Abstract: First fused sensor data is generated by fusing sensor data of the first sensor and of the second sensor. The sensor data for the first fused sensor data is in a first processing state. Second fused sensor data is generated by fusing the sensor data of the first sensor and of the second sensor. The sensor data for the second fused sensor data is in a second processing state. An accuracy of the first and the second fused sensor data is determined. Based on the determined accuracy, the first processing state or the second processing state is selected for fusing sensor data of the first and second sensors.

Abstract: An autonomous/assisted driving system (ADS) obtains a top view representation of labels of a traffic environment in Cartesian coordinates from sample data. The system obtains a transformation matrix for transforming Cartesian coordinates in an observation coordinate system of a perspective of an environmental sensor of the ADS. The transformation matrix is applied to the top-view representation of the labels to obtain a perspective representation of the traffic environment in the observation coordinate system.

Abstract: A calibration matrix is determined for a camera for an automotive vehicle. In a first step, at least one object is detected, which moves in a sequence of camera images between image areas having different levels of distortion. In particular, feature tracking can be carried out for the at least one object. To determine the calibration matrix, first an expected perspective-related distortion of the at least one object is calculated. Moreover, an observed distortion of the at least one object is determined. A camera-related distortion of the at least one object is calculated from the observed distortion and the expected perspective-related distortion. A calibration matrix can then be calculated from the camera-related distortion. This matrix can be applied to the camera images. The method steps can then be iteratively repeated until a minimal camera-related distortion is achieved.