Conti Temic microelectronic GmbH

A method for creating a road model for planning a trajectory of an ego vehicle, includes capturing a vehicle environment with at least one environment capturing sensor. Objects are detected in the vehicle environment. The method includes producing a grid map with a plurality of grid cells and entering the detections into the grid map wherein the detections are enlarged prior to being entered. The method also includes determining at least one path through the grid map which consists only of vacant grid cells. The at least one path is gradually scanned, wherein detections are determined orthogonally to a scanning direction of the path on both sides. The method further includes assigning the detections along the at least one path to road boundaries and creating the road model based on the previously determined information.

A method of recognizing gestures of a person from at least one image from a monocular camera, e.g. a vehicle camera, includes comp the steps: a) detecting key points of the person in the at least one image, b) connecting the key points to form a skeleton-like representation of body parts of the person, wherein the skeleton-like representation represents a relative position and a relative orientation of the respective body parts of the person, c) recognizing a gesture of the person from the skeleton-like representation of the person, and d) outputting a signal indicating the gesture.

The invention relates to a method and a device for fusion of measurements from various information sources (I 1 , I 2 , . . . , I m) in conjunction with filtering of a filter vector, wherein the information sources (I 1 , I 2 , . . . , I m) comprise one or more environment detection sensor(s) of an ego vehicle, wherein in each case at least one measured quantity derived from the measurements is contained in the filter vector, wherein the measurements from at least one individual information source (I 1 ; I 2 ; . . . , I m) are mapped nonlinearly to the respective measured quantity, wherein at least one of these mapping operations depends on at least one indeterminate parameter, wherein the value to be determined of the at least one indeterminate parameter is estimated from the measurements of the different information sources (I 1 , I 2 , . . . , I m) and wherein the filter vector is not needed for estimating the at least one indeterminate parameter.

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 ( 101 ) a learning data record from temporally successive raw sensor data, evaluating ( 102 ) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining ( 103 ) expected sensor data, comparing ( 104 ) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining ( 105 ) a disparity between the currently captured sensor data and the ascertained expected sensor data.

A method for determining the roadway plane in the surrounding area of a vehicle, the vehicle comprising a stereo camera system for capturing stereo images of the surrounding area of the vehicle and an artificial neural network for processing the image information provided by the stereo camera system, wherein the neural network determines disparity information of the surrounding area of the vehicle, wherein, on the basis of the disparity information, distance information is calculated which contains information regarding the distance of the objects displayed on the image information from the stereo camera system or the vehicle, wherein roadway distance information is extracted from the distance information, wherein the roadway plane is determined on the basis of the roadway distance information.

A method for creating a road model for planning a trajectory of an ego vehicle, includes capturing a vehicle environment with at least one environment capturing sensor. Objects are detected in the vehicle environment. The method includes producing a grid map with a plurality of grid cells and entering the detections into the grid map wherein the detections are enlarged prior to being entered. The method also includes determining at least one path through the grid map which consists only of vacant grid cells. The at least one path is gradually scanned, wherein detections are determined orthogonally to a scanning direction of the path on both sides. The method further includes assigning the detections along the at least one path to road boundaries and creating the road model based on the previously determined information.

A method includes the steps: a) capturing image of an environment surrounding a vehicle by a camera; b) determining an area of increased brightness in the image based on pixels having a brightness exceeding a predefined threshold; c) estimating position coordinates of a light source in the environment from the area of increased brightness; d) detecting a shadow of the vehicle in the image; and e) determining a type of the light source as a spot light source or a direction light source from the shadow, environment.

A surround view system for a vehicle includes a detection unit and an evaluation unit. The detection unit is designed to detect data relating to the surroundings. The evaluation unit is designed to identify an object in the detected surroundings data and to determine the 3D shape of this object. The evaluation unit is additionally designed to add the determined 3D shape to a projection surface of the surround view system to produce a modified projection surface. The evaluation unit is designed to project the surroundings data onto the modified projection surface.

A method and apparatus for generating an image of vehicle surroundings are disclosed, including: capturing vehicle surroundings by vehicle cameras arranged on a vehicle body of a vehicle, and generating camera images by the cameras. The camera images of adjacent cameras have overlapping image regions), generating a virtual representation of the surroundings in a virtual three-dimensional space, during which the camera images are projected onto a virtual projection surface in the space. A non-stationary virtual camera in the virtual space determines a position and/or orientation thereof. A first selection region is placed on the surface in a first overlapping image region depending on a virtual camera field of vision, at least one image parameter of a first vehicle camera in the first selection region is calculated, and at least one second vehicle image parameter is adjusted to the at least one first vehicle image parameter in the first selection region.

A method for creating a road model for a driver assistance system of an ego vehicle includes recording the surroundings of the ego vehicle with at least one environment detection sensor. The method also includes detecting static and/or dynamic objects and creating a grid map having a plurality of grid cells. Static objects are entered into the grid map as occupied grid cells. The method also includes tracking the static objects and/or the dynamic objects. Information regarding a road profile is deduced based on the detections entered in the grid map and the tracked objects. A road model with the deduced information is created. The road model is provided to at least one driver assistance system.