Continental Teves, Ag and Company Ohg

Conti Temic Microelectronic Gmbh

Continental Automotive Gmbh

Continental Automotive Technologies Gmbh

Continental Autonomous Mobility Germany Gmbh

Matthias Komar

Ulrich Stählin

Thomas Grotendorst

Stefan Hegemann

Stefan Lueke

Dirk Waldbauer

Matthias Strauss

Stefan Lüke

Dieter Kroekel

Thomas Berthold

Ralph Grewe

Adam Swoboda

Bastian Zydek

Andree Hohm

Xiuxun Yin

Mattias Strauss

Michael Darms

Wolfgang Guenther

Annemarie Kunkel

Maxim Arbitmann

Stefan LÜKE

Wolfgang GÜNTHER

In order to recognize hazard areas and/or construction areas on a roadway, an environment sensor produces detected environment data regarding objects indicating the hazard areas and/or construction areas. A traffic light system is detected in the environment data, position data of the traffic light system is determined, an environment scenario is determined from the environment data and is compared with a reference model of the environment scenario. If the reference model does not indicate the presence of a traffic light system, or indicates a traffic light system having position data differing from the position data of the detected traffic light system, then it is concluded that the detected traffic light system is a traffic light system identifying a hazard and/or construction area.

Method and device for recognizing marked hazard areas and/or construction areas in the region of lanes

Using sensors of a first and second sensor type first and second sensor data are detected and items of information describing identical objects and/or object attributes are allocated to one another in the first and second sensor data. The first and second sensor data are encumbered by first and second ambiguities. Values of the second encumbering ambiguities are at least as great as values of the first encumbering ambiguities. A comparison of the first and second encumbering ambiguities of items of information describing identical objects and/or object attributes takes place. In the case of values of substantially the same size, the items of information describing identical objects and/or object attributes are allocated to one another in the first and second sensor data. In the case of values which are not of substantially the same size the items of information describing identical objects and/or object attributes are confirmed or discarded.

Method and system for information use

In a method for evaluating image data of a vehicle camera, information about raindrops on the vehicle's windshield within the field of view of the camera is taken into account in the evaluation of the image data for detection and classification of objects in the environment of the vehicle. Particularly, for example, depending on the number and the size of the raindrops on the windshield, different detection algorithms, image evaluation criteria, classification parameters, or classification algorithms are used for the detection and classification of objects.

Method for evaluating image data of a vehicle camera taking into account information about rain

A method supports a vehicle's traffic-light-sequence assistant system that detects traffic light sequences of a traffic light () with a camera (). According to the method, when the traffic light () is detected in the field of view (a ) of the camera (), a length or an end stopping point (L) of a driving path (W) toward the traffic light () is determined by a control unit (), such that the traffic light will remain within view of the camera when the vehicle drives the determined length along the driving path to the end stopping point, and a control signal (St) concerning the determined length (L) or the end stopping point of the driving path (W) is outputted.

Method for supporting a traffic-light-sequence assistant of a vehicle, said assistant detecting traffic lights

In a method for determining a course of a traffic lane for a vehicle (), structures that demarcate a trafficable space are detected in image data from an image acquisition unit (), and these structures are plotted on a surroundings map () that subdivides the surroundings of the vehicle into a two-dimensional grid () of cells (). The position of the vehicle in the grid of the surroundings map is determined and updated by odometric data. The distance and the direction of the vehicle () relative to those cells () of the grid () having structures that demarcate the pavement and/or the traffic lane are determined. Brightness transitions in the image data are detected and are plotted on the cells () of the grid (). The course of the traffic lane () is determined from the cells with the detected brightness transitions.

Method for determining a course of a traffic lane for a vehicle

A method and a device are provided for calibrating a plurality of environment sensors in a vehicle. Traffic light signals are detected and identified from the data of at least one environment sensor or from data received by a car-2-X communication unit. A calibration of the plurality of environment sensors is activated and performed in response to a determination that a traffic light signal pertaining to the vehicle has turned red.

Method for calibrating a plurality of environment sensors in a vehicle

A method for determining the speed of a vehicle equipped with at least one surround sensor that determines environment data of the vehicle relative to at least one motionless object includes: receiving, by the vehicle, through wireless vehicle-to-environment communication with the at least one motionless object, a transmitted piece of information about the motionlessness of the motionless object; determining the relative motion of the vehicle with respect to the motionless object from the environment data of the vehicle relative to the motionless object; and determining the speed of the vehicle and/or the relative position of the vehicle with respect to the motionless object from the determined relative motion.

Method and device for determining the speed and/or position of a vehicle

A method for accelerated object detection or for accelerated object attribute detection, wherein a first information item is acquired by a vehicle-to-X communication device, and describes at least one object or at least one object attribute in an evaluated data form. A second information item is acquired by at least one individual sensor or by a sensor group, and describes the at least one object or the at least one object attribute in sensor raw data form, and an object detection algorithm and/or an object attribute detection algorithm is applied to sensor raw data of the second information item. The method is defined in that a threshold value of the object detection algorithm or of the object attribute detection algorithm for detecting the at least one object or at least one object attribute described by the first information item is reduced in the sensor raw data of the second information item.

Method and system for accelerated object recognition and/or accelerated object attribute recognition and use of said method

A method for assisting a vehicle driver, wherein a sensor for covering the surroundings covers a pavement in front of the vehicle in order to determine a virtual vehicle corridor for a road vehicle in a complex traffic situation and the virtual vehicle corridor is determined depending on identified traffic lines and raised pavement boundaries.

Method for determining a virtual vehicle corridor

A method for determining relevant objects in a vehicle moving on a roadway An assistance function is executed in relation to a position of a relevant object, and the relevant objects are determined on the basis of an image evaluation of images of a surrounding area of the vehicle. The images are detected by way of camera sensors. By way of a radar sensor positions of stationary objects in the surrounding area of the vehicle are determined. A profile of a roadway edge is determined using the positions of the stationary objects and that the image evaluation is carried out in relation to the roadway edge profile determined. A driver assistance system suitable for carrying out the method is also described.

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Frankfurt am Main, Germany

Matthias Komar