AT527952A1 - Method for creating a three-dimensional road map - Google Patents

Abstract

The present invention provides a method for creating a three-dimensional road map (44), comprising the steps of: a) providing a vehicle (10) which comprises a measuring device (12) comprising an inertial measuring system, a GNSS antenna (20), and a camera device (13) time-synchronized with the inertial measuring system and the GNSS antenna (20); b) recording the vehicle orientation with the inertial measuring system, the vehicle position with the GNSS antenna (20), and images with the camera device (13) while driving along a lane (60), such that at least some of the images show an outer contact point (32) of a tire (16, 18) and a lane edge (36); c) determining a distance (42) between the outer contact point (32) of the tire (16, 18) and a lane edge point (34) of the lane edge (36) from vehicle alignment values recorded at the same time in step b) and one of the images; d) calculating an absolute position (52) of the lane edge point (34) defined in step c) from the vehicle position and the distance (42) determined in step c) for the time; e) repeating steps c) and d) for a plurality of temporally successive absolute positions (52); f) determining a first lane edge line (46) of the lane by connecting or interpolating the plurality of temporally successive absolute positions (52); and g) repeating steps b) to e) for a second lane edge (36) opposite the first lane edge (36), and subsequently h) determining a second lane edge line (48) of the lane (60) opposite the first lane edge (36) by connecting or interpolating the plurality of temporally successive absolute positions (54) of the second lane edge (36); and i) outputting the three-dimensional road map (44) comprising the first lane edge line (46) and the second lane edge line (48).

Description

Method for creating a three-dimensional road map

The present invention relates to a method for creating a three-dimensional road map, a three-dimensional road map created using such a method and a method for using such a three-dimensional

Road map for controlling an autonomous vehicle.

The present invention is based on known techniques for road surveying. Roads are measured manually, travelled with cameras or

scanned with lasers.

The disadvantage of the known solutions is that they are either inaccurate, particularly complex in terms of measurement technology or not suitable for applications such as autonomous driving

not capture all relevant parameters.

High-resolution three-dimensional road maps are regularly required, especially for applications in the field of autonomous driving. High-resolution maps can better consider important safety aspects in autonomous driving and contribute to the accurate detection of otherwise ambiguous driving situations. High-resolution three-dimensional maps are also advantageous for vehicle control and better route planning.

The object of the present invention is to at least partially overcome the disadvantages described above in a cost-effective and simple manner. In particular, the object of the present invention is to easily produce three-dimensional

to create road maps.

It is a further object of the invention to provide a three-dimensional road map that provides an accuracy of control of an autonomous vehicle

improved.

It is a further object of the invention to provide a three-dimensional road map

to provide a tool to evaluate the driving behavior of a vehicle.

It is a further object of the invention to provide a method by which the accuracy of a three-dimensional road map is guaranteed by means of a validation

can be.

It is a further object of the invention to provide a three-dimensional road map

which enables the location of a vehicle in real time.

The above objects are achieved by a method having the features of claim 1, a road map having the features of claim 13, and methods having the features of claims 14 and 15. Further features and details of the invention emerge from the subclaims, the description, and the drawings. Features and details described in connection with the method according to the invention naturally also apply in connection with the three-dimensional road map according to the invention, and vice versa, so that with regard to the disclosure of the individual aspects of the invention, reference is always made to each other.

can be.

According to the invention, a method is intended to enable the creation of a three-dimensional road map. Such a method is characterized by the following

Steps from:

a) providing a vehicle comprising a measuring device comprising an inertial measuring system, a GNSS antenna and a camera device time-synchronous with the inertial measuring system and the GNSS antenna;

b) recording the vehicle orientation with the inertial measurement system, the vehicle position with the GNSS antenna and images with the camera device while driving along a lane, so that at least some of the images show an outer contact point of a tire and a lane edge;

c) Determining a distance between the outer contact point of the tire and a lane edge point of the lane edge from values of the

Vehicle orientation and one of the images;

d) calculating an absolute position of the lane edge point defined in step c) from the vehicle position and the distance determined in step c) for the time;

e) repeating steps c) and d) for a plurality of temporally successive absolute positions;

f) determining a first lane edge line of the lane by connecting or interpolating the plurality of temporally successive absolute positions; and

g) Repeat steps b) to e) for a second lane edge opposite the first lane edge and then

h) determining a second lane edge line of the lane opposite the first lane edge by connecting or interpolating the majority of the temporally successive absolute positions of the second lane edge; and

ij) outputting the three-dimensional road map comprising the first

Lane edge line and the second lane edge line.

The core idea of a method according to the invention is that it is easy to measure lanes and in particular

To measure lane edge lines with high accuracy and reliability.

A vehicle equipped with an inertial measurement system and a GNSS antenna is used to record the map data. The vehicle is therefore a detection vehicle. The inertial measurement unit (IMU) comprises a combination of several inertial sensors such as acceleration sensors and gyroscopes. To record the six possible kinematic degrees of freedom, the inertial measurement system comprises three orthogonally positioned acceleration sensors (translation sensors) for recording the translational movement in the x-, y-, and z-axes, and three orthogonally mounted gyroscopes for recording rotating (circling) movements in the x-, y-, and z-axes.

The measuring unit provides three linear acceleration values for the

translational motion and three angular velocities for the rotation rates. In an inertial navigation system (INS), the linear velocity is determined from the measured values of the inertial measurement system for linear accelerations, after compensation for the acceleration due to gravity, by integration, and the position in space relative to a reference point is determined by further integration. The integration of the three angular velocities provides the orientation in space relative to a reference point. To determine the integration constant, to improve accuracy, and to minimize the zero-point and long-term drift of the above-mentioned

To correct the sensors mentioned, additional magnetometers can be integrated.

The GNSS antenna is used to determine the vehicle's three-dimensional position on Earth. GNSS is a collective term for the use of existing and future global satellite systems such as NAVSTAR GPS, GLONASS Galileo and

Beidou. The method can, in particular, be computer-implemented.

The measuring device can have one or more cameras, in particular three cameras, which each record videos and/or individual images taken at short intervals one after the other to carry out the method. The sampling rate is preferably at least 20 images per second, more preferably at least 30 images per second, and most preferably at least 30 images per second. However, it is also possible to carry out the method with significantly lower sampling rates. Preferably, the images taken in step b) depict the roadway at least every 2 meters. The cameras are synchronized with each other, with the inertial measurement system, and with the GNSS antenna. In principle, however, the different systems have different sampling rates. For example, the GNSS antenna can have a sampling rate of 100 Hz. The cameras can, for example, record images at 20 fps (20 Hz) while carrying out the method. The inertial measurement system can, for example, record data at a frequency of 30 Hz. Therefore, the timing of the captured images and measured values generally do not match exactly. To increase accuracy, a data synchronization step can be

For this purpose, the time of recording can be

The individual image is then calculated using data points from the GNSS antenna and the inertial measurement system that are adjusted to the individual image. The calculation can be performed by interpolating the measurement data, in particular by linear interpolation of the measured values from the GNSS antenna and the inertial measurement system around this measurement time. The data synchronization step performed in this way enables the data from the GNSS antenna and the inertial measurement system to be synchronized with those of the images, i.e., data is generated whose timestamps match the timestamps of the images. For this purpose, each individual camera preferably has synchronization channels with the following parameters: image acquisition time, latitude of the image position, longitude of the image position, elevation of the image position.

and orientation of the image position.

Preferably, the cameras are each arranged on the left and/or right side of the vehicle, and in the case of two or three cameras, possibly also in the middle, whereby the image section of the camera(s) is selected such that the left and/or right front wheel and, if applicable, the front of the vehicle are located in the entire image field of the cameras. With the camera device in combination with the previous camera calibration, and using the developed distance calculation logic, it is possible in particular to determine relative, time-synchronized distances of the wheel contact points (left and right) and/or the outer contact points of the tires as well as a known or determined point of intersection of the vehicle center plane in the vehicle's longitudinal direction with a plane through the vehicle's front axle and the road plane to the lane edges. The wheel contact points are each offset from the outer contact points of the tires by half a tire width. The distance can also be zero, particularly when crossing a lane edge line. Preferably, in step c), the zero point of the calibration can also be used to determine a distance between the outer contact point of the tire and a lane edge point. The zero point of the calibration is a fixed point of known position, in particular on a calibration tape. The zero point of the calibration can be used to determine the calibration accuracy.

increase.

The process allows the precise geographical position of the vehicle to be determined at each image acquisition time. In addition to the acquisition time and image number, the longitude, latitude, altitude, and accuracy of the GNSS measurement can be saved. The process steps for recording the measurement data provide consistent and synchronized measurement data.

which can be further processed automatically.

Steps c) and d) can be performed by determining the exact geographical position of the vehicle at each frame point of a survey video and, using a three-dimensional offset in Cartesian coordinates between the arrangement of the GNSS antenna and the outer tire contact point, the outer tire contact point is determined by a measurement. For this purpose, the longitude, latitude, altitude, and accuracy of the GNSS measurement are saved in addition to the recording time and the image number. This provides consistent and synchronized measurement data, which can be further processed automatically. If multiple tires and/or multiple outer tire contact points are on the

procedures, individual offsets must be taken into account.

Preferably, a synchronous measurement file of the

Image positions are saved.

The determination of a first and/or second lane edge by connecting or interpolating the temporally successive absolute positions can be carried out in particular by first performing a nearest neighbor search from a lane edge point to the next lane edge point starting from the time the image was taken, then time differences to the next lane edge point can be determined starting from the time the image was taken, then the minimum value is selected and finally a second nearest neighbor search is carried out to the second nearest lane edge point starting from the time the image was taken. By connecting or

By interpolating the lane edge points, each of the lane edge lines is determined.

The images of the first lane edge and the images of the second lane edge can be recorded consecutively or, if a suitable camera system is available, simultaneously. The lane edge points are thus ordered according to the time of recording and subsequently

Order connected or interpolated.

It is further advantageous if the recording in step b) is carried out in time-synchronized

intervals.

This allows the validity of the recorded data to be checked by easily identifying missing or additional data. Furthermore, it is possible to ensure consistent accuracy of the lane edge lines of the three-dimensional road map, especially if in step b) the

Lane is driven at an essentially constant speed.

Further advantages are achieved if the camera device comprises a first camera on the left side of the vehicle and a second camera on the right side of the vehicle, wherein the first camera is oriented such that its field of view encompasses an outer contact point of a left tire and the second camera is oriented such that its field of view encompasses an outer contact point of a right tire. In particular, the camera device may also comprise a third camera,

whose field of vision shows the front of the vehicle and the lane.

With two cameras aimed at different tires, images of the first and second edges can be captured in parallel, i.e., simultaneously or essentially simultaneously. A third, central camera facilitates data analysis, as it can create a complete image of the road. Here, too, a calibration procedure can be applied to compare all relevant data.

synchronize.

Further advantages are achieved if the method further comprises step a1) according to

Step a) includes:

a1) Calibration of the camera system with a sensor located on the floor of the lane

Calibration device.

The calibration of the area to be measured on the floor of the lane can be carried out, for example, using a calibration tape with a centimeter scale, which is placed on the lane. For this purpose, the following values are displayed on an image taken by the camera device and showing the calibration tape:

Calibration steps performed:

° Right-angle alignment of the calibration tape, especially with a laser

or a laser measuring device ° Setting calibration points on the calibration tape ° Saving the calibration file ° Validating the calibration with a distance measurement

First, calibration points are set on the centimeter measurements. The calibration then takes place on a line in the image. The calibration points are set on the calibration line in the image into the crosshairs on the calibration tape. The first calibration point is the zero point of the calibration, and the other calibration points are set manually and/or automatically in ascending order.

The ratio of image pixels to real distance measurement can then be determined.

Found in the outer edge area in a distance segment of 1 cm

For example, a total of 69 pixels can be measured to an accuracy of 0.15 cm.

The calibration accuracy can preferably be carried out before and after the measurement, i.e. steps b) and c), which ensures that the distance measurement is correct. This step thus increases the accuracy of the

Street map.

Preferably, the calibration device can comprise a measuring device with calibration markings arranged on the measuring device. As an alternative to the simple form of the centimeter rule as the calibration device, a measuring field that arranges measuring devices in two dimensions on the lane surface can also be used.

be provided.

Further advantages are achieved if the lane edge in step c) is

Edge detection algorithm is defined.

The edge detection algorithm is used to separate flat areas in an image from one another if they differ sufficiently in color or gray value, brightness, or texture along straight or curved lines. The edge detection algorithm is used to detect the transitions between these areas and mark them as edges. At the same time, however, a single, homogeneous area should be recognized as such and not divided into two areas by an edge. To do this, the color value gradient at each individual pixel of an image can be calculated by examining the area surrounding the point. This process is performed by discretely convolving the image with a convolution matrix, the edge operator. The latter defines the size of the surrounding area to be examined and the weighting of its individual pixels in the calculation. The edge operator determines an average gradient value for the central pixel from the surrounding area. If this operation is performed for all pixels in the image, an edge image can be compiled from the resulting matrix of gradients. On this image, the edges between homogeneous areas stand out, since a comparatively large gradient of the color values exists at these points. Other edge detection methods are also possible.

Further advantages are achieved if the method further comprises the steps:

Calculating a center position as the geographical center of an absolute position of the first lane edge line and one of the absolute positions of the first lane edge

opposite absolute position of the second lane edge line; and

Determining a lane centerline by connecting or interpolating the temporally successive center positions.

The lane centerline is another aspect of the three-dimensional road map that improves its use for autonomous driving. A deviation from an ideal driving line or the initiation of an overtaking maneuver can be more easily detected by autonomous vehicles if a lane centerline is present. The centerline is calculated from the two left and right lane edges. The centerline of each lane serves as a reference and is preferably interpolated at a constant distance. The resolution of the centerline can be varied as desired. This is often

also depends on the lane characteristics.

Further advantages can be achieved if the method described last further comprises the step: Calculating lane segments as a polygon from two consecutive center positions and those two absolute positions of the first lane edge line and the second lane edge line with which the two center positions were calculated, whereby a lane is defined as the sum of the

Lane segments are defined.

A lane segment thus consists of a total of six support points. These support points each contain two consecutive GNSS points of the left and right lane edge lines, as well as the lane centerline. This geographical information can be incorporated into the three-dimensional road map as a geometric model. With this information, the three-dimensional road map can

in particular include the following elements: ° Polygon line of the left lane edge ° Polygon line of the right lane edge ° Polygon line of the road centre ° Lane edge points of the left lane edge ° Lane edge points of the right lane edge ° Multipoints road centre ° Polygons of the lane segments * Polygons of the lane boundary

Lane segments also allow for more precise control and/or validation of autonomous vehicle behavior. For example, deviations from the expected driving behavior of other road users can be more easily detected if the vehicle accesses a three-dimensional road map containing polygon segments. In particular, unusual braking behavior and unusual

Acceleration behavior can be detected more easily and accurately.

Thus, there are three lines per lane: the left and right lane edge lines and the lane center line. For these three lines,

In particular, the parameters listed below are calculated:

° Lane length ° Lane width

° Lane line type

° Lane line color

° Lane direction

° Lane curvature

° Lane gradient

° Lane cross slope

° Lane line quality

The lane line type, lane line color and lane line quality can be derived from the images or the edge detection algorithm

The remaining parameters are calculated as shown in the following table:

Parameter Unit Calculation Description Lane length |m Distance point to point Length along the lane Lane width |m Distance point to point Lane width Lane deg Angle between two points Lane direction Lane curvature 1/m Change in angle between three points per 1 meter lane segment Lane curvature % Derivative of elevation Slope of the lane Lane slope % Difference in elevation is divided by the cross slope of the lane width.

Further advantages are achieved if the method further comprises the step:

Calculating a lane center area as a distance between the first lane edge line

and the second lane edge line, each of which is

Distance value from the first lane edge line and the second lane edge line. Lane center areas also allow for more precise control of autonomous vehicles. For example, deviations from the expected driving behavior of other road users can be more easily detected if the vehicle accesses a three-dimensional road map that includes lane center areas. In particular, unusual steering behavior can be detected more easily and accurately. Lane keeping assistants can also be integrated into

This allows you to react more quickly and safely to unusual driving situations.

Further advantages are achieved if the latter method further comprises the step

includes:

Calculating a lane edge area as the difference between the area spanned by the first lane edge line and the second lane edge line and the

Lane center area.

This creates a three-dimensional map that defines an edge area and a center area. This maps the distances defined by the lane center area

received benefits are further improved.

Further advantages are achieved if the method further comprises the step:

Linking static or dynamic lane parameters to the lane.

Static lane parameters that can be provided include, in particular: a lane direction, a lane width, a lane gradient, a lane cross slope, a lane length, a lane curvature of the left lane edge line, a lane curvature of the right lane edge line and/or a lane curvature of the lane center line.

Dynamic lane parameters can include, in particular, relative distances from the vehicle to the lane edge. These include the shortest distance to the left lane edge line, the right lane edge line, and the lane center line, the corresponding current distances, the corresponding average distances, and the corresponding orthogonal distances. An angle between the direction of travel and the lane center line or between the direction of travel and one of the

Lane edge lines can be provided as a dynamic driving parameter. Here too

can be a current angle, an average angle or a maximum angle as

dynamic lane parameters. Further advantages are achieved if the method further includes the steps:

Providing a three-dimensional road map, obtained according to one of the

preceding claims, repeatedly crossing the edge of the lane with the vehicle and thereby

Recording the vehicle orientation with the inertial measurement system, the vehicle position with the GNSS antenna and images with the camera device, so that at least some of the images show an outer contact point of a tire on

point to the edge of the lane;

Determination of accuracy-optimized lane edge points from vehicle orientation values recorded at the same time and the images that show the

outer contact point of the tire on the edge of the lane; and

Validate and/or correct the three-dimensional road map with the

Accuracy-optimized lane edge points.

HD map validation provides an accuracy measure of the measured route and thus of the previously created three-dimensional road map. The vehicle is used as a validation vehicle for this purpose. Validation is carried out with a highly accurate recording of the vehicle position, for example with an accuracy of less than +5 cm, preferably less than +3 cm, and particularly preferably less than +1.5 cm. An inherent measurement error of the camera during distance recordings is reduced by the fact that the outer contact point of the tire, which is known relative to the GNSS antenna, is also the measurement point. The temporal measurement grid should be selected as short as possible. In particular, the images should be acquired at intervals of 50 ms or less, preferably 20 ms or less, and particularly preferably at intervals of

10 ms or less. Images can be captured for each of the

Procedure may also involve recording a video.

Lane edges are deliberately crossed. The point of crossing is determined from the images or preferably the video and compared with the results of the

From this, a relative distance between the

Lane edge line of the previously output three-dimensional road map and the now calculated values of the lane edge line. If a lane center line marking is present, the validation can also be performed for the

Road center line.

According to a second aspect, the present invention provides a three-dimensional road map obtained by a method according to any one of the preceding

Claims.

The three-dimensional road map comprises at least the first and second lane edge lines in three-dimensional coordinates and high accuracy of preferably +3 cm or less, particularly preferably +1.5 cm or less and can further comprise the lane center line and/or the lane segments and/or the

Lane center area and/or lane edge area.

According to a third aspect, the invention provides a method of using a

three-dimensional road map for controlling an autonomous vehicle.

For this purpose, the three-dimensional road map can be integrated into the vehicle's control system, or the vehicle can access the three-dimensional road map for control via a communication network. The lane edge lines and/or the lane center line and/or the lane segments and/or the lane center area and/or the lane edge area can be used to carry out driving maneuvers and to assess the driving behavior of other road users and to adapt the control of the vehicle accordingly. According to the third aspect of the invention, an autonomous vehicle can, in addition to accessing sensor data relating to the current driving situation, also use the data integrated in the three-dimensional road map to assess road conditions and driving situations and thereby

to improve driving behavior.

According to a fourth aspect, the invention provides a method of using a

three-dimensional road map according to claim 13 for detecting crossing of a lane edge line, in particular in a racing competition, comprising the steps:

Providing a three-dimensional road map, in particular according to claim 13;

Moving a detection vehicle with a GNSS device on a

Road area covered by the three-dimensional road map; recording a vehicle position using the GNSS device;

Comparing the vehicle position with coordinates of the first lane edge line and/or the second lane edge line; and

Detecting crossing of a lane edge line by comparison.

For this purpose, the detection vehicle preferably also has a GNSS measuring device, and its dimensions, i.e., its maximum extent, are further preferably known by the GNSS measuring device. By recording the vehicle position over a period of time, a vehicle trajectory is formed. The maximum vehicle dimensions can be added by offsets. In this way, detection is also possible if the detection vehicle, which is currently participating in a racing competition, for example, is traveling with only one tire.

or part of a tire crosses a lane edge.

Further advantages, features and details of the invention will become apparent from the following description, in which exemplary embodiments of the invention are described in detail with reference to the drawings.

schematically:

Fig. 1 a vehicle with a measuring device for carrying out a

inventive method,

Fig. 2 a calibration device with recorded measured values for calibrating the

measuring device,

Fig. 3 is an image from a road measurement for measuring a distance between the outer contact point of the tire and a lane edge point of the

lane edge,

Fig. 4 a three-dimensional road map with two lane edge lines and one

lane center line,

Fig. 5 a three-dimensional road map with two lane edge lines, one

Lane centerline and lane segments,

Fig. 6 a three-dimensional road map with two lane edge lines, a

Lane center area and a lane edge area, Fig. 7 is a flow chart of a method according to the invention, and

Fig. 8 shows an alternative flow chart of a method according to the invention.

Figure 1 schematically shows a vehicle 10 with a measuring device 12, which is suitable for carrying out a method according to the invention for creating a three-dimensional road map. The measuring device 12 comprises a camera device 13 with three time-synchronized cameras 14. A first camera 14 is arranged on the left side of the vehicle 10 and a second camera 14 on the right side of the vehicle 10. The first and second cameras 14 can be arranged in particular in an area between the B-pillar of the vehicle 10 and a front wheel plane 19. The front wheel plane 19 runs perpendicular to the vehicle center plane 15 and parallel to the wheel axis. Particularly preferably, the cameras 14 can be arranged vertically above the tires 16, 18 on the vehicle 10. In such an arrangement, the fields of view of the cameras 14 can run parallel to the axis direction, whereby perspective distortion of the images only runs in one direction and is thus minimized, and consequently the accuracy of the method is increased. The first camera 14 is aligned such that its field of view includes an outer contact point of a left tire 16 and the second camera is aligned such that its field of view includes an outer contact point of a right tire 18. The cameras 14 are designed as video cameras in order to

to enable a sufficiently high image capture rate.

The measuring device 12 further comprises a GNSS antenna 20, which operates synchronously with the camera device 13. The GNSS antenna comprises a transmitting and receiving device as well as an evaluation device (neither explicitly shown) for evaluating the position data. Synchronous operation means

that the position data recorded by the GNSS antenna 20 have a time stamp and the images recorded by the camera device 13 also have a time stamp, and that the time stamps match each other for the same recording times. This allows a correlation between the position data and the images and a synchronization between the images and the data. An inertial measurement system that is also time-synchronous is not explicitly shown. A third camera 14 is arranged centrally on the vehicle, with the field of view of the third camera 14 directed forward, so that its field of view, during operation, covers the front of the vehicle and the lane.

The third camera can therefore simplify the evaluation.

The position of the GNSS antenna to the outer support points of the tires is

known.

The vehicle is arranged on a calibration device 22. The calibration device 22 is designed as a calibration belt 24 and has a scale that is arranged transversely to the direction of travel, in the axial direction below the front tires. In this position, a calibration of the camera device 13 can be carried out. For a particularly precise vertical arrangement of the calibration belt 24, a laser measuring device 21 is used. With the laser measuring device 21 arranged at one end of the calibration belt 24, the laser beam 17 emanating from it and with it the calibration belt 24 can be arranged particularly precisely parallel to the front wheel plane 19 and perpendicular to the vehicle center plane 15. Since the precise arrangement of the calibration belt 24 affects the calibration of the measuring device 12, the accuracy of the

procedure increased.

Figure 2 schematically shows a calibration tape 24. The calibration tape 24 has calibration points 26 marked by circles on a line. For the calibration of a camera 14, the calibration points are linked to the pixels of the camera image. This can be done manually or automatically. For this purpose, the calibration tape 24 is preferably arranged so that its distance runs along a pixel line of the camera 14. The calibration can be carried out in

Procedure by a software that uses mouse clicks as setting a calibration point

interpreted. Using the calibration points, the ratio

of pixel distances to the real distance measure.

In an example measurement, there are a total of 69 pixels in the outer edge area of the image in a distance segment of 1 cm. In this example,

Can be measured with an accuracy of 0.015 cm.

Figure 3 shows an image of a road measurement taken while driving along a lane. The image shows an outer support point

of the right front tire 14

For measuring a distance between the outer contact point 32 of the tire 14 and a lane edge point 34 of the lane edge 36. In this example, the lane edge is defined by the lower edge of a curb 38 facing the lane 30. In other cases, the lane edge 36 can be defined by a road marking or in some other way. The lane edge 36 can be determined and/or defined using an edge detection algorithm. The errors from machine edge detection can optionally be compensated for in order to achieve greater accuracy and, in particular, eliminated using human logic. This can compensate for effects that arise, for example, due to sunlight making detection difficult, shadows, leaves, sand, or other objects obstructing the lane markings, or due to the fact that no

Road markings are present.

To determine a lane edge point 34, a distance 42 between the outer contact point 32 of the tire 14 and a lane edge point 34 of the lane edge 36 is determined from vehicle orientation values and the image recorded at the same time in step b). The known offset between the GNSS antenna 20 and the outer contact point 32 of the tire 14 is taken into account.

The distance 42 is determined from a connecting line 40 between the outer contact point 32 of the tire 14 and a lane edge point 34, which runs according to the pixel line of the camera used during calibration.

Absolute position in GNSS coordinates of the lane edge point is then calculated from

the vehicle position and the distance 42 for the time point.

These steps are repeated for both lane edges. For this purpose, the vehicle 10 can be equipped with only one camera 14, which, for example, only records the right tire 14. In this case, the lane 30 is traveled twice, each time in a different direction, so that absolute positions

both lane edges 36 can be recorded.

If, however, the camera device has two cameras 14 arranged on different sides of the vehicle 10, it may be sufficient for the lane 30 to be driven on only once and for the images of both lane edges 36 to be recorded simultaneously.

can be.

By connecting or interpolating a plurality of temporally successive absolute positions of the first and second lane edges 36 separately, lane edge lines of the first and second lane edges are determined. In this way

become

Figure 4 shows a three-dimensional road map 44, which includes a first lane edge line 46, a second lane edge line 48 and a lane center line 50. The first and second lane edge lines 46, 48 have been determined by interpolating respective absolute positions 52, 54 and therefore form

three-dimensional GNSS coordinate system.

The lane center line 50 is calculated from the two lane edge lines 46, 48 and/or their absolute positions 52, 54. The lane center line 50 serves as a reference and is interpolated at a constant distance. The resolution of the lane center line 50 can, in principle, be varied as desired, in particular depending on the lane characteristics. In the example shown, the lane center line 50 can be determined by first calculating a center position 56 as the geographical center point of an absolute position 52 of the first lane edge line 44 and an absolute position 54 of the second lane edge line 48 opposite the absolute position 52 of the first lane edge. Subsequently, the

Lane center line 50 by connecting or interpolating the temporally successive

following middle positions 56 are determined.

Fig. 5 shows a three-dimensional road map 44 with lane segments 58. The lane segments 58 are defined according to the invention as a polygon consisting of two consecutive center positions 56 and the two absolute positions 52 of the first lane edge line 46 and the second lane edge line 48 with which the two center positions 56 were calculated. A lane is defined as the sum of the lane segments 58. The sum of the lane segments 58 thus forms a surface in three-dimensional space. This surface and thus the lane 60 can be further smoothed by means of a three-dimensional interpolation between the lane segments 58 and/or the absolute positions 52, 54 and center positions 56. A lane segment 58 thus consists of a total of six support points. These support points each contain two consecutive GNSS points of the left and

right lane edge and the center of lane 60.

Figure 6 shows a three-dimensional road map 44 with two lane edge lines 46, 48, a lane center area 62 and a lane edge area 64, wherein the lane edge area 64 consists of two spatially separated areas.

The lane center area 62 is an area located between the first lane edge line 46 and the second lane edge line 48, each spaced by a distance value from the first lane edge line 46 and the second lane edge line 48. It can be calculated using simple geometric considerations. The lane edge area 64 can be calculated as the difference between the area spanned by the first lane edge line 46 and the second lane edge line 48.

and the lane center area 62 are calculated.

Fig. 7 shows a flow chart of a method according to the invention.

In step a), a vehicle is provided which comprises a measuring device comprising an inertial measuring system, a GNSS antenna and a sensor connected to the inertial measuring system and

the GNSS antenna includes time-synchronous camera equipment.

In step b) the vehicle orientation is determined while driving along the lane with

the inertial measurement system, the vehicle position with the GNSS antenna and images with

of the camera device. At least some of the images show

an outer contact point of a tire and a lane edge.

In the subsequent step c), the distance between the outer tire contact point and a lane edge point is determined. This distance is determined from vehicle alignment values recorded at the same time in step b) and one of the images.

In step d), an absolute position of the lane edge point defined in step c) is calculated. The calculation is carried out from the vehicle position and the

certain distance for the time point is calculated.

Step e) is a repetition step. Here, steps c) and d) are repeated for a plurality of consecutive absolute positions. The number of repetitions depends on the length of the lane to be traveled or the total measurement duration of the measurement unit. The number of repetitions is generally n.

freely selectable.

In step f), a first lane edge line of the lane is determined. The determination is performed by connecting or interpolating the majority of the temporally consecutive absolute positions.

Parallel to step f), after step f) or before step f), step g) is performed, in which steps b) to e) are repeated for a second lane edge opposite the first lane edge. This is a one-time repetition, which is usually performed separately in the case where the

Camera system only takes pictures on one side of the vehicle.

Subsequently, in step h), a second lane edge line of the lane opposite the first lane edge is determined. The determination is carried out by connecting or interpolating the majority of the temporally successive

Absolute positions of the second lane edge. Finally, in step i) the three-dimensional road map, which includes at least the first

lane edge line and the second lane edge line.

Figure 8 shows an alternative method according to the invention, which includes all

Figure 7. In contrast to the steps described in Figure 7

In this method, steps b), c), d), e) are carried out in parallel for the first and second lane edges to determine the data for determining the first and second lane edge lines. The parallel execution of these steps is possible if the vehicle's camera system is configured to capture images on both sides of the vehicle simultaneously, for example, by two or more

Cameras.

The above explanations of the embodiments describe the

present invention exclusively within the scope of examples.

List of reference symbols

10 vehicles

12 Measuring device

13 Camera setup

14 Camera

15 Vehicle center plane 16 Left tire

17 Laser beam

18 right tire

19 front wheel plane

20 GNSS antenna

21 Laser measuring device 22 Calibration device

24 calibration tape

26 Calibration point

30 lanes

32 outer support point 34 lane edge point

36 Lane edge

38 curb

40 connecting line

42 distance

44 three-dimensional road map 46 first lane edge line 48 second lane edge line 50 lane center line

52 Absolute position of the first lane edge line 54 Absolute position of the second lane edge line 56 Center position

58 lane segment

60 lane

62 Lane center area 64 Lane edge area

Claims (15)

Claims 1. A method for creating a three-dimensional road map (44), comprising the steps of: a) providing a vehicle (10) which comprises a measuring device (12) comprising an inertial measuring system, a GNSS antenna (20) and a camera device (13) time-synchronized with the inertial measuring system and the GNSS antenna (20); b) recording the vehicle orientation with the inertial measurement system, the vehicle position with the GNSS antenna (20) and images with the camera device (13) while driving along a lane (60), such that at least some of the images show an outer contact point (32) of a tire (16, 18) and a lane edge (36); c) determining a distance (42) between the outer contact point (32) of the tire (16, 18) and a lane edge point (34) of the lane edge (36) from values of the vehicle orientation recorded at the same time in step b) and one of the images; d) calculating an absolute position (52) of the lane edge point (34) defined in step c) from the vehicle position and the distance (42) determined in step c) for the time; e) repeating steps c) and d) for a plurality of temporally successive absolute positions (52); f) determining a first lane edge line (46) of the lane by connecting or interpolating the plurality of temporally successive absolute positions (52); and g) repeating steps b) to e) for a second lane edge (36) opposite the first lane edge (36) and then h) determining a second lane edge line (48) of the lane (60) opposite the first lane edge (36) by connecting or interpolating the plurality of temporally successive absolute positions (54) of the second lane edge (36); and ij) outputting the three-dimensional road map (44) comprising the first Lane edge line (46) and the second lane edge line (48). 2. The method according to claim 1, wherein the recording in step b) is carried out in time-synchronous intervals. 3. Method according to one of the preceding claims, wherein the camera device (13) has a first camera (14) on the left side of the vehicle (10) and a second camera (14) on the right side of the vehicle (10), wherein the first camera (14) is aligned such that its field of view includes an outer support point (32) of a left tire (16) and the second camera (14) is aligned such that its field of view includes an outer support point (32) of a right tire (18). 4. Method according to one of the preceding claims, further comprising step a1) after step a): a1) Calibrating the camera device (13) with a camera mounted on the floor of the lane arranged calibration device (22). 5. The method according to claim 4, wherein the calibration device (22) comprises a measuring device with calibration markings arranged on the measuring device includes. 6. Method according to one of the preceding claims, wherein the lane edge (36) is defined in step c) with an edge detection algorithm. 7. Method according to one of the preceding claims, further comprising the steps: Calculating a center position (56) as a geographical center of an absolute position (52) of the first lane edge line (46) and a position opposite the absolute position (52) of the first lane edge (36) Absolute position (54) of the second lane edge line (48); and Determining a lane center line (50) by connecting or interpolating the temporally successive middle positions (56). 8. The method of claim 7, further comprising the step: Calculating lane segments (58) as a polygon from two consecutive center positions (56) and those two absolute positions (52, 54) of the first lane edge line (46) and the second lane edge line (48) with which the two center positions (56) were calculated, wherein the Lane (60) is defined as the sum of the lane segments (58). 9. Method according to one of the preceding claims, further comprising the step: Calculating a lane center region (62) as a region lying between the first lane edge line (46) and the second lane edge line (48), each of which is offset by a distance value from the first lane edge line (46) and the second lane edge line (48). 10. The method of claim 9, further comprising the step: Calculating a lane edge area (64) as the difference between the area spanned by the first lane edge line (46) and the second lane edge line (48) area and the lane center area (62). 11. Method according to one of the preceding claims, further comprising the step: Linking static or dynamic lane parameters to the lane (60). 12. Method according to one of the preceding claims, further comprising the steps: Providing a three-dimensional road map (44), in particular receiving according to one of the preceding claims, Driving over the edge of the lane (36) several times with the vehicle (10) and thereby Recording the vehicle orientation with the inertial measurement system, the vehicle position with the GNSS antenna (20) and images with the camera device (13), so that at least some of the images show an outer contact point (32) of a tire (16, 18) on the lane edge (36); Determining lane edge points (34) optimized for accuracy from values of the vehicle alignment recorded at the same time and the images showing the outer contact point (32) of the tire (16, 18) on the lane edge (36); and Validating and/or correcting the three-dimensional road map (44) with the accuracy-optimized lane edge points (34). 13. Three-dimensional road map (44) obtained by a method according to one of the preceding claims. 14. Use of a three-dimensional road map (44) according to claim 13 for Controlling an autonomous vehicle. 15. Use of a three-dimensional road map (44) according to claim 13 for detecting crossing of a lane edge line (46, 48), in particular in a racing competition, comprising the steps of: providing a three-dimensional road map (44) according to claim 13; Moving a detection vehicle with a GNSS device on a road area covered by the three-dimensional road map (44); Recording a vehicle position using the GNSS device; Compare the vehicle position with the coordinates of the first lane edge line (46) and/or the second lane edge line (48); and Detecting crossing of a lane edge line (46, 48) by comparison.