Abstract: A battery structure including a protective device, a protective device for a battery housing, and a fiber-matrix semi-finished product for producing the protective device.

Abstract: A composite component, a motor vehicle component or a building component comprising the composite component, to a method for producing the composite component and to the use of the composite component.

Abstract: Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

Abstract: A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

Abstract: The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.

Abstract: A method is provided for predicting trajectories of a plurality of road users. For each road user, a set of characteristics detected by a perception system of a vehicle is determined, wherein the set of characteristics includes specific characteristics associated with a predefined class of road users. The set of characteristics is transformed to a set of input features for a prediction algorithm via a processing unit of the vehicle, wherein each set of input data comprises the same predefined number of data elements. At least one respective trajectory for each of the road users is determined by applying the prediction algorithm to the input data.

Abstract: A method is provided for predicting respective trajectories of a plurality of road users. Trajectory characteristics of the road users are determined with respect to a host vehicle via a perception system, wherein the trajectory characteristics are provided as a joint vector describing respective dynamics of each of the road users for a predefined number of time steps. The joint vector of the trajectory characteristics is encoded via an algorithm which included an attention algorithm for modelling interactions of the road users. The encoded trajectory characteristics and encoded static environment data obtained for the host vehicle are fused in order to provide fused encoded features. The fused encoded features are decoded in order to predict the respective trajectory of each of the road users for a predetermined number of future time steps.

Abstract: A computer-implemented method for collision threat assessment of a vehicle includes obtaining context information for the surrounding of the vehicle, including information about at least one road user. The method includes determining ego occupancy information for multiple possible future locations of the vehicle at multiple future points in time based on the context information. The method includes determining road user occupancy information for multiple possible future locations of the at least one road user at multiple future points in time based on the context information. The method includes fusing the ego occupancy information and the road user occupancy information to obtain fused occupancy information at each future point in time. The method includes determining a collision threat value based on the fused occupancy information.

Abstract: Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

Abstract: A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

Abstract: The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.