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 assigning data points from first data to one of several objects, wherein the first data comprises at least three spatial dimensions. Groups of projected data points are formed and assigned to the respective objects, taking into account received information data from two-dimensional bounding polygons and from a bin of the data point by an assignment method such that for overlapping polygons in which projected data points are located within more than one two-dimensional bounding polygon, the groups are formed such that no bin is assigned to more than one of the overlapping polygons at the same time. A device is also provided for data processing to execute the method, and a computer program product, and a computer-readable medium on which the above computer program product is stored.

Abstract: A computer-implemented method for ascertaining a three-dimensional bounding volume, preferably a bounding box, for an object displayed on first data, the first data having at least three spatial dimensions, information data of a two-dimensional minimum bounding polygon being received for the object displayed on second data, a projection matrix, which defines a mapping of a three-dimensional data point of the first data onto a two-dimensional data point in the second data being received, and the three-dimensional bounding volume of the object being ascertained with the aid of a mathematical optimization procedure and a target function, the target function comprising fewer than 100,000 terms. The invention also relates to a device for processing data, which comprises computer components for carrying out the above method, as well as a computer program product, comprising commands, which, when the program is executed by a computer, prompt the latter to carry out the above method.

Abstract: A method for calibrating the relative orientation of a spatial sensor and an area sensor includes: receiving a plurality of sensor frames of the spatial sensor and the area sensor; grouping sensor frames of the spatial sensor with sensor frames of the area sensor based on temporal correlation; selecting at least one group of frames comprising a spatial sensor frame and an area sensor frame; receiving annotations for the sensor frames in the at least one selected group, wherein the annotations comprise bounding boxes for detected objects; projecting the corners of a three-dimensional bounding box in the spatial sensor frame to an image plane of the area sensor, to produce a projected rectangle; calculating an energy term based on the coordinates of the corners of the projected rectangle and the corresponding bounding box in the area sensor frame; and optimizing the relative sensor orientation by minimizing the energy term.

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 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.