Aptiv Technologies Limited

Aptiv Technologies Ag

Delphi Technologies, Inc.

Alexander Barth

Douglas L Welk

Christian Nunn

Dennis Mueller

David Schiebener

Amil George

Patrick Weyers

Klaus Friedrichs

Lutz Roese-Koerner

Detlef Wilke

Monika Heift

Roland Hollemann

Xuebing Zhang

Christian Prediger

Andrew J Lasley

Markus Bühren

Dennis Müeller

Marek Uszkur

Ali Saheli

Timo Rehfeld

Piotr Bando

Dennis Müller

A method includes obtaining a short exposure color-infrared (color-IR) image captured with an activated infrared (IR) illuminator and a long exposure color-IR image captured with a deactivated IR illuminator. The short exposure color-IR image is captured with a shorter exposure time than the long exposure color-IR image. The method includes determining an IR light impact of the of the short exposure color-IR image and of the long exposure color-IR image. The method includes mitigating the IR light impact in the short exposure color-IR image to obtain a short exposure IR-suppressed color image. The method includes mitigating the IR light impact in the long exposure color-IR image to obtain a long exposure IR-suppressed color image. The method includes merging the short exposure IR-suppressed color image with the long exposure IR-suppressed color image to obtain a merged HDR image.

Method for generating a high dynamic range image

Systems and techniques are provided for detecting drowsiness of a driver in a vehicle. In aspects, a driver is monitored by capturing an image sequence of an interior of the vehicle via a camera system. The image sequence is then analyzed to extract a characteristic pattern of the driver. An activity level of the driver is determined based on at least one of the image sequence or an output signal provided by another sensor of the vehicle. At least one of the extracted characteristic pattern and the activity level of the driver is selected as a basis for determining a drowsiness signal indicative of the drowsiness of the driver.

Method of determining drowsiness of a driver in a vehicle

Systems and techniques are described herein for detecting a state of presence of a given object in a vehicle. In aspects, techniques include processing data received over time from various devices in the vehicle to determine a set of accumulated scores. The data includes parameters indicative of a presence or absence of a detected object in the vehicle. Further, the processing may be effective to determine a set of accumulated scores. The techniques further include determining an object-related confidence value representing a likelihood that the detected object is present in the vehicle, based on the set of accumulated scores, and then comparing the object-related confidence value to a predetermined threshold. The comparison is sufficient to detect the presence of the detected object in the vehicle if the object-related confidence value exceeds the predetermined threshold.

Method for detecting state of presence of object in vehicle

A method is provided for authenticating an occupant within an interior of a vehicle. The vehicle includes a camera which is configured to monitor the interior of the vehicle, and a processing device being configured to process image or video data provided by the camera. Characteristic features are determined being visible via the camera, and authenticating image or video data are captured via the camera while an occupant is present within the interior of the vehicle. Via the processing device, face recognition and liveliness check are performed for the occupant based on the authenticating image or video data, and an identification degree of the characteristic features is determined within the authenticating image or video data. Based on the face recognition, the liveliness check and the identification degree it is determined whether an authentication of the occupant is to be accepted.

Method and system for authenticating an occupant within an interior of a vehicle

Systems and techniques are provided for monitoring an occupant located in an interior of a vehicle. The techniques include the detection an occupant in at least one image. Body points of the occupant are identified based on the at least one image, and a pose of the occupant is classified based on the detected body points. A recommendation for adapting the occupant's pose is provided based on the classified pose.

Method and system for monitoring an occupant of a vehicle

A computer implemented method for detecting whether a seat belt is used in a vehicle comprises the following steps carried out by computer hardware components: acquiring an image of an interior of the vehicle; determining a plurality of sub-images based on the image; detecting, for at least one of the sub-images, whether a portion of the seat belt is present in the respective sub-image; and determining whether the seat belt is used based on the detecting.

Methods and systems for detecting whether a seat belt is used in a vehicle

A method for providing information related to an external object in a field of view of a driver of a vehicle comprises that the driver is monitored by a camera system, and that an estimated eye position is identified based on data provided by the camera system. Spatial information is gathered for at least one external object outside the vehicle. A basic display location is determined for a visual output related to the external object based on the spatial information. At least one imminent or instantaneous disturbance event for the vehicle is detected. An eye position correction and/or a display location correction are determined in response to the imminent or instantaneous disturbance event. The visual output related to the external object is generated based on the estimated eye position and the basic display location and based on the eye position correction and/or the display location correction.

System and method for displaying spatial information in the field of view of a driver of a vehicle

The present disclosure describes a method for occupancy class prediction, such as for occupancy class detection in a vehicle. In aspects, the method includes determining, for a plurality of points of time, measurement data related to an area and determining, for a plurality of points of time, occlusion values based on the measurement data. The method further includes selecting, for a present point of time, one of a plurality of modes for occupancy class prediction based on the occlusion values for at least one of the present point of time and a previous point of time and/or based on one of the plurality of modes for occupancy class prediction selected for the previous point of time. The method additionally includes determining, for the present point of time, one of a plurality of predetermined occupancy classes of the area based on the selected mode for the present point of time.

Methods and system for occupancy class prediction and occlusion value determination

A computer-implemented method for determining an estimate of the capability of a vehicle driver to take over control of a vehicle, wherein the method comprises: determining at least one estimation parameter, the at least one estimation parameter representing an influencing factor for the capability of the vehicle driver to take over control of the vehicle; and determining an estimate on the basis of the at least one estimation parameter by means of a predefined estimation rule, the estimate representing the capability of the vehicle driver to take over control of the vehicle.

Method and device for determining an estimate of the capability of a vehicle driver to take over control of a vehicle

A computer implemented method for determining an activity of an occupant of a vehicle comprises the following steps carried out by computer hardware components: capturing sensor data of the occupant using at least one sensor; determining respective two-dimensional or three-dimensional coordinates for a plurality of pre-determined portions of the body of the occupant based on the sensor data; determining at least one portion of the sensor data showing a pre-determined body part of the occupant based on the sensor data and the two-dimensional or three-dimensional coordinates; and determining the activity of the occupant based on the two-dimensional or three-dimensional coordinates and the at least one portion of the sensor data.

Method and system for determining an activity of an occupant of a vehicle

A method for localizing a sensor in a vehicle includes using a first sensor mounted on a vehicle to capture at least one image of a moveable element of the vehicle, the moveable element having a predetermined spatial relationship to a second sensor mounted on the vehicle, the moveable element being moveable relative to the first sensor; determining spatial information on the moveable element on the basis of the at least one image; and localizing the second sensor on the basis of the spatial information by a transformation rule representing the predetermined spatial relationship between the moveable element and the second sensor.

Method and device for localizing a sensor in a vehicle

A computer-implemented method for determining an information on whether at least one hand of a vehicle driver cooperates with a manual steering element of a vehicle, wherein the method comprises: taking at least one image by means of at least one sensor mounted on the vehicle, wherein the at least one image captures at least a manual steering element of the vehicle; and determining, on the basis of the at least one image, an information on whether at least one hand of a vehicle driver cooperates with the manual steering element.

Method and device for determining whether a hand cooperates with a manual steering element of a vehicle

A system for generating a confidence value for at least one state in the interior of a vehicle, comprising an imaging unit configured to capture at least one image of the interior of the vehicle, and a processing unit comprising a convolutional neural network, wherein the processing unit is configured to receive the at least one image from the imaging unit and to input the at least one image into the convolution-al neural network, wherein the convolutional neural network is configured to generate a respective likelihood value for each of a plurality of states in the interior of the vehicle with the likelihood value for a respective state indicating the likelihood that the respective state is present in the interior of the vehicle, and wherein the processing unit is further configured to generate a confidence value for at least one of the plurality of states in the interior of the vehicle from the likelihood values generated by the convolutional neural network.

System and method for generating a confidence value for at least one state in the interior of a vehicle

A system for predicting a touch position of a pointer on a touch-enabled unit includes a 3D imaging unit, a processing unit coupled to the 3D imaging unit, and a touch-enabled unit coupled to the processing unit. The 3D imaging unit is configured to monitor an interaction zone in front of the touch-enabled unit. The processing unit includes a prediction module that is configured to predict where a pointer that approaches the touch-enabled unit and is monitored by the 3D imaging unit will touch the touch-enabled unit, and a calibration module that is configured to generate at least one calibration parameter by comparing the predicted touch position to the actual touch position of the pointer detected by the touch-enabled unit and to transfer the at least one calibration parameter to the prediction module in order to calibrate the prediction module.

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Wermelskirchen, Germany

Alexander Barth