Conti Temic Microelectronic Gmbh

Continental Teves, Ag and Company Ohg

Vitesco Technologies Germany Gmbh

Adc Automotive Distance Control Systems Gmbh

Continental Autonomous Mobility Germany Gmbh

Stefan Hegemann

Stefan Lueke

Stefan Heinrich

Ralph Grewe

Matthias Komar

Marc Fischer

Dieter Kroekel

Rolf Adomat

Christoph Hassenpflug

Ulrich Stählin

Thomas Fechner

Christian Scharfenberger

Matthias Zobel

Michelle Karg

Matthias Strauss

Aless Lasaruk

Chen Zhang

Frank Gruson

Andree Hohm

Florian Damerow

Peter Van Der Vegte

Ann-Katrin Fattal

Simon Hachfeld

Michael Darms

Thomas Bopp

Tobias Stephan

Christopher Linsenmann

Kai Ide

Reik Mueller

Matthias Limmer

Akitsugu Yoda

Raksincharoensak Pongsathorn

Masao Nagai

Ikuko Shimizu

Andreea Mirela Fischer

Stefan Lüke

Stefan LÜKE

A method for lane and roadway detection uses a multitask DNN architecture including an encoder, a first decoder and a second decoder. The method includes the following steps:

Method for lane and roadway detection

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 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 device for object recognition of an input image includes: a patch selector configured to subdivide the input image into a plurality of zones and to define a plurality of patches for the zones; a voting maps generator configured to generate a set of voting maps for each zone and for each patch, and to binarize the generated set of voting maps; a voting maps combinator configured to combine the binarized set of voting maps; and a supposition generator configured to generate and refine a supposition out of or from the combined, binarized set of voting maps.

Device and method for object recognition of an input image for 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 driver assistance system () of a vehicle () includes a laser scanner () with a phase-controlled laser (), a sensor unit () and an evaluation unit (). The phase-controlled laser is configured to produce a controllable laser beam by beam forming and to direct the laser beam into an environment outside of the vehicle. The sensor unit () is configured to detect a retroreflection caused by the laser beam. The evaluation unit () produces driver assistance data by evaluating the detected retroreflection.

Vehicle environment scanning by a phase-controlled laser

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

A driver assistance system for controlling a high beam, including an environment detecting device. At least one radar or lidar sensor system as well as a camera sensor system are provided for detecting the environment in front of the vehicle. A control signal for a high beam controller or warning device is generated when the high beam is active and at least one sensor system for detecting the environment simultaneously detects a potentially dazzled object.

Driver assistance system

A sensor system detects objects in an environment ahead of a vehicle. The environment is represented by a predetermined fixed number of position points in an environment model. Initially and when no objects are detected, the position points may be distributed stochastically over the detection area of the sensor system. When objects are detected, the position points are re-distributed based on the detected objects, e.g. with a higher density of position points to represent the detected objects. Because the total number of position points is a predefined fixed number that remains constant, the processing, storage and transmission of the environment model involves a constant data volume and efficient use of memory and transmission bandwidth.

Method for representing a vehicle environment with position points

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 for analyzing trafficability use a computer to perform the following steps:

Method and device for analyzing trafficability

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 for learning traffic events, the traffic events being transmitted to a data network using vehicle-to-X communication. The traffic events include position data and time data assigned to the traffic events, and the traffic events are stored electronically in the data network. The method is characterized in that an individual storage duration is determined for each traffic event, and the traffic event is deleted from the data network after the storage duration expires. The invention further relates to a corresponding system and to the use thereof.

Method and system for learning traffic events, and use of the system

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 and a device determine whether a vehicle can pass through a passage of an object, based on image data from a 3-D camera, which records an image of surroundings of the vehicle. A trajectory of the vehicle is ascertained. From the image data, it is determined whether an object located above the trajectory is recognized and whether the object has at least one connection to the ground. The dimensions and the shape of an entry area or a passage space between the object and the roadway, through which the vehicle is to pass, are determined from the image data. By comparing the dimensions and shape of the entry area or the passage space with the dimensions and shape of the vehicle, it is determined whether and how the vehicle can pass through the entry area or passage space.

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Wangen, Germany

Stefan Hegemann