Aptiv Technologies Ag

Aptiv Technologies Limited

Delphi Technologies, Inc.

Kun Zhao

Mirko Meuter

Uri Iurgel

Yu Su

Maximilian Schaefer

Christian Nunn

Ido Freeman

Dennis Mueller

Edin Andelic

Andre Paus

Thomas Kurbiel

Markus Buehren

Frederik Lenard Hasecke

Jittu Kurian

Markus Bühren

Michael Arnold

Yvonne Schnickmann

Abdallah Alashqar

Mirko Mueter

Lech J Szumilas

Lukas Hahn

Dennis Müller

Lech Szumilas

Mirko Nicolas Meuter

Christian Markus Nunn

A method is provided for controlling the movement of a host vehicle. Lane data related to road items are determined from a data source, and object data acquired from objects are determined via a sensor system. The road items and the objects are located in an external environment of the host vehicle. A machine-learning algorithm is fed with the lane data and the object data to generate a set of controlling functions for controlling the movement of the host vehicle by performing the steps of: classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle. The movement of the host vehicle is controlled based on the classified maneuvers and the determined controlling functions.

Method and computer system for controlling the movement of a host vehicle

Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

Method, computer system and non-transitory computer readable medium for target selection in the vicinity of a vehicle

A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

Methods and systems for predicting properties of a plurality of objects in a vicinity of a vehicle

The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.

Detection system for predicting information on pedestrian

A computer implemented method for determining weights for an attention based trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.

Method and system for determining weights for an attention based method for trajectory prediction

A method for determining information on an expected trajectory of an object comprises: determining input data being related to the expected trajectory of the object; determining first intermediate data based on the input data using a machine-learning method; determining second intermediate data based on the input data using a model-based method; and determining the information on the expected trajectory of the object based on the first intermediate data and based on the second intermediate data.

Method and system for determining information on an expected trajectory of an object

Disclosed herein are methods and systems for determining an attribute of an object at a pre-determined time point. Data representing a respective property of the object and a plurality of further objects at a plurality of time points different from the pre-determined time point are determined, and the data is arranged in an image-like data structure. The image-like data structure has a plurality of columns and a plurality of rows. The data is arranged in the image-like data structure such that each of one of the rows or the columns corresponds to respective properties of the object or of one of the plurality of further objects and each of the other of the rows or the columns corresponds to respective properties at one of the plurality of time points. The attribute is then determined using a pre-determined rule based on the image-like data structure.

Method and system for determining an attribute of an object at a pre-determined time point

A device for searching for a lane on which a vehicle can drive, wherein the device is configured to receive an image captured by a camera, the image showing an area in front of the vehicle, detect lane markings in the image, determine for each of the lane markings if the respective lane marking is of a first type indicating a road condition of a first type or a second type indicating a road condition of a second type, create lane candidates from the lane markings, divide the lane candidates into classes of lane candidates depending on the type of the lane markings of the lane candidates, and search the classes of lane candidates for a lane on which the vehicle can drive.

Device and a method for searching for a lane on which a vehicle can drive

A method for lane marker recognition includes: providing a filter bank with a plurality of different pairs of filters adapted to detect the left edge and the right edge of a specific type of a lane marker, respectively; receiving an image of a road; dividing the image of a road into a plurality of image segments, wherein each image segment includes at least one row of pixels of the image of a road; and for each of the image segments: applying a plurality of the pairs of filters of the filter bank to the image segment to generate a plurality of filter outputs; and determining which of the filter outputs correspond to a lane marker by using geo-metric information and appearance based information, wherein the geometric in-formation describes allowable dimensions of a determined lane marker, and the appearance based information describes allowable pixel values of a determined lane marker.

Lane marker recognition

The present invention relates to a method and an image processing system for determining the color of a street marking, by: capturing an image of a street, detecting a street marking as a set of pixels provided by the image, wherein the pixels include at least two different pieces of color information, determining a color score for the street marking by comparing said at least two different pieces of color information, and determining the color of the street marking by comparing the color score to at least one threshold value.

Street marking color recognition

An image processing method includes: determining a candidate track in an image of a road, wherein the candidate track is modelled as a parameterized line or curve corresponding to a candidate lane marking in the image of a road; dividing the candidate track into a plurality of cells, each cell corresponding to a segment of the candidate track; determining at least one marklet for a plurality of said cells, wherein each marklet of a cell corresponds to a line or curve connecting left and right edges of the candidate lane marking; determining at least one local feature of each of said plurality of cells based on characteristics of said marklets; determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells; and determining if the candidate lane marking represents a lane marking based on the at least one global feature.

Image based lane marking classification

A method for lane detection for a camera-based driver assistance system includes the following steps: image regions in images that are recorded by a camera are identified as detected lane markings if the image regions meet a specified detection criterion. At least two detected lane markings are subjected to a tracking process as lane markings to be tracked. By means of a recursive state estimator, separate progressions are estimated for at least two of the lane markings to be tracked. Furthermore, for each of a plurality of the detected lane markings, a particular offset value is determined, which indicates a transverse offset of the detected lane marking in relation to a reference axis. By means of an additional estimation method, the determined offset values are each associated with one of the separate progressions of the lane markings to be tracked.

Growing community of inventors

Duisburg, Germany

Kun Zhao