Conti Temic Microelectronic Gmbh

Adc Automotive Distance Control Systems Gmbh

Robert Thiel

Thomas Fechner

Dieter Krökel

Christian Scharfenberger

Michelle Karg

Martin Pfitzer

Karsten Breuer

Stefan Heinrich

Dieter Kroekel

Elias Strigel

Helmut Riedel

Alexander Bachmann

Olga Prokofyeva

Holger Grunleithner

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 device is configured to classify data. Its operation involves providing () data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated () by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor. Each group of the first plurality of groups is populated () by the processing unit implementing the artificial neural network with a different at least one of the plurality of negative samples based on a different feature of sample data similarity for each group, which involves processing the negative samples to determine a number of different features of sample data similarity in order to populate different groups with negative samples that share or substantially share that or a similar feature, wherein at least one of the groups of the first plurality of groups contains at least two negative samples. It is determined () by the processing unit implementing the artificial neural network whether the at least one test sample contains data relating to the at least one object based on the plurality of the positive samples and the first plurality of groups. The artificial neural network implements the learning algorithm.

Device for classifying data

A method for the three-dimensional graphic or pictorial reconstruction of a vehicle begins with capturing an image of at least one vehicle with a camera. A first rectangular border of the entire vehicle is captured in the image, to obtain a first rectangle. A second rectangular border of one side of a vehicle is captured in the image, to obtain a second rectangle, and it is determined whether the first and second rectangles are borders which relate to the same vehicle. If so, then it is determined whether a side orientation of the vehicle can be assigned from the two rectangles and, if so, the side orientation is determined. Finally, a three-dimensional reconstruction of the vehicle is performed from the first rectangle, the second rectangle and the side orientation.

Method for three-dimensional graphic reconstruction of a vehicle

A camera device includes an optronics system and an image capture control unit, for acquiring a sequence of images of a surrounding region of a vehicle. The optronics system includes a wide-angle optical system and a high-resolution image acquisition sensor. The optronics system and the image capture control unit are configured to generate a reduced-resolution binned image of the entire capture region of the optronics system, or to capture an unbinned high-resolution image of a subregion of the capture region, respectively for each individual image of the sequence of images, depending on a current traffic and/or surrounding situation. A height and a width of the subregion are set depending on the current situation. A size of the subregion is set such that the pixel count of the high-resolution image of the subregion is no greater than the pixel count of the reduced-resolution image of the entire capture region.

Camera device and method for capturing a surrounding region of a vehicle in a situation-adapted manner

An object-recognition method for a vehicle's driver assistance system involves obtaining a 2D image and a 3D image, forming a 3D apparent object from the 3D image, detecting one or more detected objects in a portion of the 2D image corresponding to the apparent object from the 3D image, classifying the one or more detected objects into at least one pre-defined object class, and dividing the apparent object into at least two 3D objects when the apparent object does not correspond with at least one class-specific property of the determined at least one object class.

Method and device for detecting objects from depth-resolved image data

A method for regulating the velocity of a motor vehicle in a complex traffic situation is presented. The motor vehicle is equipped with a sensor system for recording the environment. In order to regulate the velocity, an at least partially covered object and/or at least one object on an adjacent lane is viewed.

Method for speed regulation of a motor vehicle in a complex traffic situation

The invention relates to a method for determining a speed of a vehicle (), whereby at least two images (A, B) of an area surrounding the vehicle are recorded in time succession using a camera on the vehicle (), and between the images (A, B) changes () in a position and/or a size of at least one object (X) contained in the images (X) are determined, and from the changes, a speed of the vehicle () is determined relative to the object (X).

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Niederstaufen, Germany

Robert Thiel