Method for calculating and providing a lane route

Abstract

The present invention relates to a method for calculating and providing a lane path of a road using a central computing unit that is connected to vehicles in a vehicle group in a data-technical manner. According to the present invention, the method comprises the following steps: providing a geometric map (K) including geometric lane boundaries (S), applying a grid network (G) having grid cells (G1 to G49) of a predetermined size to the map (K), collecting fleet data of the vehicles in the vehicle group, including a position sequence in which the vehicles in the vehicle group travel, calculating the orientation of the vehicles at the positions of the grid cells (G1 to G49) from the collected position sequence, discretizing the calculated vehicle orientation, generating a histogram (H) for each of the discretized vehicle orientations calculated at the positions of the respective grid cells (G1 to G49) for each individual grid cell position, selecting a map section having a predetermined number of grid cells (G1 to G49), and calculating a lane path in the map section using a learning-based method from the histogram (H) created for the grid cells (G1 to G49) of the map section and the geometric lane boundary (S) in the map section.

Description

Technical Field

[0001] The present invention relates to a method for calculating and providing a lane path of a road using a central computing unit that is technically connected to vehicles in a vehicle group.

Background Art

[0002] From German Patent Application Publication No. 102013211696, a method for completing and / or updating a digital road map of a geographical area, a device for an automobile, and an automobile are known. This method includes: a) detecting a digital image of a road section using a camera of an automobile present on the road section; b) calculating, using an arithmetic unit of the automobile, based on the image, the current position of the automobile in the width direction of the road section and / or at least one characteristic of the road section; c) transmitting a data set including information on the geographical position where the image was detected, and information on at least one calculated characteristic of the road section and / or the position in the width direction to a server device. Steps a) to c) are executed by a plurality of automobiles present in the area, and the digital road map is completed and / or updated using the server device based on the received data set.

[0003] From German Patent Application Publication No. 102013018315, a method for providing an environmental model for a vehicle is known. In this method, a grid map is used to display the environmental model, and this grid map is adapted to the lane path. For example, when the lane is curved, the cells of the grid map are also arranged in a curved manner.

[0004] From German Patent Application Publication No. 102013208521, a method for creating a road model is known. In this method, trajectory data is obtained from fleet data (vehicle group data), and a road model is created from these trajectory data using a statistical method.

[0005] From German Patent Application Publication No. 102019119002, a method for determining a lane is known. In this method, lane markings are scanned, the scanning direction is determined, and the scan is clustered considering that direction.

[0006] From European Patent No. 2888604, a method for determining a lane path of a vehicle is known. In this method, in a grid map, the position of the vehicle is determined, and the direction and distance of the vehicle with respect to grid cells including lane boundaries are determined.

SUMMARY OF THE INVENTION

PROBLEM TO BE SOLVED BY THE INVENTION

[0007] An object of the present invention is to provide a novel method for calculating a lane path of a road.

MEANS FOR SOLVING THE PROBLEM

[0008] According to the present invention, this object is achieved by a method having the features described in claim 1.

[0009] Advantageous embodiments of the present invention are the subject matter of the dependent claims.

[0010] According to the present invention, in a method for calculating and providing a lane route (lane course, lane passage) of a road using a central computing unit that is connected to vehicles in a vehicle group (fleet) technically in terms of data technology (from the perspective of data technology), a geometric map including geometric lane boundaries is provided, and a grid network having grid cells of a predetermined size is applied to the geometric map. Fleet data of the vehicles in the vehicle group is collected, and the fleet data includes a position sequence in which the vehicles in the vehicle group travel. Further, the orientation of the vehicle at the position of the grid cell is calculated from the collected position sequence, and the calculated vehicle orientation is discretized (individualized). Subsequently, for each individual grid cell position, a histogram for the vehicle orientation calculated and discretized at the position of each grid cell is generated respectively, a map section having a predetermined quantity (predetermined number) of grid cells is selected, and a lane route in the map section is calculated (determined) using a learning-based method from the histogram created for the grid cells in the map section and the geometric lane boundaries in the map section.

[0011] By applying this method, it becomes possible to perform a process that combines the geometric infrastructure and the fleet driving behavior. As a result, it becomes possible to completely automatically derive the lane route. In contrast, in the derivation of the lane route based purely on the geometric infrastructure, manual post-processing (reprocessing, restart) is always required, and in the derivation of the lane route based on the fleet driving behavior, complete coverage cannot be achieved.

[0012] By using geometric information, it is generally possible to ensure that the modeled lane route is directed (conforms) to a predetermined infrastructure.

[0013] In this method, since the fleet driving behavior is provided, the driving style of a human, that is, the manual driving operation of the vehicles in the vehicle group is considered. Thereby, for example, lane modeling in an unstructured area, for example, a place where there is no lane marking and / or bus lane is supported.

[0014] The learning-based method substantially automatically takes into account such special cases if they are represented in the dataset, i.e., the fleet data. The method is scalable using the fleet data, whereby the calculation (determination) of the lane path is continuously improved as the number of training examples increases.

[0015] Furthermore, the learning-based method can directly generate the geometric characteristics (geometry) of the lane path. Furthermore, old lane paths can be selectively considered in the learning-based method, so that only the parameters of the lane segment regarding the anchor point, alignment, curvature and / or width of the lane need to be predicted. Thereby, the learning process is simplified and it is ensured as much as possible to continuously generate geometrically valid lane segments.

[0016] By abstracting the lane path calculated based on the fleet data of vehicles into a grid network, it becomes possible to consistently process the driving behaviors of vehicles belonging to different groups of vehicles with different driving frequencies.

[0017] The multi-dimensional grid network enables the modeling of multiple driving directions, for example, in the area of two-way roads or intersections.

[0018] Grid networks created depending on (in response to) different points in time enable the detection of changes in the fleet driving behavior and thus lead to conclusions about changes in the road infrastructure. Therefore, changes that cannot be detected by signals from the vehicle sensor system (vehicle sensor system), such as traffic signs prohibiting a direction change, can also be calculated.

[0019] Since the processing is performed at the level of grid cells, the problem of calculating the lane path can be divided (broken down) into partial problems (sub-problems, lower-level problems), thereby enabling parallelization.

[0020] In one embodiment, the calculated lane route is provided so that the vehicles in the vehicle group can obtain it. Based on these provided calculated lane routes, optimized automatic driving operations and autonomous driving operations of the corresponding vehicles become possible. Since each lane route is known, in an automatic driving operation or an autonomous driving operation, for example, due to the absence of lane markings, it is not necessary for the driver of the vehicle to take over the driving task and move the vehicle by manual driving operation.

[0021] In another embodiment, the calculated lane route is provided to the vehicle as data in a digital map and stored in the vehicle, so that the automatic driving operation or autonomous driving operation of the vehicle can be significantly improved without driver intervention.

[0022] In one possible embodiment of the method, the calculated lane route is supplied to the trajectory planning module of the autonomous vehicles in the vehicle group. Subsequently, based on the calculated lane route, a trajectory for each autonomous vehicle is planned, so that an optimized autonomous driving operation of the vehicle can be substantially realized without intervention by handing over the driving task to the driver of the vehicle.

[0023] In one development form of the method, the training of the learning-based method is performed using the created histogram and the ground truth map created for a predetermined area. Therefore, the learning-based method is trained based on real data and optimized for calculating lane routes based on the transmitted fleet data.

[0024] Hereinafter, embodiments of the present invention will be described in more detail based on the drawings.

Brief Description of the Drawings

[0025]

Figure 1

Figure 2

Figure 3

Embodiments for Carrying Out the Invention

[0026] In any of the figures, the corresponding parts are labeled with the same reference numerals.

[0027] FIG. 1 shows a part of a geometric map K including geometric lane boundaries S to which a grid network G having grid cells G1 to G49 is applied (plotted).

[0028] FIG. 2 shows a grid network G having grid cells G1 to G49 filled (entered) by a data structure, and FIG. 3 shows a plurality of tubular corridors (passages, roads).

[0029] Generally, it is known that the automatic driving operation or autonomous driving operation of a vehicle requires a high-precision digital map, that is, a road map. The content of the digital map must be accurate and its content needs to be updated. In the application cases of automatic driving operations at level 2+ or higher of the standard SAE J3016, a digital map accurately modeled in terms of lanes by topology is essential. Mapping is respectively executed based on the signals detected by the vehicle sensors of the vehicles in a vehicle group (fleet), especially those of the vehicle manufacturer. By using the vehicles in the vehicle group, a relatively wide range can be covered. The vehicle transmits fleet data (vehicle group data) as information regarding static infrastructure such as identified traffic signs and lane markings to a central computing unit that is connected to the vehicles in the vehicle group technically (from the perspective of data technology).

[0030] In addition to those identified information regarding static infrastructure as peripheral information, in order to convert the detection regarding the vehicle coordinate system into a global coordinate system, the attitude measured by the satellite navigation of each vehicle, particularly the orientation, is also transmitted to the central computing unit. Further, the satellite navigation provides information regarding the driving behavior of individual vehicles, particularly information regarding drivable corridor F.

[0031] Hereinafter, a method for calculating and providing a lane route of a road will be described using a central computing unit that is technically connected to vehicles in a vehicle group. Here, the method is composed of multiple steps.

[0032] First, a geometric map K including lane boundaries S is provided, and a grid network G having grid cells G1 to G49 is applied. Here, the grid cells G1 to G49 have a predetermined size with respect to the map K. In particular, the geometric map K is provided from observations of vehicles in the vehicle group using a method known from the prior art. The individual steps related to this are so-called single-trace pose optimization, multi-trace alignment, and geometric aggregation.

[0033] The fleet data of vehicles in the vehicle group is collected by the central computing unit. Here, the fleet data includes the position sequence in which the vehicles in the vehicle group drive. The orientation of each vehicle is detected as fleet data, and the position sequence has a predetermined duration (period) or length. In order to anonymize the collected fleet data and make it difficult to draw conclusions regarding the movement profile of each vehicle, the duration or length is selected to be relatively short. Therefore, all orientations with respect to the geometric map K are detected in a localized (position-specific) manner for the exact position of the vehicle in the lane unit. In particular, the orientation of the vehicle at the positions of the grid cells G1 to G49 is calculated (determined) from the collected position sequence.

[0034] The calculated vehicle orientation is discretized (individualized). That is, the calculated vehicle orientation is classified into a predetermined pattern (grid, classification frame), for example, a 60° pattern. In other words, depending on each geographical location, a data structure for the grid network G is generated. The dependence of the grid network G on the geographical location, that is, the dependence on the global coordinate system having coordinates x, y, z, can be discretized (individualized) into 1m×1m squares using a predetermined pattern of the geographical map K.

[0035] Subsequently, for each individual grid cell position, a histogram H of the discretized vehicle orientations calculated at the positions of the respective grid cells G1 to G49 is generated. Here, for example, in order to be able to map geometric points having a plurality of driving directions in the intersection area, a frequency distribution depending on the driving direction is modeled in a multi-dimensional manner using the grid cells G1 to G49. The frequency distribution can be discretized, for example, using binning in 10° classes.

[0036] Learning-based methods, such as Convolutional Neural Network, Transformer Network, Graph Neural Network, supply a lane model as output information using, as input information, the geometric lane boundaries S, particularly lane markings, curbs, etc. from a pre-provided geometric map K, and also using the grid network G having a frequency distribution from fleet data.

[0037] The learning-based method is trained using a Ground-Truth map that maps the actual lane route for a specific area. The learning-based method learns, based on examples, how to generate a lane model from the geometric lane boundaries S (lane markings, curbs, etc.) and the histogram H.

[0038] FIG. 1 exemplarily shows a part of a grid network G over which a lane boundary S is superimposed. The mode of the distribution with respect to (over) the traveling direction is indicated by an arrow P.

[0039] It can be relatively easily seen that two arrows P pointing in opposite directions are represented in the grid cells G1 to G49 through which vehicles pass in both traveling directions.

[0040] The grid cells G1 to G49 in the first row Z1 are numbered, and the first corridor F represented by hatching and the second corridor F represented by another hatching are lane routes generated using a learning-based method.

[0041] FIG. 2 exemplarily shows respective histograms H for the grid cells G1 to G7 of the grid network G. A discretization (individualization) of the vehicle direction in 60° classes is selected. In particular, FIG. 2 shows respective histograms H in which the number of vehicle orientations discretized for each of the grid cells G1 to G7 shown in FIG. 1 is calculated.

[0042] According to the grid cell G5 shown in FIG. 1, it can be seen that a vehicle has traveled within this cell in the orientation represented by the downward arrow P. Therefore, the mode (maximum value of the distribution) of the histogram H is approximately 200°.

[0043] In the grid cells G6 and G7, vehicles travel in the orientations of vehicles in the opposite traveling direction, so the mode of the histogram H is approximately 20°.

[0044] Respective histograms H based on the grid cells G1 to G7 of the grid network G represent relatively efficiently any number of travels in the grid cells G1 to G49 of the grid network G applied on a geographical map K, and reproduce (reflect in a unified form) the free-floating behavior of vehicles (travel characteristics of a vehicle fleet) in a uniform form.

[0045] Based on such an abstraction process, the histogram H is used as a certain (consistent, coherent) input for a downstream learning-based method that derives a lane path including the geometric surrounding environment.

[0046] Furthermore, by utilizing such a uniform representation for different numbers of trips, changes in fleet driving behavior can be detected at a relatively early stage of the mapping process. That is, changes in fleet driving behavior that are not detected by signals from the vehicle sensor system, such as a special traffic sign prohibiting a direction change, can also be detected.

[0047] The lane path generated using the learning-based method is, from a geometric perspective, a tubular corridor F. For this reason, optionally, an existing old lane path can be considered using the learning-based method, so that this learning-based method only needs to predict the parameters of the lane segment such as, for example, the anchor point, orientation, curvature, and width of the lane. This can simplify the training process and ensure that a geometrically valid lane path is continuously generated. An example of such a tubular corridor F resulting from appropriately selecting the input parameters for the learning-based method is shown in Figure 3.

[0048] Furthermore, in this method, the lane path calculated in the shape of the tubular corridor F is provided to the vehicles of the vehicle group as data in the digital map.

[0049] In particular, since the calculated lane path is supplied to the trajectory planning module of an autonomous vehicle or a self-driving vehicle, the vehicle's trajectory can be planned according to the existing lane path.

Prior Art Documents

Patent Documents

[0050]

Patent Document 1

Patent Document 2

Claims

1. A method for calculating and providing a lane path of a road using a central computing unit that is connected to vehicles in a vehicle group in a data-technical manner, comprising: - providing a geometric map (K) including geometric lane boundaries (S), and applying a grid network (G) having grid cells (G1 to G49) of a predetermined size to the map (K); - collecting the fleet data of the vehicles in the vehicle group, including the position sequence in which the vehicles in the vehicle group travel; - calculating the orientation of the vehicles at the positions of the grid cells (G1 to G49) from the collected position sequence; - discretizing the calculated vehicle orientations; - generating a histogram (H) for each of the discretized vehicle orientations calculated at the positions of the respective grid cells (G1 to G49) for each individual grid cell position; - selecting a map section having a predetermined number of grid cells (G1 to G49), and - calculating the lane path in the map section using a learning-based method from the histogram (H) created for the grid cells (G1 to G49) of the map section and the geometric lane boundary (S) in the map section. A method, characterized in that it has the above steps.

2. The calculated lane path is provided so as to be acquirable by the vehicles in the vehicle group. The method according to claim 1, characterized in that it has the above feature.

3. The calculated lane path is provided to the vehicles as data in a digital map. The method according to claim 1 or 2, characterized in that it has the above feature.

4. The calculated lane path is supplied to the trajectory planning module of the autonomous vehicles in the vehicle group. The method according to any one of claims 1 to 3, characterized in that it has the above feature.

5. The training of the learning-based method is performed using the created histogram (H) and a ground-truth map created for a predetermined area. The method according to any one of claims 1 to 4, characterized in that it has the above feature.

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