Abstract: A method for predicting the behavior of at least one road user in a traffic situation. The method includes: obtaining a graph representation of the traffic situation, wherein nodes represent road users, edges represent interactions between the road users and define an adjacency between road users, each node is associated with a state, and each edge is associated with edge attributes; computing an evolution of the states of the nodes based at least in part on a self-evolution of the state of each considered node that is dependent on this state and mediated by a self-evolution operator; and an interaction of each considered node with other nodes that is dependent on the states of these other nodes and mediated by an interaction operator; and computing a sought property that characterizes the behavior of the at least one road user.

Abstract: A computer-implemented system and method provide knowledge-based entity prediction. The system and method include obtaining a knowledge graph, which includes nodes and edges. A set of the nodes represent labels associated with a scene. The edges represent relations between related pairs of nodes. The system and method include identifying a path with multiple edges having multiple relations from a source node to a target node via at least one intermediary node between the source node and the target node. The path is reified by generating a reified relation to represent the multiple relations of the path. The reified relation is represented as a new edge that directly connects the source node to the target node. A reified knowledge graph structure is constructed based on the knowledge graph and the reified relation. The reified knowledge graph structure includes at least the source node, the target node, and the new edge.

Abstract: A device and a computer implemented method for explainable clustering of a scene. The method includes determining a first relation that relates a first object class to a second object class, wherein determining the first relation includes determining, depending on the first object class and the second object class, a pair of entities in a first knowledge graph, in particular a commonsense knowledge graph, that represents information about a domain, wherein the pair of entities is related with the first relation in the first knowledge graph, determining a cluster in that the scene belongs depending on the scene and depending on other scenes, determining a second relation that relates the scene with the cluster depending on at least one feature of digital image data representing the scene, determining a rule that maps the first relation to the second relation.

Abstract: A method of road sign classification utilizing a knowledge graph, including detecting and selecting a representation of a sign across a plurality of frames, outputting a prompt initiating a request for a classification associated with the representation of the sign, classifying one or more images including the sign, querying the knowledge graph to obtain a plurality of road sign classes with at least one same attribute as the sign, and classifying the sign across the plurality of frames in response to a confidence level exceeding a threshold.

Abstract: A method for generating a user interface with a user interface device in a distributed automation system includes receiving a service message from a home automation device in the distributed automation system, identifying a state of a dialog manager of the user interface device in response to receiving the service message, and generating a natural language output message based at least in part on a device identifier parameter in the service message and a plurality of natural language templates stored in the memory in response to the dialog manager being in an idle state. The method further includes storing the service message in a priority queue in the memory based on a priority level parameter corresponding to the service message in response to the dialog manager being in an active state.

Abstract: A method of road sign classification utilizing a knowledge graph, including detecting and selecting a representation of a sign across a plurality of frames, outputting a prompt initiating a request for a classification associated with the representation of the sign, classifying one or more images including the sign, querying the knowledge graph to obtain a plurality of road sign classes with at least one same attribute as the sign, and classifying the sign across the plurality of frames in response to a confidence level exceeding a threshold.

Abstract: A method for generating a user interface with a user interface device in a distributed automation system includes receiving a service message from a home automation device in the distributed automation system, identifying a state of a dialog manager of the user interface device in response to receiving the service message, and generating a natural language output message based at least in part on a device identifier parameter in the service message and a plurality of natural language templates stored in the memory in response to the dialog manager being in an idle state. The method further includes storing the service message in a priority queue in the memory based on a priority level parameter corresponding to the service message in response to the dialog manager being in an active state.

Abstract: Machine semantic perception is discussed. One example system can comprise an environmental knowledgebase (KB) associating features with properties, and an interface component receiving sensor data associated with observed properties. The KB and sensor observations can be encoded in bit-matrix and bit-vector representations, respectively, for efficient storage and computation. A perception component can perform semantic perception on observed properties based on the KB. The perception component can determine explanatory features associated with the observed properties through abductive reasoning, and determine discriminatory properties associated with the explanatory features through deductive reasoning. These can be executed in an iterative and interleaved Perception Cycle for efficient computation of minimum actionable explanations of observations.