classifier; generating an intermediate probability from the first portion of the sensor data, using the first classifier; feeding at least a second portion of the sensor data and the at least one intermediate probability into a second classifier; generating a final probability of the state of the surrounding area from the second portion of the sensor data and the at least one intermediate probability, using the second classifier.

Abstract: The invention relates to a real time-capable analysis of a sequence of electronic images for estimating the pose of a movable object captured by means of the images. The invention further relates to implementing the invention in software and, in this connection, to a computer-readable medium that stores commands, the execution of which causes the method according to the invention to be carried out. The invention proceeds from a skeleton model, which is described by a small number of nodes in 3D space and permits a good data compression of the image information when the co-ordinates of the nodes describe at any time the position of predetermined parts of the moving object. The skeleton model simultaneously represents previous knowledge of the object, by defining e.g. node pairs and optionally also node triplets in the skeleton model that describe cohesive object parts or optionally object surfaces, which are contained in the measured 2½-D image information, i.e. are visible to the camera.

Abstract: The invention relates to a real time-capable analysis of a sequence of electronic images for estimating the pose of a movable object captured by means of the images. The invention further relates to implementing the invention in software and, in this connection, to a computer-readable medium that stores commands, the execution of which causes the method according to the invention to be carried out. The invention proceeds from a skeleton model, which is described by a small number of nodes in 3D space and permits a good data compression of the image information when the co-ordinates of the nodes describe at any time the position of predetermined parts of the moving object. The skeleton model simultaneously represents previous knowledge of the object, by defining e.g. node pairs and optionally also node triplets in the skeleton model that describe cohesive object parts or optionally object surfaces, which are contained in the measured 2½-D image information, i.e. are visible to the camera.