METHOD FOR CREATING MAP DATA

Abstract

A method for creating map data having lane-specific resolution. Mapping data are initially received, the mapping data being transmitted by a vehicle, and including a vehicle trajectory and at least one object feature. Once the mapping data has been received, it is checked whether map data for local surroundings of the received mapping data are present. In the event the check indicates that no map data are present, map data are created from the mapping data and stored in a memory. In the event the check indicates that map data are already present, the map data are compared with the mapping data. If this comparison reveals that the mapping data differ from the map data, the map data are adapted, the adaptation taking place on the basis of a weighting factor. The adapted map data are subsequently stored in the memory.

Claims

1. A method for creating map data having lane-specific resolution, comprising the following steps: receiving mapping data, transmitted by a vehicle, the mapping data including a vehicle trajectory and at least one object feature; checking whether map data for local surroundings of the received mapping data are already present; based on the check indicating that no map data are present, creating map data from the mapping data and storing the map data in a memory; based on the check indicating that map data are present: comparing the map data with the mapping data; based on the comparison revealing that the mapping data differ from the map data: (i) adapting the map data, the adaptation taking place based on a weighting factor, and storing the adapted map data in the memory.

2. The method as recited in claim 1, wherein the creation of map data from the mapping data takes place in such a way that mapping data of multiple vehicles are averaged and are statistically evaluated, the creation of the map data taking place only when a predefined statistical accuracy of the averaged mapping data is present.

3. The method as recited in claim 1, wherein the adaptation of the map data takes place in such a way that in the case of a deviation of the mapping data from the map data, a shift of a traffic lane by a predefined distance is recognized and the shift is taken into account when adapting the map data.

4. The method as recited in claim 3, wherein the predefined distance is at least 50 centimeters.

5. The method as recited in claim 1, wherein the map data is created or adapted for a section of a map, the map data being linked at an edge of the section to an existing map.

6. The method as recited in claim 1, wherein the weighting factor includes a first weighting factor element of the vehicle trajectory and a second weighting factor element of the object feature, and the second weighting factor element being greater than the first weighting factor element.

7. The method as recited in claim 1, wherein the vehicle trajectory includes waypoints determined using satellite navigation.

8. The method as recited in claim 1, wherein the object feature includes a distance between an object and the vehicle determined using a sensor and a direction of the object relative to the vehicle determined using the sensor.

9. The method as recited in claim 1, wherein the map data are further transmitted to a vehicle and at least one driving function in the vehicle is controlled based on the map data.

10. A central processing unit, comprising: a data interface; a memory; and a processor; the central processing unit being configured to receive mapping data from a vehicle via the data interface, the mapping data including a vehicle trajectory, and the processor being configured to check whether map data for local surroundings of the received mapping data are already present, and, in the event the check indicates that no map data are present, to create map data from the mapping data and to store the map data in the memory, and, in the event the check indicates that map data are present, to compare the map data with the mapping data and, in the event the comparison reveals that the mapping data differ from the map data, to adapt the map data, the adaptation taking place based on a weighting factor, and to store the adapted map data in the memory.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Exemplary embodiments of the present invention are explained on the basis of the figures.

[0022] FIG. 1 schematically shows a flowchart of a method for creating map data, according to an example embodiment of the present invention.

[0023] FIG. 2 schematically shows a road course, according to an example embodiment of the present invention.

[0024] FIG. 3 schematically shows the road course of FIG. 2 at another point in time.

[0025] FIG. 4 schematically shows a further representation of a road course, according to an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0026] FIG. 1 shows a flowchart 100 of a method for creating map data having lane-specific resolution. In a receiving step 101, mapping data are received, which are transmitted by a vehicle. The mapping data include a vehicle trajectory and at least one object feature. The vehicle trajectories in this case may be consecutive points on a drive of a vehicle and may relate, for example, as two-dimensional or three-dimensional coordinates to a point of the earths' surface. The vehicle trajectory may be ascertained, for example, with the aid of a satellite navigation system. One further alternatively or additionally provided option for ascertaining the vehicle trajectory may be coupled navigation. The object feature may include a characteristic with respect to an object situated in the area of a road course or of a traffic lane.

[0027] In a check step 102, it is checked whether map data for local surroundings of the received mapping data are already present. If no map data are present as yet, i.e., the check indicates in check step 102 that no map data for the local surroundings are present, then map data are created from the mapping data in a creation step 103 and are subsequently stored in a memory in a storage step 104. If it is revealed in check step 102 that map data are already present, then the map data are compared with the mapping data in a comparison step 105. If the comparison of the map data with the mapping data in comparison step 105 reveals that the mapping data do not differ from the map data, then the method may be terminated and the receipt of the next mapping data in receiving step 101 may be awaited. In the event, however, it is revealed in comparison step 105 that the mapping data differ from the map data, an adaptation step 106 takes place, in which the map data are adapted. This adaptation takes place on the basis of a weighting factor, different values of vehicle trajectory and object feature being capable of being taken into account with the aid of the weighting factor. An alternative storage step 107 subsequently takes place, in which the adapted map data are stored in the memory.

[0028] The creation of the map data from the mapping data in creation step 103 may include recognizing a new, for example, branching traffic lane and connecting it to the existing map data with the non-branching traffic lane.

[0029] The individual method steps of flowchart 100 are now explained in greater detail in conjunction with FIGS. 2 and 3.

[0030] FIG. 2 shows a road course 200 including a first traffic lane 201 and a second traffic lane 202. A first road boundary line 203 delimits first traffic lane 201. A second road boundary line 204 delimits second traffic lane 202. A center line 205 is situated between traffic lanes 201, 202.

[0031] A vehicle 10 is situated in the area of road course 200, vehicle including a first sensor 11, a second sensor 12, a third sensor 13 as well as a vehicle interface 14. First sensor 11 in this case may be provided for ascertaining a vehicle trajectory of vehicle 10. Thus, first sensor 11 is able to record a vehicle trajectory of vehicle 10, the vehicle trajectory being able to include, for example, coordinates of vehicle 10 at various points in time. First sensor 11 may, for example, include a receiver for a satellite navigation system such as GPS, Galileo and/or GLONASS. First sensor 11 may further include an acceleration sensor and a rotation sensor and may thus be suitable for determining a vehicle trajectory with the aid of coupling navigation. First sensor 11 may also include in both cases the electronics necessary for the respective sensor. Second sensor 12 may be configured to determine an object feature. Third sensor 13 is optional and may also be used for ascertaining an object feature. Second sensor 12 and/or third sensor 13 in this case may include cameras, RADAR sensors and/or LIDAR sensors and the electronics necessary for evaluating the signals. The vehicle trajectory recorded with the aid of first sensor 11 and the object features recorded with the aid of second sensor 12 and, optionally, of third sensor 13 may be conveyed to a central processing unit 50 with the aid of vehicle interface 14. Central processing unit 50 in this case includes a data interface 51, a processor 52 as well as a memory 53. Processor 52 may be configured in this case to carry out the method steps explained in conjunction with FIG. 1. Vehicle interface 14 in this case may include, in particular, a transmitter for emitting and a receiver for receiving digital data. The same applies to data interface 51.

[0032] A first vehicle trajectory 211, a second vehicle trajectory 212 as well as third vehicle trajectory 213 are plotted on first traffic lane 201 of FIG. 2. These vehicle trajectories may, for example, have been transmitted to central processing unit 50 from vehicles that have already passed road course 200. If vehicle 10 now negotiates, for example, first traffic lane 201, then a further vehicle trajectory similar to first vehicle trajectory 211, to second vehicle trajectory 212 or to third vehicle trajectory 213 may be recorded. As shown in FIG. 2, first vehicle trajectory 211, second vehicle trajectory 212 as well as third vehicle trajectory 213 potentially differ in this case from one another. This may, for example, be due to the fact that different vehicles 10 are guided differently by vehicle drivers over road course 200 and the exact coordinates of vehicle trajectories 211, 212, 213 may potentially differ from one another. On second traffic lane 202, a fourth vehicle trajectory 214 and a fifth vehicle trajectory 215 are plotted, which are also slightly different. A first object 221, represented as a building, and a second object 222, represented as a tree, are further shown in the area of road course 200. Objects 221, 222 may be utilized in order to achieve a more accurate mapping of traffic lanes 201, 202. For this purpose, it is provided at particular points of first traffic lane 201 or of second traffic lane 202 to ascertain an object feature of first object 221 or of second object 222. The object feature of first object 221 in this case may include, for example, a first direction 231 from a predefined area 206 of first traffic lane 201 to first object 221. Alternatively or in addition, the object may include a first distance 241 from predefined area 206 of first traffic lane 201 to first object 221. First direction 231 and/or first distance 241 in this case may be ascertained with the aid of second sensor 12 and/or of third sensor 13. Second sensor 12 in this case may be a radar sensor or LIDAR sensor and may provide a run-time measurement of a radar pulse or of a laser pulse for determining first distance 241 and a location resolution of the backscattered radar pulse or laser pulse for ascertaining first direction 231. For a predefined area 206 of second traffic lane 202, a second direction 232 having a second distance 242 to first object 221 may also be evaluated.

[0033] In the process, it may be provided that vehicle trajectories 211, 212, 213 may be adapted on the basis of first direction 231 and/or of first distance 241. First object 221 is generally stationary relative to first traffic lane 201, so that a vehicle located in predefined area 206 of first traffic lane 201 will ascertain first direction 231 or first distance 241 to first object 221. In this way, a calibration of the measured data of first vehicle trajectory 211, of second vehicle trajectory 212 and/or of third vehicle trajectory 213 may take place. This is useful, in particular, because vehicle trajectories ascertained with the aid of satellite navigation or coupled navigation exhibit a relatively high degree of accuracy of the individual points relative to one another, however, an absolute accuracy is not necessarily guaranteed. Via the comparison with the object data (here, first direction 231 or first distance 241) it is possible to increase the accuracy. The same applies to fourth vehicle trajectory 214 and fifth vehicle trajectory 215 in predefined area 206 of second traffic lane 202, here, second direction 232 or second distance 242 to first object 221 being able to be evaluated and utilized.

[0034] A further predefined area 207 of first traffic lane 201 is situated on first traffic lane 201 in the area of second object 222. A further predefined area 207 of second traffic lane 202 is also situated on second traffic lane 202. An object feature, now of second object 222, may be recorded in further predefined area 207 of second traffic lane 202 and in the process a third direction 233 or a third distance 243 may be evaluated similarly to the above-described approach. The same applies to further predefined area 207 of first traffic lane 201 with respect to a fourth direction 234 and to a fourth distance 244.

[0035] When creating the map data in creation step 103, it may be provided, for example, that the creation of the map data from the mapping data takes place in such a way that mapping data of multiple vehicles 10 are averaged and statistically evaluated. The creation of the map data takes place only when a predefined statistical accuracy of the averaged mapping data is present. This may be the case in FIG. 2, for example, when vehicle 10 has negotiated first traffic lane 201 and a further vehicle trajectory of first traffic lane 201 has been recorded as a result.

[0036] Together with first vehicle trajectory 211, second vehicle trajectory 212 and third trajectory 213 already present, a sufficient statistical accuracy may now be potentially present, so that an averaging of vehicle trajectories 211, 212, 213 then present as well as the vehicle trajectory of vehicle 10 to be newly recorded allow for a mapping and a storage in the memory of first traffic lane 201. Two further vehicles 10 would then have to be moved on second traffic lane 202 in order to achieve an identical statistical accuracy. In this case, it may be provided, in particular, that in the predefined areas 206 or in further provided areas 207, an adaptation of the vehicle trajectories on the basis of the object features, i.e., of directions 231, 232, 233, 234 and of distances 241, 242, 243, 244 to respective object 221, 222 is utilized in order to correspondingly adapt vehicle trajectories 211, 212, 213, 214, 215.

[0037] FIG. 3 shows road course 200 of FIG. 2 at a later point in time. An obstacle 223 is now situated on first traffic lane 201, which may, for example, be part of a construction site or of another obstacle, which results in a traffic lane pivot. Thus, in the area of obstacle 223, first road boundary line 203 as well as center line 205 are correspondingly shifted. Vehicles that negotiate road course 200 in this area also exhibit a trajectory pivot necessary due to the traffic lane pivot, so that first vehicle trajectory 211, second vehicle trajectory 212, third vehicle trajectory 213, fourth vehicle trajectory 214 and fifth vehicle trajectory 215 are also pivoted here. Further predefined areas 207 are also located in the area of the traffic lane pivots, so that in the area of further predefined areas 207, the object features of second object 222, i.e., in particular, third direction 233 and fourth direction 234 as well as third distance 243 and fourth distance 244 differ accordingly from the representation of FIG. 2. Both the deviation of the object features of second object 222 and the deviation of vehicle trajectories 211, 212, 213, 214, 215 may be recognized in comparison step 105. Furthermore, an adaptation of the map data and thus an adaptation of the pieces of information about first traffic lane 201 and second traffic lane 202 stored in memory 53 may now take place in adaptation step 106 on the basis of the mapping data. This takes place on the basis of a weighting factor. In this case, it may, for example, be provided, in particular, that if a predefined area 206 and/or a further predefined area 207 is/are situated in the area of the deviating mapping data, the object data of associated first object 221 or 222 (in FIG. 3 only of object 222) are weighted more heavily than altered vehicle trajectories 211, 212, 213, 214, 215. It may, for example, be provided, in particular, that a shift of first traffic lane 201 or of second traffic lane 202 by a predefined distance, for example, by at least 50 centimeters, is recognized, in particular, with the aid of an evaluation of the object data.

[0038] Instead of objects 221, 222 represented in FIGS. 2 and 3, further objects having different directions may also be evaluated such as, for example, tunnel walls, bridge walls or building walls extending in parallel to road course 200. It may further be provided that the weighting factor includes a first weighting factor element of vehicle trajectories 211, 212, 213, 214, 215 and a second weighting factor element of the object feature of objects 221, 222. The second weighting factor element in this case may be greater than the first weighting factor element. It may, for example, be provided, in particular, that a changed object feature may be incorporated weighted at 70 to 90 percent into the adaptation of traffic lanes 201, 202, whereas a changed vehicle trajectory is incorporated only up to 30 to 10 percent into adapted traffic lanes 201, 202. For example, a weighting of 80 percent to 20 percent may be provided, i.e., that a changed object feature is incorporated weighted at 80 percent into the adaptation of traffic lanes 201, 202, whereas a changed vehicle trajectory is incorporated only up to 20 percent into adapted traffic lanes 201, 202.

[0039] Vehicle 10 further optionally includes a device 15 for the automated execution of a driving function. The map data stored in memory 53 may also be transferred via data interface 51 and vehicle interface 14 to vehicle 10. Device 15 may be configured to control at least one driving function on the basis of the map data. This may include, in particular, a longitudinal and transverse guidance of vehicle 10 and thus include a velocity control and a steering control of vehicle 10.

[0040] FIG. 4 schematically shows a view of a further road course 200 including a first traffic lane 201 and a second traffic lane 202. Map data are adapted in one section 208, because in this section 208, a pivoted first traffic lane 251 and a pivoted second traffic lane 252 have been detected using the methods explained in conjunction with FIGS. 2 and 3. The map data are linked at edges 209 of section 208 to an existing map made up of first traffic lane 201 and second traffic lane 202. In other words, this means, therefore, that section 208 is selected in such a way that pivoted traffic lanes 251, 252 are situated within section 208 and traffic lanes 201, 202 outside section 208 are not pivoted. Similarly, this methodology may also not be applied in adaptation step 106, but rather in creation step 103 if no map data are present yet in section 208.

[0041] Although the present invention has been described in detail via the preferred exemplary embodiments, the present invention is not restricted to the described examples and other variations may be inferred therefrom by those skilled in the art without departing from the scope of protection of the present invention.