Abstract: A radar-based environmental detection system for motor vehicles. The system includes at least one radar sensor for providing location data regarding objects in the environment of the motor vehicle, and including a neural network for converting the location data into an environmental model which represents spatio-temporal object data of the objects. The neural network is conditioned to give priority to outputting environmental models in which at least one predetermined physical relationship between the location data and the object data is satisfied.

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 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 device and method for triggering an automatic response of a motor vehicle to an imminent accident situation by using data in a processing unit to create a traffic situation model of the existing traffic situation, analyzing a traffic situation and ascertaining response options, determining sequences to be expected beyond the primary accident incorporating subsequent movements of the road users and objects involved leading to secondary accidents or further subsequent accidents or potentially arising further hazardous situations, computing a probability distribution and/or an extent of injuries to persons and/or damage to objects of the road users and objects involved as a function of the response options, selecting that response option that is expected to have the smallest total degree of probability or the smallest extent of injury to persons and/or damage to objects overall for all road users and objects, and outputting control signals to initiate the selected response option.