Abstract: A method for operating an ego vehicle in a traffic scene includes identifying an actual state of the traffic scene, and generating a tree structure of future states of the traffic scene for each planning component of a set of different planning components based on the actual state. The method further includes predicting the future states for a predetermined number of successive time steps. The future states include a plurality of child future states and a plurality of parent future states. Each child future state results from exactly one parent future state from a preceding time step. Any number of child future states results from one parent future state. The method further includes planning at least one behavior of the ego vehicle for the traffic scene of a corresponding parent future state to predict corresponding child future states using a respective planning component of the set of different planning components.

Abstract: A method for training a machine learning model for an autonomous driving function, in particular for behavior prediction and/or behavior planning and/or for tracking one or more vehicles is disclosed.

Abstract: A computer-implemented method and system for planning the behavior of a vehicle in a traffic scene. The behavior planning pursues a specified destination. The system includes a perception level for aggregating scene-specific information and for generating at least one scene representation of the traffic scene, a neural network which carries out strategic behavior planning based on the scene representation generated by the perception level, and a downstream planning component which carries out detailed behavior planning based on the strategic behavior planning. The neural network is trained to generate a geometric behavior specification for the vehicle in the given traffic scene as a result of the strategic behavior planning. For this purpose, the neural network identifies at least one go zone that the vehicle may or should pass through to pursue the specified destination, and/or at least one no-go zone that the vehicle should avoid when pursuing the specified destination.

Abstract: A method for reducing computing operations or model components of a GNN includes (i) providing a GNN architecture in which distances and/or relative orientations between nodes can be modeled and processed in the form of edge features, (ii) providing discretization values of distances and/or relative orientations between nodes of a network graph of the GNN, (iii) encoding the discretization values into a latent space by an edge-feature encoder, (iv) performing retrievable and index-based storage of the encoded discretization values in a database; and (v) retrieving the encoded discretization values from the database by the GNN to reduce the computing operations or model components associated with the encoded discretization values.

Abstract: A computer-implemented method and system which enable a practical verification and meaningful evaluation of trajectories suggested by a planning module for an automated operating mode of a vehicle. The method includes: aggregating situation-specific information; generating a surroundings model based on the situation-specific information; ascertaining possible behaviors of the vehicle based on the situation-specific information and the surroundings model, wherein each behavior is described by a set of boundary conditions; and prioritizing the possible behaviors of the vehicle based on the boundary conditions in conjunction with a predefined set of rules. A compatibility check is carried out for a suggested trajectory and at least one of the ascertained behaviors by checking whether the suggested trajectory satisfies the boundary conditions of the respective behavior.

Abstract: A method for predicting a state of an environment of a vehicle includes determining an occupancy grid, a digital map, and a list of objects. The method further includes encoding the occupancy grid to a first occupancy grid representation in a latent space for the occupancy grid, the digital map to a first map representation in a latent space for the digital map, and the list of objects to a first object list representation in a latent space for the list of objects. The method further includes predicting, for each of one or more future points in time of the environment of the vehicle, a respective further occupancy grid representation, a respective further map representation, and a respective object list representation.

Abstract: A computer-implemented training method is for training a model for analyzing a traffic scene. The model includes a GNN encoder and an analysis decoder for generating analysis results based on latent features generated by the GNN encoder. Training data elements for the training method each include a graph representation and an image representation of a training scene and an analysis result as groundtruth information. For each training data element, a GNN set of latent features is generated using the GNN encoder based on the graph representation to generate a GNN analysis result using the analysis decoder to determine a distance between the GNN analysis result and the groundtruth information and compare it to an optimization criterion. Based on the image representation and the GNN set of latent features of the training scene, at least one further distance is determined and compared to at least one further optimization criterion.

Abstract: A computer-implemented method for behavior planning for a first participant in a traffic scene, in which a predicted behavior of at least one further participant in the traffic scene is taken into account, specifically depending on the epistemic uncertainty of the predicted behavior. For this purpose, the behavior of the further participant is predicted at regular time intervals, in each case for a prediction interval of a specified length, wherein each prediction interval includes a specified number of consecutive prediction steps. The epistemic uncertainty of the predicted behavior is determined for each prediction step of a prediction interval and is evaluated using a decision criterion to decide whether the behavior of the further participant predicted for the corresponding prediction step is taken into account in the behavior planning for the first participant.

Abstract: A computer-implemented method for predicting a behavior of at least one participant in a traffic scene. At least one AI-based prediction component is used to predict a specified number K of behavior options of the at least one participant for at least one future time segment on the basis of scene-specific information which are aggregated at a current time point. Each time segment includes a specified number T of consecutive time points. At least one current overall uncertainty value that quantifies the epistemic uncertainty of all predicted behavior options for the at least one future time segment is determined in parallel to the prediction of the individual behavior options.

Abstract: A method for predicting a future state and/or behavior of a scenario whose further development is correlated with one or more observable variables, without directly and unambiguously arising from these observable variables. In the method: measured observations of the observable variables at current points in time are processed using an encoder to form context representations; the context representations are processed using a specified processing function to form processing products; predictions for context representations of the scenario at future points in time are determined using a context predictor on the basis of at least the processing products as the sought-after prediction of the future state and/or behavior; wherein the processing function is designed to aggregate context representations from a specified time horizon prior to a point in time to form the processing product.

Abstract: Container with at least one vacuum enclosure with an access opening, especially a beverage container with a self-cooling device having a vacuum enclosure, such as a beer barrel or the like, the access opening of which is closed off by a closing mechanism after the vacuum is produced, a chamber being provided, which is downstream from the access opening and in which a valve element is disposed, which is opened during the generation of the vacuum and closed after the vacuum is generated, which chamber is filled with a medium containing an element or compound capable of diffusing, which diffuses if the chamber and, with that, the vacuum chamber is not closed off tightly, through the access opening.

Abstract: Container with at least one vacuum enclosure with an access opening, especially a beverage container with a self-cooling device having a vacuum enclosure, such as a beer barrel or the like, the access opening of which is closed off by a closing mechanism after the vacuum is produced, a chamber being provided, which is downstream from the access opening and in which a valve element is disposed, which is opened during the generation of the vacuum and closed after the vacuum is generated, which chamber is filled with a medium containing an element or compound capable of diffusing, which diffuses if the chamber and, with that, the vacuum chamber is not closed off tightly, through the access opening.

Abstract: A tearing mill for comminuting fruits is equipped with a rotatably supported plate having tearing elements And with a stuffing device with a feed screw for delivering the fruits to the plate. A gap of adjustable width exists between the stuffing device and the plate. By means of an independent control of the gap and the rotational speed of the feed screw and plate, a mash structure of the comminuted fruits is achieved which is well adapted to the type of fruits and to subsequent processing of the mash.

Abstract: A cutting tool with a linear oscillating drive has a handle body (1) and a knife (3, 4) which is guided therein and is displaceable in an oscillating manner in the longitudinal direction, the knife (3, 4), by means of a knife holder (4), being detachably connected via a clamping member to a transmission element, arranged in the handle body (1), of the linear oscillating drive. Since the knife holder (4) is cohesively connected, in particular sealed, together with a knife shaft (31), the cutting tool complies with the hygiene regulations for use in the foodstuffs sector.

Abstract: A cutting tool with a linear oscillating drive has a handle body (1) and a knife (3, 4) which is guided therein and is displaceable in an oscillating manner in the longitudinal direction, the knife (3, 4), by means of a knife holder (4), being detachably connected via a clamping member to a transmission element, arranged in the handle body (1), of the linear oscillating drive. Since the knife holder (4) is cohesively connected, in particular sealed, together with a knife shaft (31), the cutting tool complies with the hygiene regulations for use in the foodstuffs sector.

Abstract: A tearing mill for comminuting fruits is equipped with a rotatably supported plate having tearing elements And with a stuffing device with a feed screw for delivering the fruits to the plate. A gap of adjustable width exists between the stuffing device and the plate. By means of an independent control of the gap and the rotational speed of the feed screw and plate, a mash structure of the comminuted fruits is achieved which is well adapted to the type of fruits and to subsequent processing of the mash.

Abstract: A motor housing (11) is mounted in a handle body (10) so as to slide in the longitudinal direction. A tubular pipe (37) that can slide in a bore (38) of the body (10) projects from the back of the housing (11). Compressed air can be applied to the bore (38) through a switching valve (40). A piston (14) with a piston rod (17) can slide in the housing and a tool can be secured to its front end. A flutter valve (23) which alternatingly connects the bore (38) to the two cylinder chambers (27, 28) is built into the valve housing (22). The piston (14) alternatingly opens the connection to an outgoing air conduit (29-31). A pre-stressed spring (47) is set between the body (10) and the housing (11). In operation, the housing (11) oscillates out of step with the piston (14), so that their initial forces are largely compensated and no vibrations are transmitted to the handle body (10).