Robert Boschgmbh

Claudius Glaeser

Lutz Buerkle

Thomas Michalke

Thomas Benjamin Gussner

Thomas Maurer

Florian Drews

Lars Rosenbaum

Florian Faion

Jasmin Ebert

Fabian Timm

Michael Ulrich

Sebastian Muenzner

Thomas Gumpp

Alexandru Paul Condurache

Rainer Stal

Learning extraction of movement information from sensor data includes providing a time series of frames of sensor data recorded by physical observation of an object, providing a time series of object boundary boxes each encompassing the object in sensor data frames, supplying the object boundary box at a time t, as well as a history of sensor data from the sensor data time series, and/or a history of object boundary boxes from the time series of object boundary boxes, prior to time t to a trainable machine learning model which predicts an object boundary box for a time t+k, comparing the predicted object boundary box with a comparison box obtained from the time series of object boundary boxes for the time t+k, evaluating a deviation between the predicted object boundary box and the comparison box using a predetermined cost function, and optimizing parameters which characterize the behavior of the model.

Method for training and operating movement estimation of objects

A method for monitoring surroundings of a first sensor system. The method includes: providing a temporal sequence of data of the first sensor system for monitoring the surroundings; generating an input tensor including the temporal sequence of data of the first sensor system, for a trained neural network; the neural network being configured and trained to identify, on the basis of the input tensor, at least one subregion of the surroundings, in order to improve the monitoring of the surroundings with the aid of a second sensor system; generating a control signal for the second sensor system with the aid of an output signal of the trained neural network, in order to improve the monitoring of the surroundings in the at least one subregion.

Method for monitoring surroundings of a first sensor system

A method for the detection of an object in an environment of a vehicle as a function of sensor signals of a sensor for acquiring the environment of the vehicle. The method includes: processing the sensor signals using a region proposal network to obtain at least one object hypothesis per anchor, the object hypothesis including an object probability and a bounding box; selecting the best object hypothesis on the basis of a quality model, the quality model being a function of the anchor and the bounding box of the object hypothesis; identifying redundant object hypotheses relative to the selected object hypothesis, the redundant object hypotheses being identified as a function of the anchors of the redundant object hypotheses, using a target function assigned to the region proposal network; and fusing the selected object hypothesis with the identified redundant object hypotheses for the object detection.

Method, device, computer program, and machine-readable storage medium for the detection of an object

A method is described for segmenting an occupancy grid for a surroundings model of a driver assistance system for a vehicle. The method encompasses a step of reading in an occupancy grid having a plurality of grid cells, each grid cell of the plurality of grid cells having assigned to it a grid cell information item that encompasses an information item about a degree of occupancy and at least one additional information item about the grid cell; and a step of assigning at least one object and/or a plurality of objects to the plurality of grid cells using the grid cell information item, in order to segment the occupancy grid for a surroundings model.

Method and apparatus for segmenting an occupancy grid for a surroundings model of a driver assistance system for a vehicle background of the invention

A method for situation analysis for a driver assistance system on board a motor vehicle includes steps of providing a representation of the surroundings of the motor vehicle, the representation including a system of cells that are reversibly unambiguously associated with predetermined sections of the surroundings, scanning the surroundings of the motor vehicle with the aid of at least one sensor in order to detect an obstacle, and marking cells, based on the scan, in such a way that a cell that is associated with an obstacle-free section of the surroundings carries a first marker, and a cell that is associated with a section of the surroundings in which an obstacle has been detected carries a second marker. A morphological filter is subsequently used on the representation in order to provide cells, which are not accessible by the motor vehicle, with the second marker.

Situation analysis for a driver assistance system

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Ditzingen, Germany

Claudius Glaeser