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: Interacting entities are classified into cluster classes, where an interaction is a relation between two entities based on a promised outcome by each entity and an effective outcome of the interaction. A model for infinite relational trust is used which has hidden variables associated with entity classes corresponding to the entities. A conditional probability distribution of the hidden variables is calculated depending on observable attributes assigned to the entities and the relations.