Abstract: A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

Abstract: A method is provided for determining patterns related to an environment of a host vehicle from sequentially recorded data. Respective sets of characteristics detected by a perception system in the environment of the host vehicle are determined for a current point in time and for a predefined number of previous points in time. A set of current input data associated with the sets of characteristics is generated for the current point in time, and a set of memory data is generated by aggregating the sets of characteristics for the previous points in time. An attention algorithm is applied to the set of current input data and to the set of memory data to generate a joined spatial-temporal data set, and at least one pattern is determined for the environment of the host vehicle from the joined spatial-temporal data set.

Abstract: A computer implemented method for processing radar reflections includes receiving radar reflections by at least one radar sensor; determining a target angle under which radar reflections related to a potential target are received by the at least one radar sensor; and determining an energy of radar reflections received by the at least one radar sensor under a pre-determined angular region around the target angle.

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: 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 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: A method for determining information on an expected trajectory of an object comprises: determining input data being related to the expected trajectory of the object; determining first intermediate data based on the input data using a machine-learning method; determining second intermediate data based on the input data using a model-based method; and determining the information on the expected trajectory of the object based on the first intermediate data and based on the second intermediate data.

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: Scene classification method and apparatus for a vehicle sensor system. Feature maps generated from sensor data provided by the vehicle sensor system are received at an input. The feature maps are processed using longitudinal and lateral feature pooling to generate longitudinal and lateral feature pool outputs. Inner products are then generated from the longitudinal and lateral feature pool outputs. The scene is then classified based on the generated inner products.

Abstract: The present disclosure describes a computer-implemented method for controlling a vehicle. In aspects, the computer-implemented method includes acquiring sensor data from a sensor, determining first processed data related to a first area around the vehicle based on the sensor data using a machine-learning method, and determining second processed data related to a second area around the vehicle based on the sensor data using a conventional method. The second area may include a subarea of the first area. In addition, the computer-implemented method includes controlling the vehicle based on the first processed data and the second processed data.

Abstract: The present disclosure is directed at systems and methods for determining objects around a vehicle. In aspects, a system includes a sensor unit having at least one radar sensor arranged and configured to obtain radar image data of external surroundings to determine objects around a vehicle. The system further includes a processing unit adapted to process the radar image data to generate a top view image of the external surroundings of the vehicle. The top view image is configured to be displayed on a display unit and useful to indicate a relative position of the vehicle with respect to determined objects.

Abstract: A method of multi-sensor data fusion includes determining a plurality of first data sets using a plurality of sensors, each of the first data sets being associated with a respective one of a plurality of sensor coordinate systems, and each of the sensor coordinate systems being defined in dependence of a respective one of a plurality of mounting positions for the sensors; transforming the first data sets into a plurality of second data sets using a transformation rule, each of the second data sets being associated with a unified coordinate system, the unified coordinate system being defined in dependence of at least one predetermined reference point; and determining at least one fused data set by fusing the second data sets.

Abstract: A computer implemented method for radar data processing includes the following steps carried out by computer hardware components: acquiring radar data from a radar sensor mounted on a vehicle; determining at least one of a speed of the vehicle or a steering wheel angle of the vehicle; and determining a subset of the radar data for processing based on the at least one of the speed of the vehicle or the steering wheel angle of the vehicle.

Abstract: A method is provided for training a machine-learning algorithm which relies on primary data captured by at least one primary sensor. Labels are identified based on auxiliary data provided by at least one auxiliary sensor. A care attribute or a no-care attribute is assigned to each label by determining a perception capability of the primary sensor for the label based on the primary data and based on the auxiliary data. Model predictions for the labels are generated via the machine-learning algorithm. A loss function is defined for the model predictions. Negative contributions to the loss function are permitted for all labels. Positive contributions to the loss function are permitted for labels having a care attribute, while positive contributions to the loss function for labels having a no-care attribute are permitted only if a confidence of the model prediction for the respective label is greater than a threshold.

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: Provided is a method for object detection in a surrounding of a vehicle using a deep neural network, comprising: inputting a first set of sensor-based data for a first Cartesian grid having a first spatial dimension and a first spatial resolution into a first branch of the deep neural network; inputting a second set of sensor-based data for a second Cartesian grid having a second spatial dimension and a second spatial resolution into a second branch of the deep neural network; providing an interaction between the first branch of the deep neural network and the second branch of the deep neural network at an intermediate stage of the deep neural network; and fusing a first output of the first branch of the deep neural network and a second output of the second branch of the deep neural network to detect the object in the surrounding of the vehicle.

Abstract: A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

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 training a machine-learning method comprises the following steps carried out by computer hardware components: determining measurement data from a first sensor; determining approximations of ground truths based on a second sensor; and training the machine-learning method based on the measurement data and the approximations of ground truths; wherein approximations of ground truths of lower-approximation quality have a lower effect on the training than approximations of ground truths of higher-approximation quality.

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.