Abstract: A method for classifying objects includes receiving sensor data from an environment sensor of the vehicle; recognizing, based on the sensor data, an object in a region of a roadway; determining an object region on the roadway with which the object is associated; assigning a base point to the object based on the sensor data; determining the height of the base point with respect to a vehicle vertical direction; determining the roadway height with respect to the vehicle vertical direction in the object region under the assumption of a predetermined grade of the roadway between a forward-zone region of the roadway in front of the vehicle and the object region; and classifying the object as an object that can be driven under, if the difference between the roadway height and the height of the base point exceeds a predefined threshold value.

Abstract: A method for operating a surroundings sensing device for a motor vehicle includes sensing the surroundings by at least one first surroundings sensor of the surroundings sensing device and by at least one second surroundings sensor of the surroundings sensing device; transmitting the surroundings sensed by the first surroundings sensor and the surroundings sensed by the second surroundings sensor to an electronic computing unit of the surroundings sensing device; performing grid-based evaluation of the surroundings for a first distance range of the surroundings by means of the electronic computing unit; performing fusion of the surroundings for a second distance range, which is different from the first distance range, by the electronic computing unit; and evaluating the surroundings depending on the grid-based evaluation and the fusion.

Abstract: The invention relates to a method for detecting an object in a spatial region. The method comprises the following steps for each cell of a plurality of cells of the spatial region: determining evidence masses for the hypotheses that a cell is occupied, is not occupied, or may not be occupied at a current point in time on the basis of sensor data relating to the spatial region; determining predicted evidence masses for the hypotheses at the current point in time on the basis of actual evidence masses for a hypothesis at a previous point in time and on the basis of a plurality of particles for a corresponding plurality of movement hypotheses for the cell; and determining the actual evidence masses at the actual point in time by combining the measured evidence masses with the predicted evidence masses at the current point in time.

Abstract: A method (700) for determining the value of a size of an object (150) is described. The method (710) comprises determining (711) an occupancy grid (200) indicating evidence that individual cells (201) are vacant or occupied by the object (150). Further, the method (710) comprises determining (712) a subset of cells (201) associated with the object (150). Further, the method (710) comprises detecting (713) two opposing bounding edges of the object (150), and determining (714) a measurement value of the size of the object (150) based on the distance between the detected edges of the object (150). The method (710) further comprises determining (715) a quality measure indicating how well the two opposing edges of the object (150) could be detected, and determining (716) a measurement probability distribution of the size of the object (150) dependent on the quality measure.

Abstract: A controller for a vehicle is configured to determine image data from a camera of the vehicle which indicates an environment of the vehicle starting from a reference point. The controller further detects at least one camera object in the environment of the vehicle based on the image data. The controller is also configured to determine sensor data from a distance-sensing environment sensor of the vehicle. The camera object can be shifted to a plurality of different distances from the reference point. A corresponding plurality of values of a degree of overlap between the camera object and the sensor data can be determined for the plurality of different distances. The controller is also configured to determine an object distance between the camera object and the reference point based on the plurality of values of the degree of overlap.

Abstract: A method for ascertaining a description of a lane which is in an environment of a vehicle includes ascertaining an assignment function that is set up to assign different values of a feature vector different descriptions of a lane. The method also includes ascertaining environment data of the vehicle, where the environment data includes information about a roadway marking and/or about one or more objects in the environment of the vehicle. In addition, the method includes ascertaining a current value of the feature vector on the basis of the environment data, and determining a description of the lane using the assignment function and using the current value of the feature vector.

Abstract: The invention relates to a method for detecting an object in a spatial region. The method comprises the following steps for each cell of a plurality of cells of the spatial region: determining evidence masses for the hypotheses that a cell is occupied, is not occupied, or may not be occupied at a current point in time on the basis of sensor data relating to the spatial region; determining predicted evidence masses for the hypotheses at the current point in time on the basis of actual evidence masses for a hypothesis at a previous point in time and on the basis of a plurality of particles for a corresponding plurality of movement hypotheses for the cell; and determining the actual evidence masses at the actual point in time by combining the measured evidence masses with the predicted evidence masses at the current point in time.

Abstract: A method for detecting and/or tracking an object in surroundings of a vehicle is described. The method ascertains, at a first time, on the basis of sensor data from one or more ambient sensors of the vehicle, measurement data for a multiplicity of cells of a grid of the surroundings of the vehicle, the measurement data for a first cell indicating an object probability. Moreover, the method ascertains an occupancy probability of the first cell being occupied by an object that was already detected at a preceding time. The method further assigns the first cell to the object in dependence on the object probability and in dependence on the occupancy probability.

Abstract: For detecting static and dynamic objects, the objects are statistically detected by way of a particle card, and new particles are respectively added in repeating steps. The method is characterized by the fact that static particles are also added, which makes it possible to model static objects in a very precise manner. An environmental model is also generated that has a two-dimensional arrangement of cells, and each cell represents a specific location. To each cell at least two continuous classification values are assigned that describe different attributes of the cells, for example, whether a cell has an object, a static object, a dynamic object, or whether it represents a free space, or whether none of these classes can be assigned.

Abstract: A method for detecting and/or tracking an object in surroundings of a vehicle is described. The method ascertains, at a first time, on the basis of sensor data from one or more ambient sensors of the vehicle, measurement data for a multiplicity of cells of a grid of the surroundings of the vehicle, the measurement data for a first cell indicating an object probability. Moreover, the method ascertains an occupancy probability of the first cell being occupied by an object that was already detected at a preceding time. The method further assigns the first cell to the object in dependence on the object probability and in dependence on the occupancy probability.

Abstract: A method for ascertaining a description of a lane which is in an environment of a vehicle includes ascertaining an assignment function that is set up to assign different values of a feature vector different descriptions of a lane. The method also includes ascertaining environment data of the vehicle, where the environment data includes information about a roadway marking and/or about one or more objects in the environment of the vehicle. In addition, the method includes ascertaining a current value of the feature vector on the basis of the environment data, and determining a description of the lane using the assignment function and using the current value of the feature vector.

Abstract: A method and a device for establishing a trajectory for a vehicle is provided. The method includes the following steps: registering coordinates of objects using a sensor; calculating a cost map on the basis of the registered coordinates, wherein the cost map is subdivided into cells and a cost value is assigned to each cell, which cost value describes the presence and/or the vicinity of an object, calculating a cost space, which has a plurality of layers, wherein each layer is subdivided into cells and a cost value is assigned to each cell, wherein the cost values in the respective layers are calculated in each case for a specific orientation of the vehicle, and determining a trajectory that is as cost-effective as possible.

Abstract: For detecting static and dynamic objects, the objects are statistically detected by way of a particle card, and new particles are respectively added in repeating steps. The method is characterized by the fact that static particles are also added, which makes it possible to model static objects in a very precise manner. An environmental model is also generated that has a two-dimensional arrangement of cells, and each cell represents a specific location. To each cell at least two continuous classification values are assigned that describe different attributes of the cells, for example, whether a cell has an object, a static object, a dynamic object, or whether it represents a free space, or whether none of these classes can be assigned.

Abstract: A method and a device for establishing a trajectory for a vehicle is provided. The method includes the following steps: registering coordinates of objects using a sensor; calculating a cost map on the basis of the registered coordinates, wherein the cost map is subdivided into cells and a cost value is assigned to each cell, which cost value describes the presence and/or the vicinity of an object, calculating a cost space, which has a plurality of layers, wherein each layer is subdivided into cells and a cost value is assigned to each cell, wherein the cost values in the respective layers are calculated in each case for a specific orientation of the vehicle, and determining a trajectory that is as cost-effective as possible.