Abstract: A computer-implemented method is provided for automatically annotating sensor data frames of a spatial sensor and of an area sensor with a spatially overlapping measuring range. Received sensor data frames are initially independently annotated, a bounding box being assigned to each recognized object in the sensor data frame. The sensor data frames of the spatial sensor and the sensor data frames of the area sensor are grouped, based on a temporal correlation. The three-dimensional bounding box for an object is projected into the image plane of the area sensor. If a measure of quality for a match between the projected bounding box and the two-dimensional bounding box is above a predefined threshold value, the boxes are assigned to the same object, and this object is not further checked. Attributes of the object may be subsequently determined.

Abstract: A computer-implemented method for automatically annotating sensor data includes: receiving a multiplicity of sensor data frames; annotating the multiplicity of sensor data frames using at least one neural network, wherein the annotating comprises assigning at least one data point to each sensor data frame and assigning at least one state attribute to each data point; grouping the data points on the basis of the at least one state attribute, wherein a first group comprises data points for which the at least one state attribute is in a defined value range; selecting a first sample of one or more data points from the first group; and determining a quality metric for the one or more data points in the first sample.

Abstract: A computer-implemented method for annotating driving scenario sensor data, including the steps of receiving raw sensor data, the raw sensor data comprising a plurality of successive LIDAR point clouds and/or a plurality of successive camera images, recognizing objects in each image of the camera data and/or each point cloud using one or more neural networks, correlating objects within successive images and/or point clouds, removing false positive results on the basis of plausibility criteria, and exporting the annotated sensor data of the driving scenario.

Abstract: A computer-implemented method for automatically annotating frames of sensor data includes: receiving the frames of sensor data; grouping the frames into a plurality of packets based on at least one condition attribute, wherein the at least one condition attribute describes at least one environment condition that existed while a respective frame of sensor data was being recorded; annotating frames from a first packet using a neural network, wherein the annotating comprises assigning at least one data point to each frame, wherein the first packet comprises frames for which the at least one condition attribute is in a selected value range; selecting a first sample of one or more frames from the first packet and determining a quality measure for data points of the first sample; and ascertaining that the quality measure for the first sample is below a predefined threshold.

Abstract: A computer-implemented method includes: identifying and annotating at least one object in received image, video, and/or point cloud data, wherein the identifying and/or annotating is automatically performed at least in part; calculating the numerical value of the annotation of the at least one object, wherein the numerical value is calculated at least in part on the basis of a degree of the correlation of a dimension of a visual annotation in relation to a dimension of the at least one object and/or of a correlation of a conceptual identifier of the at least one object with the at least one object and/or of a conceptual identifier of at least one sensor detecting the at least one object with the at least one sensor; and assigning the calculated numerical value to the at least one object.