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: A computer implemented method to determine ego motion of a vehicle, the vehicle having at least one radar emitter with a plurality of reception antennae, the method including the operations of acquiring, from the reception antennae, different frames of radar data of the vehicle surrounding environment, each frame being acquired at a different time; deriving from the radar data of each different frame, an environment map of the vehicle surrounding environment; and deriving the ego motion of the vehicle by: merging environment maps from at least two different frames into one accumulated map, computing, from the accumulated map, a motion vector for each pixel of the accumulated map, and extracting, from the accumulated map, a mask map including a tensor mapping a weight for each pixel of the accumulated map.

Abstract: Provided is a method and system for tracking a motion of information in a spatial environment of a vehicle. Sensor-based data regarding the spatial environment is acquired for a plurality of timesteps, the sensor-based data defining the information in spatially resolved cells. For each of the timesteps, the sensor-based data is input into a recurrent neural network, RNN, having one or more internal memory states. For each of the timesteps, the internal states of the RNN are transformed by using a motion map describing a speed and/or a direction of motion of the information of the spatially resolved cells individually. For each of the plurality of timesteps, the transformed internal states are used in a processing of the RNN to track the motion of the information in the environment of the moving vehicle.

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.

Abstract: A computer implemented method for detecting objects includes providing signal representation data comprising range information, velocity information and angular information; for each of a plurality of spatial scales, determining respective scaled data for the respective spatial scale based on the signal representation data, to obtain a plurality of scaled data; providing the plurality of scaled data to a plurality of detectors; and each detector carrying out object detection based on at least one of the plurality of scaled data.