Conti Temic Microelectronic Gmbh

Continental Automotive Gmbh

Elias Strigel

Stefan Heinrich

Martin Pfitzer

Thomas Fechner

Dieter Kroekel

Dieter Krökel

Robert Thiel

Felix Hachfeld

Jan Giebel

Manuel Du Bois

Murugan Gomathinayagam

Arun Kudana

Vishnu Vardhana Asvatha Narayanan

The invention relates to a method for detecting beacons () in the surroundings of an ego vehicle (), comprising the steps of:

Method for detecting beacons

Example embodiments relate to an ADAS sensor data processing unit, to an ADAS sensor system and to an ADAS sensor data evaluation method for use in driver assistance systems or systems for the automated driving of a vehicle. The ADAS sensor data processing unit includes an input interface, a decompression module, a processing unit and an output unit. The input interface is designed to receive data of an ADAS sensor that have been subjected to lossy compression by a compression module. The decompression module is designed to decompress the compressed data of the ADAS sensor. The processing unit is designed to process the decompressed data (IdSD) of the ADAS sensor, information relevant to an ADAS/AD function being ascertained from the decompressed sensor data. The output unit is designed to output the ascertained information relevant to the ADAS function.

Processing of lossy-compressed ADAS sensor data for driver assistance systems

A method and a driver assistance system recognize the intention of a pedestrian to move on the basis of a sequence of images of a camera. The method includes detecting a pedestrian in at least one camera image. The method also includes selecting a camera image that is current at the time t and selecting a predefined selection pattern of previous camera images of the image sequence. The method further includes extracting the image region in which the pedestrian was detected in the selected camera images of the image sequence. The method also includes classifying the movement profile of the detected pedestrian on the basis of the plurality of extracted image regions. The method outputs the class that describes the movement intention determined from the camera images of the image sequence.

Recognizing the movement intention of a pedestrian from camera images

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 () a learning data record from temporally successive raw sensor data, evaluating () the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining () expected sensor data, comparing () the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining () a disparity between the currently captured sensor data and the ascertained expected sensor data.

Method and system for predicting sensor signals from a vehicle

An apparatus for detecting a traffic light phase for a motor vehicle, includes: an image sensor device (), configured to capture an image of a traffic light and to provide the image as image data; a segmentation device () configured to define at least one partial region of the captured image and to assign it to at least one signal lamp of the traffic light; a scaling device (), configured to determine a maximum color saturation and/or a maximum brightness in the at least one partial region; and a computer device () configured to determine a signal status of the traffic light based on the maximum color saturation and/or the maximum brightness.

Apparatus and method for detecting a traffic light phase for a motor vehicle

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Wangen, Germany

Elias Strigel