Method and assistance system for an information filter-based estimation of the driving path of a motor vehicle

Abstract

Method for repeatedly estimating a current driving tube (14) of a motor vehicle (5), wherein during the operation of the motor vehicle (5) regularly repeatedly - several measurement data (11) that characterize a respective traffic situation of the motor vehicle (5) are recorded with associated uncertainties, - so that in each update step an updated information matrix and an updated information vector of the information filter formalism are estimated, and - an updated estimate of the driving tube (14) is determined from the updated information matrix and the updated information vector, characterized in that the measurement data (11) are organized in a measurement data vector and the associated uncertainties in an uncertainty matrix and in the update step the individual measurement data (11) and their associated uncertainty are taken into account independently of each other without inverting the entire uncertainty matrix.

Description

[0001] The present invention relates to a method and an assistance system for estimating the travel distance of a motor vehicle during its operation. The invention further relates to a correspondingly equipped motor vehicle.

[0002] Today, there is a growing focus on the automation of vehicle control and the further improvement of driver assistance functions and systems. A more accurate estimate, or prediction, of the vehicle's path can serve as a basis for this. Such a path, which can also be called a driving corridor, refers to an area in the direction of travel ahead of the vehicle, through or along which the vehicle is at least expected to move. This path can be bounded, for example, by road edges or similar features and / or by defined boundaries.

[0003] A method for detecting road edges along a roadway to identify a free path is described, for example, in DE 10 2013 205 950 B4. In this method, line candidates are detected based on input images, and an initial vanishing point in an image is identified as a function of the line candidates. For each line candidate, an area of ​​interest is identified in the image, with each area of ​​interest comprising a line candidate and a surrounding area. Features relating to the line candidate and extracted from the area of ​​interest are fed into a classification device to identify whether the line candidate is a potential road edge. The classification device assigns a confidence level to the line candidate, which identifies a probability regarding whether the line candidate is a road edge.The potential line candidate is identified as a reliable roadside based on the fact that the confidence level is greater than a predetermined value.

[0004] As described, for example, in DE 10 2006 040 334 A1, the current speed and yaw rate of the respective vehicle can be used to estimate the lane path. However, corresponding known methods are not always sufficient for more complex driver assistance systems. Therefore, a method for lane detection is proposed there. In this method, lane markings in an area in front of the vehicle are detected using a sensor system on the vehicle. Support points with coordinates from a first coordinate system are assigned to the lane markings, which are then converted into a second coordinate system. The course of the lane markings and / or lanes is reconstructed from the position of the support points in the second coordinate system.

[0005] German patent DE 10 2016 003 935 A1 discloses a method for determining edge-development information that describes edge-development along a route traveled by a motor vehicle. The input data used is trajectory data describing the motor vehicle's past trajectory and / or a predicted trajectory. In the method, a determination area covering the described trajectory is subdivided into intervals perpendicular to the vehicle's longitudinal direction. For each interval, obstacle positions within the interval are sorted according to the trajectory's position as belonging to either a left or right edge-development area. The edge-development information is then determined from the obstacle positions closest to the trajectory in each interval.This should enable the determination of edge development information from sensor data provided via a generic data interface, independent of a model and the specific sensor types used.

[0006] Document DE 10 2008 021 380 B4 discloses a method for predicting the course of a roadway in front of a vehicle located on the roadway, in which the position of the vehicle is determined and information about the course of the roadway in front of the vehicle's position is provided from at least one map, wherein roadway courses are divided into classes and wherein a classification for a multitude of roadway courses is defined and stored as class information, and depending on the information from the map, the course of the roadway in front of the vehicle is assigned to at least one class and the class information underlying this class is taken into account for predicting the course of the roadway.

[0007] Document DE 10 2018 215 753 A1 discloses a device for determining the trajectory of a vehicle comprising: an input interface for receiving sensor data from a radar sensor, wherein the sensor data includes information about the vehicle's environment; a preprocessing unit for generating a sensor data set based on the sensor data, wherein the sensor data set comprises accumulated sensor data from at least one measurement cycle of the radar sensor; a superposition unit for generating an environmental image based on a superposition of several sensor data sets; an evaluation unit for iteratively determining the vehicle's position and orientation based on the environmental image; and a mapping unit for generating a raster map of the environment based on the environmental image and for extracting environmental features from the raster map.and an optimization unit for determining a trajectory of the vehicle based on the determined position and orientation as well as on the environmental characteristics.

[0008] A problem with current approaches to estimating tire clearance is that these approaches are often comparatively computationally intensive, while in motor vehicles, only limited hardware and computing resources are available, for example, in embedded systems such as electronic control units or similar devices. Therefore, there is a need not only for improved environmental perception and tire clearance estimation, but also for a reduction in the required computing power.

[0009] The object of the present invention is to enable a particularly efficient estimation of the driving tube of a motor vehicle during its operation.

[0010] This problem is solved according to the invention by the subject matter of the independent claims. Possible embodiments of the present invention are disclosed in the dependent claims, in the description, and in the figure.

[0011] The method according to the invention can be used to repeatedly estimate the current travel distance of a motor vehicle, which is always located ahead of the motor vehicle in the direction of travel. The method can be applied while the respective motor vehicle is in operation. Thus, the current travel distance can be continuously or regularly estimated or updated during driving using the method according to the invention. In one step of the method according to the invention, measurement data that indicate or characterize a particular traffic situation of the motor vehicle are acquired along with their associated uncertainties. The measurement data can, for example, be represented or organized in a vector. The uncertainties can, for example, be represented or organized in an uncertainty matrix, which may contain only the uncertainties of the measurement data.Uncertainties can, for example, indicate measurement errors, confidence intervals, or correspondingly limited accuracies of the measurement data. These uncertainties can be provided and / or calculated by a sensor along with the respective measurement data or values, for example, using a predefined heuristic or similar method.

[0012] The measurement data and their associated uncertainties can together form or span a measurement space, or be specified within a measurement space. This measurement space is therefore an abstract mathematical space. If multiple measurement data points, for example, of different types and / or for different positions, are used to estimate or update the tire pressure, the measurement space can be correspondingly high-dimensional. In practice, the uncertainty matrix can, for example, have on the order of 100 rows and columns or more.

[0013] The measurement data can describe or depict the vehicle's surroundings, particularly those in the direction of travel. Similarly, the measurement data can include, for example, operating or condition data of the vehicle, movement data of other road users (such as trajectories driven by other vehicles in the same area at earlier times), and / or similar information. In particular, the individual measurement data can be of several different types, originate from multiple sensors, and be taken from different measurement points (i.e., locations in the surroundings), and / or similar factors. This can enable a particularly robust and accurate estimation or update of the vehicle's path. However, with current approaches, this can also involve a correspondingly high data processing and computational effort.However, this problem is circumvented or avoided by the method according to the invention.

[0014] In a further process step of the inventive method, an updated information matrix and an updated information vector are estimated within the framework of the information filter formalism or mechanism, for example, as soon as new measurement data are available or have been acquired. In other words, an information filter is used here to take the respective new or updated measurement data, including their uncertainties, into account in the estimation or update of the driving tube. The information filter is a type of Kalman filter, which is also referred to in technical terms as an inverse covariance filter.

[0015] In a further process step, for example, in each iteration of the process or after each update step, an updated estimate of the driving tube is determined from the updated information matrix and the updated information vector. In other words, a state or state vector can be determined that describes the driving tube according to the current estimate or prediction.

[0016] The application of the information filter formalism proposed here enables an update of the road tire estimate without inverting the uncertainty matrix, as is typically necessary with previous approaches. Instead, individual measurements can be considered and processed independently of one another in the update step. For this purpose, correspondingly smaller matrices can be inverted for each individual measurement, for example, a single sensor, a single measuring point or position, or the like. The information matrix that may need to be inverted can be smaller, in particular much smaller, i.e., have a smaller dimension than the uncertainty matrix for the respective set of acquired or newly acquired measurement data. Thus, the present invention enables a road tire estimate or...The inventive method provides a driving tube update that is equivalent to previous approaches where updating the driving tube estimate by considering the new measurement data requires inverting the comparatively high-dimensional uncertainty matrix, which represents the uncertainties of all new measurement data or measurements. The inventive method can be performed with significantly less computational effort compared to such previous approaches. This can enable driving tube estimation in motor vehicles that is equivalent to or even improved upon previous methods, for example, by using more measurement data and / or more frequent or high-frequency updates, particularly when using limited hardware or computing resources, such as those found in commonly used control units or embedded systems.

[0017] The driving tube, or its estimation, can be described by relatively few parameters or values ​​compared to the quantity or number of measurement data points and their uncertainties. This means that calculations or procedural steps within a corresponding state space defined by the parameters used to describe the driving tube or its state can be performed with particularly low computational effort. Since a sufficiently accurate and reliable driving tube estimation can form the basis for a wide variety of driver assistance systems and / or at least partially automated vehicle operation, this approach allows for significant savings in effort and energy, and / or facilitates improved safety and / or enhanced comfort with minimal drawbacks.For example, even with the computing hardware commonly found in modern vehicles, such an update of the driving distance estimate can be performed in less than 1 ms on average. This allows for a correspondingly quick response to new measurement data or a changed traffic situation.

[0018] The traffic situation of the motor vehicle, as defined by the present invention, can be determined by an environmental situation or the environment of the motor vehicle. Likewise, the traffic situation can encompass the current state of the motor vehicle, such as its position, speed, steering angle, and / or the like. The environment can be defined or described, for example, by the local traffic infrastructure, the presence and, where applicable, the positions of environmental features, objects, obstacles, other road users, or the like, as well as their sensory detectability. Corresponding features or objects in the environment can be, for example, lane or road markings, curbs, the course of the road or carriageway, elements of road equipment, road or carriageway edges, guardrails, vegetation strips, and / or the like.

[0019] The problem addressed and the proposed solution can therefore be summarized as follows, which will be explained in more detail below: Matrix inversion for large matrices is inherently very computationally expensive. This is due to the measurement uncertainty matrix, which in previous methods must be inverted for each update and is much larger than the state uncertainty matrix. The estimation problem can be formulated based on an information matrix and an information vector. At least as part of the solution, the information filter can then be applied. This allows for an independent analysis of all individual measurements. This means that instead of inverting the comparatively large entire measurement uncertainty matrix for updating, only several comparatively much smaller measurement uncertainty matrices need to be inverted. The final inversion of the similarly small information matrix to transfer the updated estimate into the state space is significantly less complex than inverting the entire measurement uncertainty matrix.

[0020] In one possible embodiment of the present invention, the measurement data describe at least the road ahead of the motor vehicle in its direction of travel at different distances from the motor vehicle, i.e., from its current position. The measurement data can thus characterize, map, or scan the respective environment or road, for example, with a resolution in the range of centimeters or decimeters, or on the order of 1 m, up to a distance of several tens of meters. This allows the environment or the road ahead to be recognized with particular accuracy, which ultimately enables a particularly precise, robust, and reliable estimation of the driving distance.The use of such detailed measurement data as a basis for estimating the road tire is made possible here by the particularly efficient data processing, i.e., the particularly efficient road tire estimation without significant disadvantages compared to previous approaches, which, given computing capacity or computing hardware, may only be able to process less or less detailed measurement data in real time.

[0021] In a further possible embodiment of the present invention, the measurement data are organized in a measurement data vector and the associated uncertainties in an uncertainty matrix. In the update step, the individual measurement data points and their associated uncertainties are considered independently of one another, without inverting the entire uncertainty matrix. For example, individual submatrices corresponding to the individual measurement data points of the overall uncertainty matrix can be inverted individually, instead of inverting the entire uncertainty matrix. These submatrices can, for example, have dimensions of 3 x 3 or 4 x 4, while the dimension of the overall uncertainty matrix can be on the order of 100 x 100 or more. In particular, the dimension of the submatrices can be an integer divisor of the dimension of the overall uncertainty matrix.Here, the advantage of the information filter formalism can be exploited, namely that measurements or individual measurement data can be filtered in each iteration or time step of the process by summing the corresponding information matrices and information vectors. Individual measurement data, in this context, can be individual measurement points or data points from one or more data sources. A single sensor can provide a single measurement data point or multiple individual measurement data points in each new measurement. The embodiment of the present invention proposed here offers an easy-to-implement approach for estimating the tire tube without loss of accuracy and thereby avoiding the computational or data processing effort required to invert the entire uncertainty matrix.

[0022] In a further possible embodiment of the present invention, a corresponding updated state or state vector for the drive tube is first determined from the updated information matrix and the updated information vector. This state or state vector characterizes or describes the drive tube by predefined, in particular geometric, parameters. The number of these predefined parameters is smaller, in particular much smaller, than the dimension of the measurement data or the measurement space in which the measurement data and their uncertainties are specified. For example, if the measurement data is organized in vector or matrix form, there may be dozens or hundreds of rows or columns. The number of predefined parameters for characterizing, describing, or specifying the drive tube is therefore limited.The state or state vector for the driving tube, on the other hand, can be, for example, at most 20 or at most 10, or even 4. The number of parameters can therefore be, for example, at most one-fifth or at most one-tenth of the number of rows in the measurement data vector or the associated uncertainty matrix. In this way, the driving tube, i.e., its current estimate, can be specified and handled with particularly low data processing effort.

[0023] In a possible further development of the present invention, the specified parameters for characterizing the drive tube include its curvature, in particular at several specified distances from the vehicle or its current position, i.e., at several points spatially spaced apart from one another in the direction of travel of the vehicle or in the longitudinal direction or along the longitudinal extent of the drive tube. This allows the drive tube or its course to be described or specified in a particularly simple, efficient, and data-saving manner. Such a characterization of the drive tube can be sufficient for practical purposes, since certain properties of the drive tube or corresponding boundary conditions for its course must always be present or fulfilled, and can therefore be fixed accordingly.This can include, for example, that the driving tube always runs on the local road surface, has a predetermined width, a maximum curvature determined by the vehicle's technical specifications, must be continuous (i.e., uninterrupted), and must not have any jumps, steps, or lateral offsets, and / or similar conditions. Likewise, the parameters for characterizing the driving tube can include its radius, length, width, orientation, and / or similar characteristics. The respective values ​​of these parameters can be specified or entered in the state vector mentioned elsewhere. This can enable particularly simple and efficient data processing.

[0024] In a possible further development of the present invention, the most recently determined updated information matrix is ​​inverted to determine the updated state of the driving tube. This inverted information matrix is ​​then multiplied by the most recently determined updated information vector. This represents a particularly simple and cost-effective way to describe the driving tube, or its state or estimate, using real, intuitively understandable quantities, such as geometric ones, i.e., to specify it in a correspondingly intuitive and practical state space. This can enable particularly simple and efficient reuse of the current driving tube estimate by other assistance systems and functions. For example, the latter can then be implemented independently of the formalism used for the driving tube estimate.

[0025] In a possible further development of the present invention, a corresponding updated estimated path of the driving hose in the respective environment is determined from the updated state of the driving hose using a predefined model. The predefined model models or considers at least one predefined condition for a permissible real driving hose path. The model can, for example, model the properties or boundary conditions for the driving hose path mentioned elsewhere.From the combination of the state, which can be specified, for example, by fundamental or point-specific parameter values, and at least one predefined condition, the model can determine the complete path of the tire tube, for example, as a two- or three-dimensional elongated geometric object that can be located, for example, relative to the current position of the vehicle and / or in a predefined coordinate system, such as a fixed coordinate system. The model can thus indicate or model how the actual path of the tire tube results from the respective measurement data or the estimated state or state vector of the tire tube derived from it. The model can, for example, be stored or implemented in an estimation device designed to estimate the state of the tire tube.The model can construct the actual path of the hose based on the current state, for example, using circular segments, curved lines, or similar elements. A condition can be specified, for instance, that transitions between successive circular segments or curved lines should be seamless; that is, these circular segments or curved lines must connect or merge into each other without a jump, offset, or gap. This can enable particularly efficient modeling of the actual hose path.

[0026] In a further possible embodiment of the present invention, environmental data is recorded as part of the measurement data. This environmental data describes the surroundings of the vehicle, particularly those in the direction of travel. Steering angle data of the vehicle is also recorded as part of the measurement data. This steering angle data can, for example, indicate or include the current steering angle or a current change in the steering angle, such as its direction and / or rate of change. Furthermore, it is determined whether a lane change or a turning maneuver is likely to occur within a range extending from the vehicle's current position to a predetermined distance in its direction of travel.This can be done, for example, using navigation data, a driving history of the vehicle and / or the respective driver, a corresponding predefined movement model and / or the like.

[0027] If it is determined that such a maneuver is likely to occur, the updated estimate of the travel path is calculated up to the specified distance based on steering angle data, and beyond the specified distance based on environmental data without considering the steering angle data. Thus, up to the specified distance, the travel path can be estimated, for example, solely based on steering angle data, or based on both steering angle data and environmental data. This can provide a particularly accurate and reliable estimate of the travel path up to the specified distance, since the steering angle primarily determines and limits the vehicle's direction of travel in the relevant immediate vicinity.

[0028] At the same time, by disregarding the steering angle data from the specified distance onwards, the subsequent, more distant route of the driving path can be estimated particularly reliably, since unforeseen changes in steering angle or steering maneuvers can occur between the current position of the vehicle and the specified distance, meaning that the current steering angle data at the current position of the vehicle from the specified distance onwards do not provide a reliable basis for estimating the driving path.

[0029] Thus, the embodiment of the present invention proposed here enables particularly accurate and reliable estimation of the travel distance both in the near range up to the specified distance and in the far range beyond, with particularly low computational or data processing effort.

[0030] The specified distance can depend on factors such as the specific vehicle and its technical specifications, the surrounding environment, the vehicle's current speed, and so on. The specified distance can therefore be dynamic, meaning it is automatically adjusted dynamically during vehicle operation. Alternatively, the specified distance can be a fixed value. For example, the specified distance could be between 10 m and 30 m, or 15 m. Depending on the application, other values ​​may also be possible.

[0031] For example, a greater distance can be predefined or set if the vehicle has a smaller maximum possible steering angle and / or a smaller maximum possible rate of steering angle change. Similarly, a greater distance can be predefined or set if the vehicle is traveling at a higher speed. Likewise, a greater distance can be predefined or set on a highway or a road similar to a highway than in urban areas. This allows for a particularly situation-adapted, i.e., optimized, driving distance estimation for different situations or environments.

[0032] The present invention further relates to an assistance system for a motor vehicle. The assistance system according to the invention has an interface – implemented in hardware and / or software – for acquiring measurement data that characterizes a traffic situation of the motor vehicle. Furthermore, the assistance system according to the invention has a process unit, for example, a microchip, microprocessor, microcontroller, or the like, and a computer-readable data storage device coupled thereto. The assistance system according to the invention is configured to execute the method according to the invention, in particular automatically. The assistance system according to the invention can therefore be used for a regular, continuous, or quasi-continuous estimation or prediction of the driving speed of the motor vehicle, for example, depending on a measurement or recording frequency of the measurement data.An update of the estimated or predicted driving distance of the motor vehicle may be provided. For this purpose, a corresponding operating or computer program may be stored in the data storage device, which encodes or implements the process steps, measures, or sequences mentioned in connection with the method according to the invention, or corresponding control instructions. This operating or computer program can then be executed by means of the process device in order to carry out the corresponding method or to initiate its execution.

[0033] The invention further relates to a motor vehicle comprising environmental sensors for recording environmental data and an assistance system according to the invention. The environmental data can be data that describe or characterize the respective environment of the motor vehicle, particularly in the direction of travel. The environmental sensors can be part of the assistance system or connected to it, for example, via the vehicle's electrical system. The motor vehicle according to the invention can, in particular, be the motor vehicle mentioned in connection with the method according to the invention and / or in connection with the assistance system according to the invention, or correspond to it.

[0034] Further features of the invention may become apparent from the claims, the figures, and the description of the figures. The features and combinations of features mentioned above in the description, as well as the features and combinations of features shown below in the description of the figures and / or in the figures themselves, can be used not only in the combinations specified, but also in other combinations or on their own, without departing from the scope of the invention.

[0035] The drawing shows, in its single figure, an exemplary schematic overview of a traffic situation to illustrate an information-filter-based driving path estimation for a vehicle. For the sake of clarity, only a representative selection of elements that appear multiple times are explicitly marked in the figure.

[0036] Identifying objects relevant to a vehicle's longitudinal and lateral control may require determining the vehicle's potential path, to which all detected objects can then be related. To ensure high availability of the path, or rather, a current estimate of the path in a wide variety of traffic situations and road conditions, a predefined fusion algorithm can be applied to fuse various measurement or input data from different data sources, such as smart, heterogeneous sensors or similar devices. This allows different aspects of the respective environment, such as data on lane markings, roadside structures, grass verges, curbs, and / or the like, to be captured and combined into a coherent overall picture of the path.The driving path can then be assigned objects detected in the respective environment, especially moving objects. Moving objects in the vehicle's vicinity, particularly those assigned to the driving path, can be used or taken into account for longitudinal control functions such as ACC, intersection assist, adaptive recuperation, and / or the like, as well as for lateral control functions such as steering and lane guidance assist.

[0037] Previous approaches often assume that the vehicle's path, unless already known from a planning component of an automated driving system, corresponds to the course of the currently driven road or lane where the vehicle is located and must be determined using sensors that capture the surrounding environment. This path can be determined, for example, in a recursive estimation process that compares a current understanding or estimate of the road or lane's course with lane markings detected in sensor data, such as camera images.

[0038] The problem to be solved, therefore, is to determine or estimate the preceding lane or its course. Up to now, corresponding estimation processes typically operate in the corresponding state space of the solutions, where the uncertain state is updated with each uncertain measurement—that is, with each acquisition or recording of uncertain measurement data, or, for example, with each uncertain detection of lane markings, the road or lane alignment, or the like. This uncertain state can be specified, for example, by parameters or parameter values ​​of a parametric representation of the geometry of the road or the lane being traveled. Such an update allows the estimated state to be better aligned with the respective measurement, or adapted to the measurement, while simultaneously reducing the uncertainty of the state estimate.In previous approaches, the uncertainties of the measurements or the measurement data are typically represented using high-dimensional matrices of the covariances of the measured quantities.

[0039] A disadvantage of the current approach becomes increasingly apparent as more diverse measurements or data points—for example, data from different sensors or high-resolution measurements of the road's course at varying distances from the vehicle—are to be considered in the estimation process. Updating the estimate by incorporating these new measurements or data points can, in current approaches, require the inversion of a correspondingly high-dimensional matrix representing the uncertainties of the measurements. Such a matrix might, for example, have a size of 500 x 500. Inverting such matrices can require considerable computational effort, while embedded systems, such as those often underlying today's driver assistance systems, offer only very limited computing time and power.

[0040] Against this backgroundFig. Figure 1 is an exemplary schematic and partial overview of a traffic situation. Specifically, a section of road 1 is shown, which is bounded by road edges 2. Road 1 has, for example, two lanes, separated from each other by a lane marking 3. Furthermore, a guardrail 4 runs along one of the road edges 2.

[0041] A motor vehicle 5 is moving along road 1. This motor vehicle 5 may be equipped with assistance functions that use the current driving distance of the motor vehicle 5 as a data basis. To meet this requirement, the motor vehicle 5 is configured for continuous or regular estimation of the driving distance or for corresponding updates of the estimated driving distance. For this purpose, the motor vehicle 5 has environmental sensors 6 and an assistance system 7. The assistance system 7 can acquire environmental data recorded by the environmental sensors 6, as well as other data, such as steering angle data of the motor vehicle 5, via an interface 8. To process this data for estimating the driving distance, the assistance system 7 can have a processor 9 and a computer-readable data storage device 10, as shown schematically here.

[0042] The collected data can be of different types, originate from different sources, and characterize or describe the environment of the motor vehicle 5 at different locations. Several corresponding data or measurement points 11 are represented here as examples, although for the sake of clarity only a selection is explicitly labeled. The data or measurement points 11 can, for example, originate from another vehicle 12 also traveling on the road 1, describe its trajectory 13, include one or more points on a future path 17 of the motor vehicle 5 derived from the current steering angle or position of steered wheels 16, i.e., projected into the future based on the steering angle, or be or represent measurements or detections of the road edge 2, the lane marking 3, the guardrail 4, and / or the like.

[0043] Based on this, the assistance system 7 continuously and repeatedly determines a current hose estimate 14 during the operation of the vehicle 5, which is also schematically illustrated here. The respective hose estimate 14 can be described by relatively few parameters compared to the number or dimensions of the data recorded as the basis for the hose estimate 14. In this specific case, a condition to be estimated for the hose can, for example, consist of four parameters, each specifying the curvature of the hose at different distances, which are represented here by example as support points 15.The assistance system 7 can then, using a predefined model, for example stored in the data memory 10, which uses this currently estimated state as input, describe, i.e., determine, the complete course of the driving hose, i.e., the complete driving hose estimate 14 for the vehicle 5. The currently current driving hose estimate 14 can then, for example, be stored in the data memory 10 and / or provided or transmitted via the interface 8.

[0044] Based on the observation that the dimension of the corresponding state space, which is defined by the parameters describing the state or progress of the tunnel tube, is smaller, in particular much smaller, than the dimension of the measurement space in which the acquired data or their uncertainties are specified or represented, a simplified, less computationally intensive, and therefore more efficient determination or update of the tunnel tube estimate 14 can be carried out compared to previous approaches. For this purpose, the assistance system 7 estimates the information vector and the associated information matrix based on the respective acquired data within the framework of the information filter formalism, instead of directly estimating the state of the tunnel tube and the associated uncertainty. Here, the application of the information filter is provided, whereby the respective updating of the tunnel tube estimate 14 is possible without a complex inversion or...Inverting the entire uncertainty matrix of the recorded data is not possible, i.e., it cannot be performed. Instead, the individual recorded data points or measurements, for example, from the individual measurement points 11, are considered independently of each other in the respective update step. In this way, inverting the entire uncertainty matrix can be effectively replaced by inversions of correspondingly smaller matrices, which are easier to calculate. The estimate of the respective state can then be obtained from the information vector after inversion of the comparatively low-dimensional information matrix.

[0045] For an initial starting point, i.e., an initial roadway estimate 14 at or after the vehicle 5 is put into operation, a simplified roadway estimate 14 based on the current steering angle of the vehicle 5 can be determined, which can then be continuously updated as described above, in particular taking into account environmental data or an estimate of the road 1 ahead of the vehicle 5 in the direction of travel based thereon. Thus, starting from a roadway initially based on the current steering angle of the vehicle 5, the estimate of the parameters indicating the state of the roadway, i.e., for example, the aforementioned curvatures at the multiple support points 15, can be continuously adapted to the latest recorded data or the latest measurement or estimate of the actual roadway 1 in a continuous process.

[0046] For this purpose, a multitude of different measurements from various sensors can be available at any given time, all of which can be combined into a uniform format for data fusion. Such measurements can include, for example, radio waves reflected from structural features or installations along the road or carriageway edge 2, such as the guardrail 4, back to a radar sensor of the environmental sensor system 6; markings, such as lane markings 3, and / or vegetation strips or grass verges, or the like, which are detected in camera images from a camera of the environmental sensor system 6; the trajectory 13 of the preceding vehicle 12; the collective movement of other road users in the respective environment; the steering angle or steering angle change of the vehicle 5; its position, orientation, direction of movement, and speed; and / or the like.

[0047] This allows, for example, several hundred uncertain measurements, such as a specific angle of the road's course at one of several different distances, to be available for fusion in each update step. By translating the problem into the information space—that is, expressing it using the information vector and information matrix—and applying the information filter, each update can be performed with very little computational effort and therefore very quickly, requiring significantly fewer computing resources, especially in real time. This can, for example, enable the execution of such an update step on a conventional embedded system or control unit in an average of less than 1 ms, even under unfavorable conditions.

[0048] This approach proposes a combination of estimating the condition of the road tube, describing its state using a relatively small number of parameters (especially geometric parameters, for example, a maximum of 20 parameters) compared to the data collected, and an information filter formalism. This allows a large number of diverse input data to be processed quickly, easily, and efficiently to generate the current road tube estimate.

[0049] Overall, the examples described show how runtime-optimized data fusion for determining the driving hose can be implemented, especially on embedded systems. Reference symbol list 1 Street 2 Roadside 3 lane markings 4 guardrail 5 Motor vehicle 6. Environmental sensors 7 Assistance systems 8 Interface 9 processor 10 Data storage 11 measuring points 12 foreign vehicle 13 Trajectory 14 Driving hose estimate 15 support point 16 steered wheels 17 Route

Claims

[1] Method for repeatedly estimating a current driving tube (14) of a motor vehicle (5), wherein during the operation of the motor vehicle (5) regularly repeatedly - several measurement data (11) that characterize a respective traffic situation of the motor vehicle (5) are recorded with associated uncertainties, - so that in each update step an updated information matrix and an updated information vector of the information filter formalism are estimated, and - an updated estimate of the driving tube (14) is determined from the updated information matrix and the updated information vector, characterized by, that the measurement data (11) are organized in a measurement data vector and the associated uncertainties in an uncertainty matrix, and in the update step the individual measurement data (11) and their associated uncertainty are taken into account independently of each other without inverting the entire uncertainty matrix. [2] Method according to claim 1, characterized by , that the measurement data (11) describe at least the road course ahead of the motor vehicle (5) in the direction of travel at different distances from the motor vehicle (5). [3] Method according to any one of the preceding claims, characterized by, that from the updated information matrix and the updated information vector an updated state for the driving tube (14) is first determined, which characterizes the driving tube (14) by predefined, in particular geometric, parameters, wherein the number of these parameters is smaller than the dimension of the measurement data (11). [4] Method according to claim 3, characterized by , that the specified parameters for characterizing the driving hose (14) include its curvature, in particular at several specified distances (15) from the motor vehicle (5). [5] Method according to claim 3 or 4, characterized by , that to determine the updated state of the driving tube (14) the last determined updated information matrix is ​​inverted and this inverted information matrix is ​​multiplied by the last determined updated information vector. [6] Method according to any one of claims 3 to 5, characterized by, that from the updated state a corresponding updated course of the driving hose (14) in the respective environment is determined by means of a given model, wherein the model models at least one given condition for a permissible real driving hose course (14). [7] Method according to any one of the preceding claims, characterized by, that as part of the measurement data (11) environmental data describing the environment of the motor vehicle (5) and steering angle data of the motor vehicle (5) are recorded and it is determined whether a lane change or turning maneuver of the motor vehicle (5) is likely to take place in an area from the current position of the motor vehicle (5) up to a predetermined distance from the motor vehicle (5) in its direction of travel and, if this is the case, the updated estimate of the driving path (14) is determined up to the predetermined distance based on the steering angle data and from the predetermined distance based on the environmental data without taking the steering angle data into account. [8] Assistance system (5) for a motor vehicle (5), comprising an interface for recording measurement data (11) that characterizes a traffic situation of the motor vehicle (5), a processor device (9) and a computer-readable data storage device (10) coupled thereto, wherein the assistance system (5) is configured to perform a method according to one of the preceding claims. [9] Motor vehicle (5) comprising an environmental sensor system (6) for recording environmental data and an assistance system (5) according to claim 8.

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