Abstract: A computer-implemented method analyzes radar data. The method includes acquiring the radar data from one or more radar sensors. The method includes processing the radar data to derive output data including spatial points with associated features. The method includes receiving the output data as input data. The method includes analyzing the radar data based on the input data.

Abstract: The present disclosure relates to a computer-implemented method, apparatus, computer program, and a vehicle that includes the apparatus for determining one or more features of a surrounding of a vehicle. In aspects, a method includes obtaining sensor data comprising measurement points, the measurement points being arranged in a data space; determining an abstract signal processing space based on a distribution of the measurement points in the data space; mapping the measurement points into the abstract signal processing space; and determining the one or more features in the abstract signal processing space based on an arrangement of the measurement points in the abstract signal processing space.

Abstract: The disclosure includes a computer-implemented method for predicting trajectory data of an object including: acquiring radar data of the object; determining a parametrization of the trajectory data of the object based on the radar data; and determining a variance of the trajectory data of the object based on the radar data. The trajectory data of the object includes a position of the object and a direction of the object. The parametrization includes a plurality of parameters and a polynomial of a pre-determined degree. The parameters include a plurality of coefficients related to elements of a basis of the polynomial space of polynomials of the pre-determined degree.

Abstract: A method is provided for classifying a tracked object in an environment of a vehicle. The vehicle includes a plurality of radar sensors and a processing device configured to establish a neural network. According to the method, local radar detections are captured from an object in the environment of the vehicle via the radar sensors. Based on the local radar detections, point features and tracker features are determined. The point features are encoded via point encoding layers of the neural network, whereas the tracker features are encoded via track encoding layers of the neural network. A temporal fusion of the encoded point features and the encoded tracker features is performed via temporal fusion layers of the neural network. The tracked object is classified based on the fused encoded point and tracker features via classifying layers of the neural network.

Abstract: A computer implemented method for determining a direction of arrival of a radar detection comprises the following steps carried out by computer hardware components: acquiring a complex-valued beamvector of the radar detection; processing the complex-valued beamvector by a machine learning module in the complex domain; and obtaining the direction of arrival as an output of the machine learning module.

Abstract: A computer implemented method for predicting a trajectory of an object comprises the following steps carried out by computer hardware components: acquiring radar data of the object; determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; determining second intermediate data based on the first intermediate data using a feature pyramid; and predicting the trajectory of the object based on the second intermediate data.

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.