Abstract: A method for predicting whether another vehicle in the driving-environment of an ego-vehicle will execute a lane-change, based on observations of the driving-environment of the ego-vehicle, including: the observations are supplied to individual classificators; based on at least a portion of the observations, each individual classificator, in accordance with an individual instruction, ascertains an individual probability that the other vehicle will change lanes; the driving situation in which the ego-vehicle finds itself is classified as a whole by a situation classificator into one of several discrete classes; a record of weighting factors, assigned to the class into which the situation-classificator has classified the driving-situation, is ascertained, that indicates the relative weighting of the individual classificators for this driving situation; the individual probabilities are set off against the weighting-factors to form an overall probability that the other vehicle will change lanes.

Abstract: A driving assistance method for a vehicle, in which in a training phase, the vehicle is steered by a driver through a setpoint course to a setpoint destination position, and setpoint course data are gathered in the process, in a replay phase, the vehicle is steered automatically through an actual course, actual course data are gathered in the process and, via a comparison of actual course data to setpoint course data, the steering of the vehicle is carried out so that the actual course is adapted/being adapted to the setpoint course and ends in an area of the setpoint destination position; and images of the vehicle surroundings are recorded with a camera as setpoint course data at setpoint positions, spaced equidistantly on a setpoint driving route of the setpoint course, and edge segments, feature points and corresponding descriptors are determined from an image processing for each image.

Abstract: A method for generating radar reflection points comprising the steps of: providing a plurality of predefined radar reflection points of at least one first object detected by a radar and at least one first scenario description describing a first environment related to the detected first object; converting the predefined radar reflection points into at least one first power distribution pattern image related to a distribution of a power returning from the detected first object; training a model based on the first power distribution pattern image and the first scenario description; providing at least one second scenario description describing a second environment related to a second object; generating at least one second power distribution pattern image related to a distribution of a power returning from the second object based on the trained model and the second scenario description; and sampling the second power distribution pattern image.

Abstract: A method for predicting whether another vehicle in the driving-environment of an ego-vehicle will execute a lane-change, based on observations of the driving-environment of the ego-vehicle, including: the observations are supplied to individual classificators; based on at least a portion of the observations, each individual classificator, in accordance with an individual instruction, ascertains an individual probability that the other vehicle will change lanes; the driving situation in which the ego-vehicle finds itself is classified as a whole by a situation classificator into one of several discrete classes; a record of weighting factors, assigned to the class into which the situation-classificator has classified the driving-situation, is ascertained, that indicates the relative weighting of the individual classificators for this driving situation; the individual probabilities are set off against the weighting-factors to form an overall probability that the other vehicle will change lanes.

Abstract: A driving assistance method for a vehicle, in which in a training phase, the vehicle is steered by a driver through a setpoint course to a setpoint destination position, and setpoint course data are gathered in the process, in a replay phase, the vehicle is steered automatically through an actual course, actual course data are gathered in the process and, via a comparison of actual course data to setpoint course data, the steering of the vehicle is carried out so that the actual course is adapted/being adapted to the setpoint course and ends in an area of the setpoint destination position; and images of the vehicle surroundings are recorded with a camera as setpoint course data at setpoint positions, spaced equidistantly on a setpoint driving route of the setpoint course, and edge segments, feature points and corresponding descriptors are determined from an image processing for each image.