Method and system for providing trajectory for at least one vehicle

Abstract

The invention relates to a method for providing a trajectory (10) for at least one vehicle (50), providing at least one trajectory (10) generated by a vehicle (50) or other source (60), the at least one trajectory (10) being assigned or assigned a confidence (11), the provided at least one trajectory (10) being received by the at least one vehicle (50), the at least one trajectory (10) is used taking into account the assigned confidence (11). Furthermore, the invention relates to a system (1) for providing a trajectory (10) for at least one vehicle (50), comprising at least one device (2) for providing at least one trajectory (10) generated by a vehicle (50) or other source (60), which at least one trajectory (10) is assigned or can be assigned a confidence (11), and the system comprises at least one vehicle (50) which is designed to receive the provided at least one trajectory (10) and to use the at least one trajectory (10) taking into account the assigned confidence (11).

Description

Field of the Invention

[0001] The present invention relates to a method and a system for providing a trajectory for at least one vehicle. Background Art

[0002] It is known to store in a central server (backend) and transmit to a vehicle, when needed, a trajectory for driving the vehicle partially or fully automatically. However, the background, in particular with respect to the environment and / or boundary conditions, may change with respect to these trajectories. However, such a change is noticed only when re-driving through the trajectory. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to improve a method and a system for providing a trajectory for at least one vehicle.

[0004] This technical problem is solved according to the present invention by a method and a system for providing a trajectory for at least one vehicle. Advantageous design solutions of the present invention are derived from the description.

[0005] In particular, a method for providing a trajectory for at least one vehicle is provided, wherein at least one trajectory generated by a vehicle or another source is provided, wherein the at least one trajectory is assigned or can be assigned a confidence level, wherein the at least one provided trajectory is received by at least one vehicle, and wherein the at least one trajectory is used taking into account the assigned confidence level.

[0006] Furthermore, in particular, a system for providing a trajectory for at least one vehicle is implemented, which includes at least one device for providing at least one trajectory generated by a vehicle or another source, wherein the at least one trajectory is assigned or can be assigned a confidence level, the system includes at least one vehicle, and wherein at least one vehicle is configured to receive the at least one provided trajectory and use the at least one trajectory taking into account the assigned confidence level.

[0007] The method and the system enable assigning a value to the provided trajectory, based on which it can be decided how credible the provided trajectory can be. For this purpose, a confidence level is assigned to the trajectory. Based on the assigned confidence level (or the value of the confidence level), it can be decided in what way the received vehicle implements the provided trajectory.

[0008] At least one trajectory can for example be a trajectory related to trained parking. In addition, at least one trajectory can also be a trajectory in flowing traffic, which is for example trained for driving. A trajectory particularly includes a set of ordered positions and / or orientations of a vehicle, particularly in the form of geographical coordinates and / or angle specifications. In addition, speed and / or acceleration can also be part of a trajectory. In addition, control data and / or metadata (such as environmental data, background data, etc.) can also be part of a trajectory.

[0009] In a vehicle, the method steps are particularly executed by means of a control device provided therefor.

[0010] Parts of the system, particularly at least one device and a control device in a vehicle, can be designed individually or integrally as a combination of hardware and software, for example as a program code executed on a microcontroller or a microprocessor. However, it can also be provided that parts are designed individually or integrally as an application-specific integrated circuit (ASIC) and / or a field-programmable gate array (FPGA). At least one device and a control device each particularly include at least one computing device and at least one memory. In addition, a communication device can be provided respectively.

[0011] In one embodiment, it is provided that at least one trajectory is provided by means of a central server. Such a central server can for example be provided for collecting trajectories generated by vehicles and / or other sources, for example by means of simulation, and providing the collected trajectories to at least one vehicle if necessary.

[0012] In one embodiment, it is provided that the central server checks and / or determines and / or assigns a confidence level. Thereby, a central institution for checking and / or determining and / or assigning a confidence level can be provided. Here, the server can in particular also determine the confidence level for the first time, particularly based on the trajectory itself and / or based on the (meta) data assigned to the trajectory, such as background, description of the source from which the trajectory was generated, etc.

[0013] In one embodiment, it is provided that the central server checks and / or determines and / or assigns a confidence level repeatedly and / or regularly. Thereby, the confidence level assigned to the trajectory can always be kept up to date.

[0014] In one embodiment, it is provided that at least one receiving vehicle checks and / or determines and / or assigns a confidence level. Thereby, the receiving vehicle itself can also check and / or determine and / or assign a confidence level. This can in particular determine the as new (or current) as possible value of the confidence level, since this can be done shortly before the trajectory is used by the vehicle.

[0015] In one embodiment, it is stipulated that the confidence level is determined considering the age and / or the last use of the at least one trajectory. Thereby, the currency or the continuous validity of the at least one trajectory can be considered. This is based on the idea that the older the at least one trajectory or the longer the time point of the last use of the at least one trajectory, the lower the confidence level of the at least one trajectory. In particular, it can be considered here that little - used trajectories become old faster. Conversely, frequently - used trajectories become old more slowly. The reason is that the longer the time elapsed, the greater the probability of changes occurring in the environment of the at least one trajectory. After generation or after the last check, the value of the currency of the at least one trajectory especially starts with a positive value, and this value decreases as it gets older.

[0016] It can be stipulated that the degree of aging can be taken into account by means of a decay coefficient. The decay coefficient is especially a coefficient that shows to what extent the aforementioned initially positive value decreases over time.

[0017] In one embodiment, it is stipulated that the confidence level is determined considering the robustness and / or stability of the at least one trajectory. The robustness and / or stability of the at least one trajectory especially refers to the extent to which the (rated) path corresponding to the at least one trajectory needs to be adjusted during (the last) passage. In other words, if the rated path of the at least one trajectory can always be passed through, there is a maximum of robustness and / or stability. If the rated path can only be partially passed through each time, the value of robustness and / or stability is smaller. Due to changes in the environment of the trajectory, it is especially decisive how long the path to be traveled by the trajectory has not changed. Here, dynamic influences are especially relevant, such as other traffic participants, temporary construction sites, and / or long - term construction - related changes. The changing position of other vehicles during parking, the changing vegetation, and / or blockades due to renovation measures may require an adjustment of the path to be traveled and the deviation between the actual path traveled (adjusted) and the rated path of the trajectory can be detected and / or determined during the use of the at least one path. The confidence level can be determined and / or adjusted during inspection considering the deviation and / or the degree of adjustment.

[0018] If there is initially a newly - generated trajectory, no conclusion can be drawn about the robustness and / or stability yet. However, once the path corresponding to the trajectory has been traveled through multiple times, the relevance of the path is strengthened and the robustness and / or stability of the trajectory increases.

[0019] Here, it can be stipulated that a statistical scatter value (which can also be called trajectory loyalty) is additionally determined from the corresponding driving deviation and this scatter value is considered when determining the confidence level. The scatter value can also be determined and considered separately for different positions of the trajectory.

[0020] In one embodiment, it is provided that the confidence is determined taking into account the environment in which at least one trajectory is located. Thereby, environmental influences and / or ambient influences that may affect the confidence of at least one trajectory can be taken into account. In particular, the ambiguity during the detection or training of at least one trajectory can be considered here. As a supplement or alternative, the ambiguity that occurs when using at least one trajectory can also be considered. Here, in particular, the following influencing factors can be considered jointly:

[0021] - Weather;

[0022] - Sun position;

[0023] - Vegetation that acts on the environment differently according to the season;

[0024] - Load generated by road traffic;

[0025] - Frequency of accidents;

[0026] - Concentration of other traffic participants, such as pedestrians, cyclists, intra-company traffic, trams, etc.;

[0027] - Lane state;

[0028] - Frequency of blockades and traffic jams;

[0029] - Diversion of other traffic flows in the area of the trajectory.

[0030] For one or more of these influencing factors, one factor or parameter can be considered separately when checking and / or determining the confidence.

[0031] Here, it can be provided that the robustness and / or stability of at least one trajectory when it ages is considered. In particular, it can be provided that sections of the path or trajectory that are evaluated as being very robust and / or stable age more slowly than sections that are evaluated as being less robust and / or stable. Based on this, for example, the aforementioned attenuation coefficient can be determined.

[0032] In particular, the following influencing factors can be considered when determining the robustness and / or stability:

[0033] - Amount of information available (e.g., more vegetation or less / no vegetation, greater change in vegetation or smaller change in vegetation; expressed, for example, in the form of the number of changing or unchanged features);

[0034] - Information frequency (statistics of changes);

[0035] - Information consistency (i.e., how similar the deviations are at the same location);

[0036] - Information loyalty (i.e., how accurately the rated path of at least one trajectory is traveled through);

[0037] - The same or different update intervals (e.g., hourly, daily, monthly,...);

[0038] - The time period during which there is no change;

[0039] - The correlation between the confirmation and the deviation of the rated path of at least one trajectory (e.g., expressed as the correlation between the number of confirmed features and the number of deviated features).

[0040] For one or more of these influencing factors, one factor or parameter can be considered separately when determining the confidence level. The influencing factors can especially be in statistical form and can be evaluated to different extents according to the use of at least one trajectory.

[0041] In one embodiment, it is stipulated that the confidence level is determined taking into account the similarity between the characteristics of the vehicle (by which or for which at least one trajectory is generated) and the vehicle being received. Thus, the similarity between the source of at least one trajectory and the vehicle being received can be taken into account. Thus, in particular, it can be considered that a trajectory generated by or for one vehicle may not be very suitable for another vehicle with different characteristics. The following influencing factors can be considered in particular:

[0042] - Vehicle size, especially wheelbase, gauge, turning circle, external dimensions, front overhang and / or rear overhang, add-ons on the trailer hitch, coupled trailers, roof superstructure, etc.;

[0043] - Sensor system: type and age of the sensors used, the state of the sensors (such as the state of the sensors regarding defects, dirt);

[0044] - Vehicle manufacturer;

[0045] - Generation system / generation method used when generating at least one trajectory: real vehicle, simulation, creation in the map, manual description, etc.;

[0046] - The passing direction of at least one trajectory.

[0047] For one or more of these influencing factors, one factor or parameter can be considered separately when determining the confidence level. It can also be stipulated that a compatibility coefficient is determined by one or more of these influencing factors and taken into account when determining the confidence level. For example, if other vehicles with different configurations have (successfully) used at least one trajectory, the confidence level will be positively affected.

[0048] In one embodiment, it is provided that confidence levels are determined and / or assigned separately for multiple sections of a trajectory. Thereby, multiple sections of at least one trajectory can be considered individually and, in use, their respective confidence levels can be considered section by section.

[0049] In one embodiment, it is provided that a section of at least one trajectory is discarded by a receiving vehicle if the confidence level assigned to said section is below a preset threshold. Thereby, it can be achieved that at least one trajectory is only used when the confidence level reaches a minimum value.

[0050] In one embodiment, it is provided that the at least one vehicle determines the degree of automation for driving through the at least one trajectory based on the confidence level of the at least one trajectory. For example, it can be provided that in partial automated driving or in automated driving where the driver still needs to be able to take over steering, a lower confidence level value is sufficient. While in fully automated driving without a driver as a fallback level, a higher confidence level value must be met.

[0051] In one embodiment, it is provided that the at least one vehicle generates and provides feedback on the use of the at least one trajectory during and / or after driving through the at least one trajectory. Thereby, current information can be provided, which can be used as a basis for checking and / or determining the confidence level of the at least one trajectory. In particular, it can be provided that the vehicle transmits the feedback to a central server. Then, the central server can analyze the feedback and possible other feedback from other vehicles on the same trajectory, and check and / or determine the confidence level of the at least one trajectory. In particular, the confidence level can be checked and / or determined based on the aggregated data transmitted in this way by a large number of vehicles.

[0052] In one embodiment, it is provided that when the confidence level of the received at least one trajectory is below a preset threshold, the at least one vehicle retrains and provides the at least one trajectory. Thereby, a direct reaction to an overly low confidence level can be made by retraining the assigned trajectory. The training particularly includes detecting sensor data of the vehicle environment and recording control data and / or metadata for the traveled path. Then, the newly trained at least one trajectory can be transmitted to the central server and subsequently, in particular, provided to other vehicles. Description of the Drawings

[0053] The present invention will be explained in more detail below based on preferred embodiments with reference to the accompanying drawings. In the drawings:

[0054] Figure 1 A schematic diagram is shown for illustrating an embodiment of a system for providing a trajectory for at least one vehicle;

[0055] Figure 2 A schematic flowchart showing an embodiment for illustrating the method is shown;

[0056] Figure 3 A schematic flowchart showing an embodiment for illustrating the method is shown;

[0057] Figure 4 A schematic flowchart showing an embodiment for illustrating the method is shown;

[0058] Figure 5 A schematic flowchart showing an embodiment for illustrating the method is shown. Detailed implementation manners

[0059] Figure 1 A schematic diagram showing an embodiment for illustrating System 1 is shown, and the system is used to provide a trajectory 10 for at least one vehicle 50.

[0060] System 1 includes at least one device 2, and the device is used to provide at least one trajectory 10 generated by a vehicle 50 or other sources 60. The device 2 should be, for example, a central server 3. At least one device 2, especially the central server 3, especially includes a computing device 3-1 and a memory 3-2. For example, a plurality of trajectories 10 are stored in the memory 3-2, and the trajectories can be provided for different locations and / or different scenarios. An example of the trajectory 10 is a trajectory 10 that is trained to park a vehicle 50 in a parking lot (private or public parking lot) and can be used for partially automated or automated parking. However, the trajectory 10 can also be set for other scenarios in principle.

[0061] Assign a confidence level 11 to at least one trajectory 10 or be able to assign a confidence level 11 to at least one trajectory 10.

[0062] System 1 further includes at least one vehicle 50, wherein at least one vehicle 50 is configured to receive the provided at least one trajectory 10 and use the at least one trajectory 10 in consideration of the assigned confidence level 11. At least one vehicle 50 especially includes a control device 51, and the control device executes the method steps described for the vehicle 50. The vehicle 50 can especially be configured to implement at least one trajectory 10, that is, drive manually, partially automated, or automated. Here, the manual implementation especially refers to the driver driving through the trajectory 10 manually, for example, driving through the trajectory 10 with the aid of instructions.

[0063] At least one device 2, especially the central server 3 and at least one vehicle 50 especially have communication devices 4 and 52 to communicate with each other.

[0064] For example, the confidence level can be determined as a function of one or more parameters or influencing factors:

[0065] k = f (p1, p2, p3, …),

[0066] where k is the confidence level and p1, p2, p3,... represent parameters or influencing factors. A simple implementation with, for example, four parameters or influencing factors could be to sum the weighted values of each parameter:

[0067] k = a1 * p1 + a2 * p2 + a3 * p3 + a4 * p4,

[0068] where a1, a2, a3, and a4 are the weighting coefficients. However, other functions can also be set in principle. In particular, if the confidence level of a trajectory is high, the confidence level has a higher value, and if the confidence level of a trajectory is low, the confidence level has a lower value. For example, it can be stipulated that the confidence level is represented by a value in the range from 0 (no confidence) to 1 (highest confidence). Here, it can be stipulated that the parameters or influencing factors and the function can be normalized.

[0069] The confidence level parameter is determined for the first time especially by the corresponding source (such as a vehicle or a simulation). For example, the characteristics of the source (known / unknown / trajectory provider), the vehicle configuration, and the quality of the underlying map are all listed as the corresponding parameters or influencing factors here.

[0070] Here, it can be stipulated that the central server 3 checks and / or determines and / or distributes the confidence level 11.

[0071] Here, it can be stipulated that the central server 3 repeats and / or periodically checks and / or determines and / or distributes the confidence level 11. In particular, it can be stipulated that the confidence levels 11 assigned to all the trajectories 10 stored in the memory 3-2 are periodically checked, and if necessary, the confidence level 11 is re-determined by a function according to the current values of the parameters or influencing factors.

[0072] Here, it can be stipulated that at least one receiving vehicle 50 can check and / or determine and / or distribute the confidence level 11. This is especially achieved by means of the control device 52. For this purpose, at least one vehicle 50, especially the control device 52, re-determines the confidence level 11 by means of a function through the current values of the parameters or influencing factors.

[0073] Here, it can be stipulated that the confidence level 11 is determined taking into account the age and / or the last use of at least one trajectory 10. Thus, the age and / or the last use especially form the parameters and / or influencing factors in the aforementioned function.

[0074] It can be stipulated here that the confidence level 11 is determined considering the robustness and / or stability of at least one trajectory 10. Thus, the robustness and / or stability particularly form parameters and / or influencing factors in the aforementioned function.

[0075] It can be stipulated here that the confidence level 11 is determined considering the environment in which at least one trajectory 10 is located. The characteristics and / or features of the environment particularly form one or more parameters and / or one or more influencing factors in the aforementioned function.

[0076] It can be stipulated here that the confidence level 11 is determined considering the similarity between the characteristics of the vehicle 50 (by which or for which at least one trajectory 10 is generated) and the characteristics of the receiving vehicle 50. The similarity between the characteristics and / or features of the vehicle 50 particularly forms one or more parameters and / or one or more influencing factors in the above function.

[0077] It can be stipulated here that the confidence level 11 is determined and / or assigned separately for multiple sections of the trajectory 10. For this purpose, the trajectory 10 can particularly be divided into multiple sections, and then a confidence level 11 is assigned to each section. This can be done by the device 2, particularly the central server 3 and / or the vehicle 50.

[0078] It can be stipulated here that a section of at least one trajectory 10 is discarded by the receiving vehicle 50 if the confidence level 11 assigned to the section is below a preset threshold. If the confidence level 11 assigned to the trajectory is below the preset threshold, it is particularly also possible to discard the entire trajectory 10. The receiving vehicle 50 thus does not use this section or the entire trajectory of the trajectory 10.

[0079] It can be stipulated here that the at least one vehicle 50 determines the degree of automation for traveling through the at least one trajectory 10 based on the confidence level 11 of the at least one trajectory 10. For example, the control device 52 can compare the confidence level 11 with a preset threshold for this purpose and enable or disable a degree of automation according to the result. The resulting control signal can be input into the vehicle control system of the vehicle 50.

[0080] It can be stipulated here that the at least one vehicle generates and provides feedback 12 on the use of at least one trajectory 10 during and / or after traveling through the at least one trajectory. The feedback 12 is particularly transmitted to the device 2, particularly the central server 3.

[0081] It can be provided here that when the confidence level 11 of at least one received trajectory 10 is lower than a preset threshold, the at least one vehicle 50 is retrained and at least one trajectory 10 is provided. If the control device 52 determines, for example, that the confidence level 11 of the trajectory 10 transmitted by the device 2, in particular the central server 3, is lower than the preset threshold, the training of the trajectory 10 can be started, for example, by detecting environmental data and / or control data of the path corresponding to the trajectory 10 during the vehicle 50 being driven manually by the driver through this path. The trajectory 10 trained in this way can then be transmitted to the device 2, in particular the central server 3, as part of the provision.

[0082] Figure 2 A schematic flow chart is shown for illustrating an embodiment of the method. Here, the method is executed in a vehicle.

[0083] In step 100, a vehicle trains a trajectory, in particular by detecting and recording sensor data and / or control data and / or metadata describing the trajectory. The training is carried out, for example, by a device of the vehicle, in particular by a control device provided therefor. For example, the trajectory can be a trajectory including a path for parking in a parking lot.

[0084] In step 101, boundary conditions corresponding to the recorded trajectory are determined, such as vehicle characteristics, the quality and / or age of the sensor system, environmental conditions (backlight, brightness), etc. For example, the control device can query this information in the vehicle control system, and / or determine this information from the detected sensor data, and / or query this information from a third-party provider (such as a weather service).

[0085] In step 102, the recorded trajectory is stored in the memory of the vehicle. Here, a confidence level is assigned to the trajectory. The confidence level is determined, for example, by the control device according to the aforementioned parameters and assigned to the trajectory. Since the trajectory is directly recorded within the same vehicle, the confidence level is usually high.

[0086] In step 103, the vehicle can reuse the recorded trajectory, which is achieved taking into account the assigned confidence level.

[0087] It can be provided in step 102a that the recorded trajectory is transmitted to the central server (backend).

[0088] Figure 3 A schematic flow chart is shown for illustrating an embodiment of the method. The method is executed in the central server.

[0089] In measure 200, the central server receives a trajectory. For example, the trajectory can be a detected or trained trajectory of a vehicle, as referenced Figure 2 as described. Alternatively, the trajectory can also come from other sources. For example, the trajectory can have been generated as part of a simulation or provided by an infrastructure operator (such as a parking garage operator).

[0090] In measure 201, the confidence of the trajectory is checked and / or determined and / or assigned. If no confidence has been assigned to the trajectory, it can be assigned for the first time. Otherwise, the confidence can be checked and adjusted if necessary.

[0091] In measure 202, the received trajectory is stored in the memory of the central server together with the checked and / or determined and / or assigned confidence. Based on this (and of other trajectories stored in the memory in the same way), the central server can provide at least one trajectory to the vehicle and, jointly, the confidence assigned to at least one trajectory.

[0092] In parallel measure 300, the confidence of all trajectories, especially those stored in the memory of the central server, is checked. It can be provided in particular that the confidence is reduced or increased taking into account the age and / or last use of the observed trajectory. Other influencing factors have been described in the general description. It can be provided in particular that the feedback of the vehicles that have traveled through the trajectory to be checked needs to be considered. This is done especially regularly. For this purpose, it can be provided in measure 301 that the time elapsed since the last check is compared with a preset value. If the preset value is reached, measure 300 is executed again.

[0093] Figure 4 A schematic flow chart is shown for illustrating an embodiment of the described method.

[0094] In measure 400, the vehicle requests a trajectory from the central server. An exemplary application scenario is parking a vehicle in a parking space in a parking garage or underground parking lot, where a suitable path must be provided to drive the vehicle from the starting position into the parking space. For example, the vehicle can request the central server of the parking garage operator to provide a trajectory. For this purpose, the vehicle transmits in particular its current position.

[0095] In measure 401, the central server (in the background) searches the memory for a suitable trajectory based on the transmitted vehicle position. Other information can also be taken into account here. In particular, occupancy information about the parking space is to be considered, so that the transmitted trajectory has the target position of an available parking space.

[0096] In measure 402, the vehicle receives the trajectory from the central server.

[0097] In measure 403, the vehicle uses the trajectory considering the assigned confidence level. If the confidence value is high, for example, it drives through the trajectory using an SAE 3+ level (or level 4) function. If the confidence value is low, it only drives through the trajectory using an SAE 2 level function. If the confidence value is very low, the vehicle can, for example, propose to the central server to retrain the trajectory.

[0098] In measure 404, the vehicle generates and provides feedback on the use of at least one trajectory during and / or after driving through at least one trajectory. In particular, it can be stipulated that the feedback on the trajectory is transmitted to the central server. The feedback can in particular include current information on the environment (such as vegetation, construction sites, other vehicles, etc.) and information on the deviation from the nominal path of the trajectory. In addition, the feedback can include newly detected trajectory data, such as the actual path traveled or information on the deviation between the nominal path of the trajectory and the actual path traveled.

[0099] In measure 405, the central server receives the feedback transmitted by the vehicle and rechecks and / or determines the confidence level of the trajectory, and assigns the confidence level to the trajectory. In particular, the characteristics of the vehicle, such as the age of the vehicle, the sensor system, and / or the environmental conditions, are also taken into account here.

[0100] Here, it can be stipulated that measures 400 to 405 are carried out for a plurality of vehicles, especially for a fleet of vehicles.

[0101] Figure 5 A schematic flowchart showing an embodiment of the method is presented.

[0102] In measure 500, the vehicle receives a trajectory from another source (such as another vehicle).

[0103] In measure 501, the vehicle determines the confidence level of the received trajectory and assigns the determined confidence level to the received trajectory. In this process, the vehicle can, for example, consider a list of known sources. For example, the list includes sources with corresponding assignments of original confidence values, and the original confidence values can be assigned to one of the trajectories from these sources respectively. Existing test certificates and / or the relationship with the providing source (for example, if the providing source is from the family or relatives of the vehicle driver) can also be considered. The similarity between the characteristics of the vehicle (for which at least one trajectory has been generated by or for the vehicle) and the receiving vehicle can also be considered here. If the information is available, the age of the trajectories from other sources can also be considered.

[0104] In measure 502, the vehicle uses the trajectory taking into account the assigned confidence level. If the confidence level value is high, for example, SAE level 3+ functions are used to drive through the trajectory. If the confidence level value is low, for example, only SAE level 2 functions are used to drive through the trajectory. If the confidence level value is very low, the vehicle can, for example, propose to the central server to retrain the trajectory.

[0105] In measure 503, the vehicle generates and provides feedback on the use of at least one trajectory during and / or after driving through at least one trajectory. In particular, it can be stipulated that the feedback on the trajectory is transmitted to the central server. The feedback can in particular include current information on the environment (such as vegetation, construction sites, other vehicles, etc.) and information on the deviation from the nominal path of the trajectory. In addition, the feedback can include newly detected trajectory data, such as the actual path traveled or information on the deviation between the nominal path of the trajectory and the actual path traveled. If the trajectory has not yet been stored in the memory of the central server, the trajectory can also be transmitted jointly.

[0106] In measure 504, the central server receives the feedback (possibly also the trajectory) transmitted by the vehicle and rechecks and / or determines the confidence level of the trajectory and assigns the confidence level to the trajectory. In particular, the characteristics of the vehicle, such as the age of the vehicle, the sensor system and / or the environmental conditions, are also taken into account here.

[0107] List of reference signs

[0108] 1 System

[0109] 2 Device

[0110] 3 Central server

[0111] 3-1 Computing device

[0112] 3-2 Memory

[0113] 4 Communication device

[0114] 10 Trajectory

[0115] 11 Confidence level

[0116] 12 Feedback

[0117] 50 Vehicle

[0118] 51 Control device

[0119] 52 Communication device

[0120] 60 Other sources

[0121] 100 - 103 Measures of the method

[0122] Measures of the 200 - 202 method

[0123] Measures of the 300 - 301 method

[0124] Measures of the 400 - 405 method

[0125] Measures of the 500 - 504 method

Claims

1. A method for providing a trajectory (10) for at least one vehicle (50), Among them, providing at least one trajectory (10) generated by a vehicle (50) or other sources (60), wherein the at least one trajectory (10) is assigned or can be assigned a confidence level (11), wherein the provided at least one trajectory (10) is received by at least one vehicle (50), and wherein the at least one trajectory (10) is used taking into account the assigned confidence level (11).

2. The method according to claim 1, wherein, The at least one trajectory (10) is provided by means of a central server (3).

3. The method according to claim 2, wherein The central server (3) checks and / or determines and / or assigns the confidence level (11).

4. The method according to claim 3, wherein The central server (3) repeatedly and / or periodically checks and / or determines and / or assigns the confidence level (11).

5. The method according to any one of the preceding claims, characterized in that, The at least one vehicle (50) that is receiving checks and / or determines and / or assigns the confidence level (11).

6. The method according to any one of the preceding claims, characterized in that, The confidence level (11) is determined taking into account the age and / or the last use of the at least one trajectory (10).

7. The method according to one of the preceding claims, characterized in that, The at least one vehicle (50) determines the degree of automation for driving through the at least one trajectory (10) based on the confidence level (11) of the at least one trajectory (10).

8. The method according to one of the preceding claims, characterized in that, The at least one vehicle (50) generates and provides feedback (12) on the use of the at least one trajectory (10) during and / or after driving through the at least one trajectory (10).

9. The method according to one of the preceding claims, characterized in that, When the confidence level (11) of the received at least one trajectory (10) is below a preset threshold, the at least one vehicle (50) retrains and provides the at least one trajectory (10).

10. A system (1) for providing a trajectory (10) for at least one vehicle (50), comprising: At least one device (2), said device being adapted to provide at least one track (10) generated by a vehicle (50) or other source (60), wherein, the at least one trajectory (10) is assigned or can be assigned a confidence level (11), and at least one vehicle (50), wherein the at least one vehicle (50) is configured to receive the provided at least one trajectory (10) and use the at least one trajectory (10) taking into account the assigned confidence level (11).

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