A method for predicting the development of a traffic scene involving several participants, with each participant being characterized by a participant tuple, the participant tuple comprising a state tuple of several state properties, a route intention characterization, a maneuver intention characterization and an action intention characterization, wherein the state tuple of several state properties for each participant represents a global position and a heading and an absolute velocity of the participant, and with the traffic scene being characterized by a scene tuple comprising one participant tuple for each participant, the method comprising: - providing a map with corresponding map related information, - obtaining a noisy measurement, wherein the noisy measurement comprises for each participant a position measurement and a heading measurement and an absolute velocity measurement of the participant, - generating a conditional probability distribution of the scene tuple depending on the noisy measurement and the map, the generation comprising: - generating a conditional probability distribution of the state tuple at a given current point in time for each participant depending on the map and the noisy measurement, - generating a conditional probability distribution of the route intention characterization at the current point in time for each participant depending on the map and the respective state tuple, - generating a conditional probability distribution of the maneuver intention characterization at the current point in time for each participant depending on the map, the respective route intention characterization of the participant and under consideration of the state tuples of the other participants, - generating a conditional probability distribution of the action intention characterization at the current point in time for each participant depending on the map, the state tuple of the respective participant, the maneuver intention characterization of the respective participant and under consideration of the state tuples of the other participants, wherein for a given next point in time the probability distribution of the state tuple at the given next point in time for each participant is predicted depending on the state tuple at the given current point in time and the action intention characterization at the current point in time for the respective participant. - providing a map with corresponding map related information, - obtaining a noisy measurement, wherein the noisy measurement comprises for each participant a position measurement and a heading measurement and an absolute velocity measurement of the participant, - generating a conditional probability distribution of the scene tuple depending on the noisy measurement and the map, the generation comprising: - generating a conditional probability distribution of the state tuple at a given current point in time for each participant depending on the map and the noisy measurement, - generating a conditional probability distribution of the route intention characterization at the current point in time for each participant depending on the map and the respective state tuple, - generating a conditional probability distribution of the maneuver intention characterization at the current point in time for each participant depending on the map, the respective route intention characterization of the participant and under consideration of the state tuples of the other participants, - generating a conditional probability distribution of the action intention characterization at the current point in time for each participant depending on the map, the state tuple of the respective participant, the maneuver intention characterization of the respective participant and under consideration of the state tuples of the other participants, wherein for a given next point in time the probability distribution of the state tuple at the given next point in time for each participant is predicted depending on the state tuple at the given current point in time and the action intention characterization at the current point in time for the respective participant. - generating a conditional probability distribution of the state tuple at a given current point in time for each participant depending on the map and the noisy measurement, - generating a conditional probability distribution of the route intention characterization at the current point in time for each participant depending on the map and the respective state tuple, - generating a conditional probability distribution of the maneuver intention characterization at the current point in time for each participant depending on the map, the respective route intention characterization of the participant and under consideration of the state tuples of the other participants, - generating a conditional probability distribution of the action intention characterization at the current point in time for each participant depending on the map, the state tuple of the respective participant, the maneuver intention characterization of the respective participant and under consideration of the state tuples of the other participants, wherein for a given next point in time the probability distribution of the state tuple at the given next point in time for each participant is predicted depending on the state tuple at the given current point in time and the action intention characterization at the current point in time for the respective participant.