Conti Temic Microelectronic Gmbh

Adc Automotive Distance Controls Systems Gmbh

Adc Automotive Distance Control Systems Gmbh

Wladimir Klein

Michael Walter

Urban Meis

Matthias Zobel

Christoph Wiedemann

Stefan Heinrich

Steen Kristensen

Axel Roth

Christoph Brüggemann

Mari Joan Hilario

Dominik Maeder

A method for determining the course of the road for a moving motor vehicle having a surroundings sensor system. Sensor data generated by the surroundings sensor system are evaluated to detect lane-relevant features. A lane model having at least one lane model parameter that determines the course of the lane is generated for the road, structures of at least one distance range that are parallel to the road are detected in the sensor data, the tangential direction of at least the one structure that is parallel to the road is determined, and the value of the tangential direction of the structure that is parallel to the road is adopted as the value of the direction of the tangent line at the point of contact with the structure that is parallel to the road to determine at least the one lane model parameter by predictive estimation in the lane model.

Method for determining the course of the road for a motor vehicle

A device, a sensor arrangement, a system and a method are provided for checking the plausibility of sensor data. The plausibility of the results of processing of sensor data is checked with an independent module by an alternative verification method at each stage, and quality indicators are calculated for the reliability of the calculated data from this plausibility check, which can be taken into account during further processing.

System, device and method for functional plausibility checking of sensor data

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 lane detection method wherein a digitized image of a lane is evaluated, including the following steps: detecting edges in the image, determining the angles of the detected edges with respect to a reference edge, removing the edges from the plurality of detected edges, the angle of which lies within a predetermined angle sector, and detecting the lane based on the remaining edges of the plurality of detected edges.

Method and device for lane detection

To support a driver in guiding a road vehicle, the lane markings of the road, traffic signs and/or vehicle license plates are detected by an environment sensor system, which produces corresponding image data, which is evaluated by an image evaluation system by comparison to stored country-specific data. From that evaluation, at least one country estimation signal is generated in an image processor, and then a country signal indicating the current location of the vehicle is generated from the at least one country estimation signal by a microprocessor. The country signal correspondingly activates a driver information device outputting country-specific information for guidance of the vehicle and/or the country signal is fed to a driver assistance system that supports the guidance of the vehicle.

Method for supporting the driver of a road-bound vehicle in guiding the vehicle

A method for lane detection, wherein a digitized color image of a roadway is processed using the following steps:

A method for detecting the course of a traffic lane, including the following steps: measuring structures of the traffic lane; evaluating the homogeneity of the measurements; and determining the course of the traffic lane on the basis of the evaluated homogeneity.

Method and device for detecting the course of a traffic lane

A detector for identifying a road lane boundary using a digitalized optical image of the region in front of the vehicle. The detector includes a correlator configured to select the edges which are to be used for road lane estimation by searching for extreme values of convolution response, and to weight each convolution response by a weighting factor. A histogram analysis unit configured to group the extracted edges into pairs, and to determine a frequency distribution of the distances between two paired edges as a histogram, and to use said frequency distribution to determine the distances between two grouped edges forming a frequency peak or a frequency plateau in the histogram as nominal edge widths. A weighting factor determination unit configured to determine, for an edge, the weighting factor of the convolution response in the correlator in such a manner that the weight determined by the weighting factor for an edge of the paired edges is higher the smaller the deviation of the distance between the grouped edges from the nominal edge width.

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Lindau, Germany

Wladimir Klein