Abstract: A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Abstract: A charging device for electrically charging and discharging a traction battery in an electric vehicle, comprises a first DC-DC converter; a vehicle plug; a power plug or network connection; a rectifier; a second DC-DC converter; a connection node; wherein the first DC-DC converter can be coupled with a DC power source and is coupled with the connection node; the rectifier can be coupled with the power plug and is coupled with the second DC-DC converter; and the connection node is coupled with the vehicle plug. The object of providing such a charging device with improved efficiency is achieved in that the connection node is connected between the second DC-DC converter and the vehicle plug.

Abstract: A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Abstract: A charging device for electrically charging and discharging a traction battery in an electric vehicle, comprises a first DC-DC converter; a vehicle plug; a power plug or network connection; a rectifier; a second DC-DC converter; a connection node; wherein the first DC-DC converter can be coupled with a DC power source and is coupled with the connection node; the rectifier can be coupled with the power plug and is coupled with the second DC-DC converter; and the connection node is coupled with the vehicle plug. The object of providing such a charging device with improved efficiency is achieved in that the connection node is connected between the second DC-DC converter and the vehicle plug.

Abstract: A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Abstract: An evaluation device for a driver assistance system for a vehicle includes an input for receiving image information recorded by a camera, a first component for locating an image section present in a predefined shape in first image information received from the camera, and a second component for requesting second image information. The second image information corresponds to a renewed image of an image section found by the first component, with improved contrast in relation to the first image information. A third component is present for identifying a traffic sign in the second image information, and an output for emitting a signal relating to a traffic sign identified by the third component. There is also provided a computer program product and a method for operating a driver assistance system.

Abstract: A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Abstract: An evaluation device for a driver assistance system for a vehicle includes an input for receiving image information recorded by a camera, a first component for locating an image section present in a predefined shape in first image information received from the camera, and a second component for requesting second image information. The second image information corresponds to a renewed image of an image section found by the first component, with improved contrast in relation to the first image information. A third component is present for identifying a traffic sign in the second image information, and an output for emitting a signal relating to a traffic sign identified by the third component. There is also provided a computer program product and a method for operating a driver assistance system.