Conti Temic Microelectronic Gmbh

Adc Automotive Distance Control Systems Gmbh

Vitesco Technologies Germany Gmbh

Continental Teves, Ag and Company Ohg

Stefan Heinrich

Thomas Fechner

Stefan Hegemann

Elias Strigel

Martin Pfitzer

Dieter Krökel

Carlos Almeida

Ralph Grewe

Simon Hachfeld

Martin Randler

Stefan Lüke

Dieter Kroekel

Matthias Zobel

Adam Swoboda

Aless Lasaruk

Marc Fischer

Wilfried Mehr

Wladimir Klein

Robert Thiel

Matthias Strauss

Dirk Ulbricht

Jan Giebel

Felix Hachfeld

Joachim Massen

Carsten Birke

Robert Baier

Reinhard Schuler

Jürgen Pfeiffer

Manuel Du Bois

Reik Mueller

Reik Müller

Timo Seifert

Enrico Rück

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

Method for detecting beacons

A camera system and method for controlling same, including capturing images of surroundings of a vehicle for a driver assistance system thereof. The camera system includes a first rolling shutter camera having a first aperture angle, a second rolling shutter camera having a second aperture angle, and control electronics. The first and second cameras are suitable for generating wide-angle and tele camera images, respectively. The first aperture angle is greater than the second aperture angle. The two cameras are designed such that both camera images have an overlap region. The control electronics synchronizes the two cameras. The geometric arrangement of the two cameras with respect to one another, and the position of the overlap region in the wide-angle image and in the tele camera image, are determined by continuous estimation. The stored geometric arrangement and position are taken into consideration during synchronization of the first and second cameras.

Synchronized camera system having two different cameras

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

A stereo camera system for a driver assistance system includes a first camera having a first aperture angle and a second camera having a second aperture angle, wherein the first camera produces a wide-angle camera image, the second camera produces a telephoto camera image, and the two camera images have an area of overlap. The first camera is a rolling shutter camera and the second camera is a global shutter camera. The two cameras' shutters are synchronized with one another in particular manners.

Camera system having different shutter modes

A camera device for a vehicle for 3-D environment sensing includes at least two camera modules having at least partly overlapping sensing ranges, a camera control unit, an evaluation unit and a point light projector. The point light projector is arranged and configured in such a way that the point light projector projects a light pattern of measurement points into the vehicle environment. The at least two camera modules are arranged and configured in such a way that at least part of the projected light pattern is imaged in the overlapping sensing range. The evaluation unit is configured to determine the 3-D position of measurement points in the vehicle environment from image data captured with the at least two camera modules. The point light projector is configured to produce a series of 'pseudo-noise patterns' as the light pattern, the 'pseudo-noise patterns' being projected into the vehicle environment in temporal succession.

3-D environment sensing by means of projector and camera modules

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

A method and a device for determining the position of a vehicle, said method comprising the steps: recording sensor data from the ground over which the vehicle travels; extracting ground characteristics of the ground over which the vehicle travels from the recorded sensor data or from occupancy grids which are calculated on the basis of the sensor data; determining the current position of the vehicle on the basis of the extracted ground characteristics using a characteristic map.

Method and system for determining the position of a vehicle

A control device for a vehicle, with at least one environment sensor which is designed to detect the surroundings of the vehicle, with a computing device which is designed to detect the position of the vehicle and to operate the environment sensor on the basis of corresponding location-dependent operating parameters.

Control device, server system and vehicle

A method for an overtaking assistant for a vehicle is provided. The vehicle is equipped with environment sensing systems for detecting objects in the own and an adjacent lane on the sides and at the rear of the vehicle. The space in front of the vehicle is preferably detected by environmental sensors as well. Autonomous cutting into an adjacent lane is only initiated if a first vehicle is detected in the adjacent lane. The first vehicle is taken as a reference for a speed of vehicles in the passing lane. It is also assumed that a second vehicle that approaches the first vehicle fast in the same lane has detected the first vehicle and will adjust its speed to the speed of the first vehicle.

Method and device for an overtaking assistant

A method and a device are provided for detecting the condition of a pavement or roadway travel surface on which a vehicle is traveling. A 3D camera acquires 3D camera image data including at least one image of surroundings including the roadway travel surface extending in front of the vehicle in the direction of motion thereof. Transverse height profiles of the roadway travel surface that respectively extend along transverse lines extending transversely to the vehicle's direction of motion are determined from the 3D camera image data. The condition of the roadway travel surface is detected from the determined transverse height profiles.

Determining the characteristics of a road surface by means of a 3D camera

A method of detecting a roadway edge and/or a traffic lane on a roadway includes:

Method for detecting a traffic lane by means of a camera

A method and a device for estimating the coefficient of friction by a 3D camera. The 3D camera records at least one image of the vehicle's surroundings. The image data of the 3D camera is used to produce a height profile of the road surface in the entire space ahead of the vehicle. Based on the height profile, the local coefficient of friction of the road surface that is to be expected in the space ahead of the vehicle is estimated.

Estimation of coefficients of friction using a 3-D camera

A method for detecting misalignment of a vehicle headlight using a camera system is specified. For this purpose, the headlight is in a predefined position and the camera system is arranged on or in the vehicle and is oriented in such a manner that the light distribution pattern of the headlight in front of the motor vehicle is detected. With a predefined headlight position, an actual light distribution pattern of the headlight is recorded using the camera system and is compared with a desired light distribution pattern for the predefined headlight position. If the actual light distribution pattern differs from the desired light distribution pattern, misalignment of the headlight is detected.

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Achern, Germany

Stefan Heinrich