Abstract: Disclosed is a computer-implemented method for tracking an object. The method includes obtaining a motion of the object within a time frame based on data received from a sensing system. The method includes splitting the time frame into multiple sub-intervals. The method includes determining, using a tracking system, a position of the object in a next time frame based on sub-motions of the object within the sub-intervals.

Abstract: A processing method for processing data from a sensor system, the method including the steps of receiving sensor data acquired from the sensor system including a set of data points associated with a field of view (1) of at least one sensor in an environment. Data points located within one or more areas of interest (2) are selected, the one or more areas of interest being selected based on a set of criteria. The selected data points are then processed to detect objects or perform semantic segmentation within the one or more areas of interest. The one or more areas of interest may be selected based on a scenario determination of a vehicle (10) in the environment.

Abstract: The present disclosure relates to methods and systems for object tracking, for example for object detection and grid segmentation using recurrent neural networks. A computer implemented method for object tracking comprises the following steps carried out by computer hardware components: providing random values as a hidden state of a trained neural network for an initial time step, wherein the hidden state represents an encoding of sensor data acquired over consecutive time steps in a grid structure, wherein the hidden state further represents an offset indicating a movement of the object between the consecutive time steps; iteratively determining an updated hidden state by processing a present hidden state and present sensor data using the trained neural network; and determining object tracking information based on the updated hidden state.

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 determining information related to an environment of a vehicle comprises the following steps carried out by computer hardware components: determining a point cloud based on measurement data, the point cloud comprising a plurality of points; determining respective features for each point of the point cloud; determining a grid structure based on the point cloud; determining a processed grid structure based on processing the grid structure using an artificial neural network; and determining at least one final grid structure based on the point cloud, the respective features for each point of the point cloud, and the processed grid structure.

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 method is provided for determining patterns related to an environment of a host vehicle. Characteristics are detected by a perception system of the host vehicle within the environment of the host vehicle. At least two processing levels having different scales are defined. For each processing level, respective current input data associated with the characteristics for a current point in time is combined with respective memory data related to the characteristics for previous points in time in order to generate joint spatial-temporal data for the respective processing level. An attention algorithm is applied to the joint spatial-temporal data of all processing levels for generating an aggregated data set, and at least one pattern related to the environment is determined from the aggregated data set.

Abstract: A method is provided for semantic segmentation of an environment of a vehicle. Via a processing device, a grid of cells is defined dividing the environment of the vehicle. A radar point cloud is received from a plurality of radar sensors, and at least one feature of the radar point cloud is assigned to each grid cell. By using a neural network including deterministic weights, high-level features are extracted for each grid cell. Several classes are defined for the grid cells. For layers of a Bayesian neural network, various sets of weights are determined probabilistically. Via the Bayesian neural network, confidence values are determined for each class and for each grid cell based on the high-level features and based on the various sets of weights in order to determine a predicted class and an extent of uncertainty for the predicted class for each grid cell.

Abstract: Disclosed is a computer-implemented method for creating a data sample for training semantic segmentation models usable in a vehicle assistance system. The method includes obtaining a first point cloud representing a surrounding of a vehicle at a first point in time and a second point cloud representing the surrounding of the vehicle at a second point in time. The method includes joining the first and second point cloud to obtain a global point cloud representing the surrounding of the vehicle over a duration of the first point in time and the second point in time. The method includes creating a representation of the surrounding based on the global point cloud. The method includes extracting from the representation a semantic map and one or more elevation maps. The method includes providing the semantic map and the one or more elevation maps as the data sample.

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: A computer implemented method for displaying information to an occupant of a vehicle comprises the following steps carried out by computer hardware components: determining data associated with radar responses captured by at least one radar sensor mounted on the vehicle; and determining a visualization of the data; and displaying the visualization to the occupant of the vehicle.

Abstract: Disclosed is a computer-implemented method for tracking an object. The method includes obtaining a motion of the object within a time frame based on data received from a sensing system. The method includes splitting the time frame into multiple sub-intervals. The method includes determining, using a tracking system, a position of the object in a next time frame based on sub-motions of the object within the sub-intervals.

Abstract: The present disclosure relates to methods and systems for object tracking, for example for object detection and grid segmentation using recurrent neural networks. A computer implemented method for object tracking comprises the following steps carried out by computer hardware components: providing random values as a hidden state of a trained neural network for an initial time step, wherein the hidden state represents an encoding of sensor data acquired over consecutive time steps in a grid structure, wherein the hidden state further represents an offset indicating a movement of the object between the consecutive time steps; iteratively determining an updated hidden state by processing a present hidden state and present sensor data using the trained neural network; and determining object tracking information based on the updated hidden state.

Abstract: A processing method for processing data from a sensor system, the method including the steps of receiving sensor data acquired from the sensor system including a set of data points associated with a field of view (1) of at least one sensor in an environment. Data points located within one or more areas of interest (2) are selected, the one or more areas of interest being selected based on a set of criteria. The selected data points are then processed to detect objects or perform semantic segmentation within the one or more areas of interest. The one or more areas of interest may be selected based on a scenario determination of a vehicle (10) in the environment.

Abstract: A method is provided for semantic segmentation of an environment of a vehicle. Via a processing device, a grid of cells is defined dividing the environment of the vehicle. A radar point cloud is received from a plurality of radar sensors, and at least one feature of the radar point cloud is assigned to each grid cell. By using a neural network including deterministic weights, high-level features are extracted for each grid cell. Several classes are defined for the grid cells. For layers of a Bayesian neural network, various sets of weights are determined probabilistically. Via the Bayesian neural network, confidence values are determined for each class and for each grid cell based on the high-level features and based on the various sets of weights in order to determine a predicted class and an extent of uncertainty for the predicted class for each grid cell.

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.