Abstract: The invention relates to a driving assistance system including a prediction subsystem in a vehicle. According to a method aspect of the invention, the method comprises the steps of accepting a set of basic environment representations; allocating a set of basic confidence estimates; associating weights to the basic confidence estimates; calculating a weighted composite confidence estimate for a composite environment representation; and providing the weighted composite confidence estimate as input for an evaluation of a prediction based on the composite environment representation.

Abstract: The invention relates to a driving assistance system including a prediction subsystem in a vehicle. According to a method aspect of the invention, the method comprises the steps of accepting an environment representation; calculating a confidence estimate related to the environment representation based on applying plausibility rules on the environment representation, and providing the confidence estimate as input for an evaluation of a prediction based on the environment representation.

Abstract: The invention relates to a driving assistant adapted for active control of a vehicle based on predictions of a behavior of a detected object. A method aspect of the invention comprises accepting a first prediction of a behavior associated with the detected object from a first prediction subsystem and a second prediction from a second prediction subsystem; determining a control signal based on a combination of the first prediction and the second prediction; and initiating active control of the vehicle based on the control signal.

Abstract: A method for computationally predicting future movement behaviors of at least one target object can have the steps of producing sensor data by at least one sensor physically sensing the environment of a host vehicle, and computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s). The context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time. The method can also include validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, estimating at least one future position of the traffic participant based on the at least one trajectory, and outputting a signal representing the estimate future position.

Abstract: A method for computationally predicting future movement behaviors of at least one target object can have the steps of producing sensor data by at least one sensor physically sensing the environment of a host vehicle, and computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s). The context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time. The method can also include validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, estimating at least one future position of the traffic participant based on the at least one trajectory, and outputting a signal representing the estimate future position.