Abstract: A computer-implemented method for classification of at least one object in an environment of a vehicle. The method includes: collecting first data from a first sensor within a first data collecting frame; collecting second data from at least a second sensor within a second data collecting frame; determining a first object representation using the first data; determining a second object representation using the second data; updating the first and/or second object representation depending on an arrival of third data from the at least second sensor collected in a third data collecting frame after the first data collecting frame; fusing the first and second representation to determine an updated representation of the object based on the received data; applying the updated representation for training the data-driven model as input data for a data-driven model to obtain output data containing an information about a classification of the detected object.

Abstract: A method for detecting an object includes providing respective spatial coordinates relating to a multiplicity of reflection signals of a frame of a radar sensor system and providing a measurement attribute of a first portion of the multiplicity of reflection signals. The respective spatial coordinates of the multiplicity of reflection signals are transformed into an occupancy grid. An occupancy grid is generated with the multiplicity of reflection signals being spatially represented in the occupancy grid by mapping the respective spatial coordinates of the multiplicity of reflection signals in the occupancy grid and assigning the respective first measurement attribute of the multiplicity of reflection signals to the spatial representation of the multiplicity of reflection signals. An input tensor is generated using the occupancy grid for a trained neural network for detecting the at least one object. The object is detected using the input tensor and the trained neural network.

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: 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 system, a device, and a method for detecting objects by separately processing point clouds including information about the surroundings of the system and/or the device. The method includes: separating a point cloud into a plurality of point clouds according to one or more features of a plurality of features of each point of the point cloud; preprocessing the plurality of point clouds in a plurality of input paths of a network corresponding to the respective point cloud; fusing the output data of the plurality of input paths of the network; and further processing the fused output data in the network to detect the objects.

Abstract: A method for improved positioning of an ego vehicle that includes a localization system, a 3D object recognition module, and a position amalgamation module is disclosed. The method includes a) determining the position of the ego vehicle for the current time step with the localization system, b) determining a position of at least one adjacent vehicle in the vicinity of the ego vehicle with the 3D object recognition module, c) amalgamating the position of the ego vehicle determined in step a) with the position of the at least one adjacent vehicle determined in step b) to form a position amalgamation result with the position amalgamation module, and d) determining the position of the ego vehicle for the next time step with the localization system, taking into account the amalgamation result.

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 detecting an object includes providing respective spatial coordinates relating to a multiplicity of reflection signals of a frame of a radar sensor system and providing a measurement attribute of a first portion of the multiplicity of reflection signals. The respective spatial coordinates of the multiplicity of reflection signals are transformed into an occupancy grid. An occupancy grid is generated with the multiplicity of reflection signals being spatially represented in the occupancy grid by mapping the respective spatial coordinates of the multiplicity of reflection signals in the occupancy grid and assigning the respective first measurement attribute of the multiplicity of reflection signals to the spatial representation of the multiplicity of reflection signals. An input tensor is generated using the occupancy grid for a trained neural network for detecting the at least one object. The object is detected using the input tensor and the trained neural network.

Abstract: A method for processing sensor data, wherein the sensor data are present as a point cloud of individual points. The points are projected into at least two rasters with different resolutions. Points from a subregion of the point cloud are projected into a first raster with higher resolution and further points of the point cloud are projected into a second raster with lower resolution. Attributes from the first raster are compressed and arranged in the second raster before the attributes from the second raster are compressed.

Abstract: The invention relates to a method for detecting multiple objects (O1, O2) from point cloud data using a transformer with attention model, wherein the state of the tracked objects (O1, O2) is stored in a feature space. The following steps are carried out: a. calculating feature vectors from the point cloud data by means of a backbone (2), wherein the feature vectors serve as key vectors (ki) and value vectors (?i) for the transformer; b. calculating first anchor positions (?i(0)) for a first layer (s0) of the transformer from the point cloud data using a sampling method (4); c. ascertaining feature vectors from the first anchor positions (?i(0)) using an encoding (5), wherein the feature vectors serve as object queries (?i(0)) for the first layer (s0) of the transformer; d. ascertaining result feature vectors (z1(0)) in the first layer (s0) of the transformer from the object queries (?i(0)) and the key vectors (ki) and value vectors (?i) using the first layer (s0) of a decoder (6) of the transformer; e.

Abstract: A method for controlling a robot device includes (i) receiving, from each sensor of a plurality of sensors, a respective sensor data set from the sensor, (ii) determining, for each object of a set of objects containing at least one object, for each of a plurality of different combinations of the sensor data sets, a position prediction for the object by way of sensor data fusion of the sensor data sets according to the combination of the sensor data sets, (iii) determining, for each object of the set of objects, for each pair of a plurality of pairs of combinations, a distance between the position predictions determined for the object according to the combinations of the pair, (iv) feeding the determined distances to a neural network trained to determine confidence information for the position predictions from distances between position predictions for the pairs of combinations, and (v) controlling the robot device using one or a plurality of the position predictions taking into account the confidence informat

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 for object detection of an object based on measurement data from at least one point-based sensor capturing the object. The measurement data, which are based on a point cloud having a plurality of points and associated features, are processed in that, in a point-based first processing step having at least one processing level, the input-side features of the point cloud are realized as learned features, and are enriched at least by information about relationships between the points, and in a grid-based second processing step having at least one processing level, the learned features are then transferred onto a model grid having a plurality of grid cells, and cell-related output data are then generated. An image detection device, a computer program, and a storage unit, are also described.

Abstract: A method for identifying uncertainties during the detection and/or tracking of multiple objects from point cloud data using a transformer with an attention model. The state of the tracked objects is stored in the feature space. The method includes: calculating feature vectors from the point cloud data by means of a backbone, wherein the feature vectors serve as key vectors for the transformer; calculating anchor positions from the point cloud data by means of a sampling method; ascertaining feature vectors from the anchor positions using an encoding, wherein the feature vectors serve as object queries for the transformer; calculating attention weights for cross-attention from the object queries and a spatial structure used by the backbone; determining the greatest attention weights of the transformer for each object query; calculating a covariance matrix for the greatest attention weights; calculating the determinant of the covariance matrix to obtain an attention spread.

Abstract: A method for identifying uncertainties during the detection and/or tracking of multiple objects from point cloud data using a transformer with an attention model. The state of the tracked objects is stored in the feature space. The method includes: calculating feature vectors from the point cloud data by means of a backbone, wherein the feature vectors serve as key vectors for the transformer; calculating anchor positions from the point cloud data by means of a sampling method; ascertaining feature vectors from the anchor positions using an encoding, wherein the feature vectors serve as object queries for the transformer; calculating attention weights for cross-attention from the object queries and a spatial structure used by the backbone; determining the greatest attention weights of the transformer for each object query; calculating a covariance matrix for the greatest attention weights; calculating the determinant of the covariance matrix to obtain an attention spread.

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 of generating input data for a machine learning model includes determining, for a sensor, a point cloud with points detected by the sensor from surfaces in the environment of the sensor, generating a preliminary target sensor point cloud for a target sensor by transforming, for the sensor, points of the determined point cloud into points from the perspective of the target sensor according to the relative position of the target sensor to the sensor, generating a target sensor point cloud for the target sensor by using the preliminary target sensor point cloud, wherein points which, due to one or more surfaces for which points exist in the preliminary target sensor point cloud, are not detectable by the target sensor are eliminated in the target sensor point cloud, and using the target sensor point cloud as input for the machine learning 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.