Abstract: A method for processing at least one image generated by at least one camera mounted on a vehicle, in particular on a motor vehicle or rail vehicle or a mobile robot having a drive. The method includes: a) determining a relative orientation of a camera coordinate system defining the alignment of the at least one camera relative to a vehicle coordinate system; b) transforming at least one image generated by the camera into a normalized image according to the ascertained relative orientation; and c) carrying out at least one image processing measure in the at least one transformed image and/or at least one image analysis measure based on the at least one transformed image, wherein measures a) and b) are carried out prior to measure c).

Abstract: A method for extracting at least one image feature from at least one image generated by at least one real camera, present in a motor vehicle, for monitoring the environment of the motor vehicle. The method includes, in a first step a), the image generated by the camera is transformed into a transformed image by means of a virtual camera which is assigned to the real camera and the optical axis of which extends along a horizontal direction with respect to the motor vehicle, according to which, in a second step b), at least one image feature is extracted from the transformed image.

Abstract: A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: A computer-implemented method for training a birds-eye-view (BEV) object detection model includes inputting a training sample into the model. The training sample includes a BEV image with multiple pixels, and multiple target confidence values. Each pixel of the pixels is associated with a target confidence value of the target confidence values. The method includes receiving as output from the model multiple predicted confidence values. Each predicted confidence value is associated with a pixel of the pixels. The method includes adjusting a parameter set of the model according to a loss. The loss is based on the predicted confidence values and the target confidence values.

Abstract: The present disclosure relates to a computer-implemented method for determining a radial velocity of an object in a surrounding of a vehicle, the method comprising the steps of: obtaining measurement data from a radar, the measurement data comprising signal data indicative of a measured radial velocity of the object, mapping the measured radial velocity of the object to a plurality of radial velocity intervals, determining, using an artificial intelligence (AI) engine, a probability value for each interval of the plurality of radial velocity intervals based on supplemental measurement data, and determining the radial velocity of the object by selecting an interval of the plurality of radial velocity intervals based on the probability value. The disclosure further relates to a corresponding apparatus, computer program and vehicle.

Abstract: A computer implemented method for determining a property of an object comprises the following steps carried out by computer hardware components: acquiring an image of a scene comprising the object; acquiring a plurality of lidar measurements of the scene; clustering the plurality of lidar measurements into a plurality of groups of lidar measurements; acquiring a radar measurement of the scene; identifying which of the plurality of groups of lidar measurements corresponds to the radar measurement; and determining the property of the object based on the image and the identified group of lidar measurements.

Abstract: A computer-implemented method for generating ground truth data may include the following steps carried out by computer hardware components: for a plurality of points in time, acquiring sensor data for a respective point in time; and for at least a subset of the plurality of points in time, determining ground truth data of the respective point in time based on the sensor data of at least one present and/or past point of time and at least one future point of time.

Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Abstract: A computer implemented method for compressing radar data comprises the following steps carried out by computer hardware components: acquiring radar data comprising a plurality of Doppler bins; determining which of the plurality of Doppler bins represent stationary objects; and determining compressed radar data based on the determined Doppler bins which represent stationary objects.

Abstract: A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.