A method for predicting the availability of a vehicle's location based on indicators, and a method for controlling a vehicle.

Abstract

The present invention relates to a method (100) for predicting availability of landmark-based localization of a vehicle (401), the method (100) comprising: receiving (101) map data (412) of a landmark map (411) of a road traffic network; determining (103) a landmark (413) for at least one roadway (403) to be traveled by the vehicle (401) based on landmark information of the landmark map (411); calculating (105) an availability value of the landmark (413) of the at least one roadway (403) to be traveled by the vehicle (401) with respect to an availability criterion; and outputting (107) availability information comprising an availability value for the landmark (413) of the roadway (403) to be traveled.

Description

Technical Field

[0001] The present invention relates to a method for predicting the availability of index-based positioning of a vehicle. Furthermore, the present invention relates to a method for route planning for an autonomously controllable vehicle. Furthermore, the present invention relates to a method for controlling a vehicle.

Background Art

[0002] Automated driving and highly automated driving are considered to be future trends. For the orientation of an autonomously controllable vehicle, it is of great importance to map the surrounding area of the vehicle. Vehicle positioning is achieved, in particular, by matching the indices of the corresponding index map with the objects arranged in the surrounding area of the vehicle, which are detected through the surrounding area sensors. If this type of positioning is not possible because the corresponding objects are not detected through the surrounding area sensors during driving, autonomous control cannot be performed. Therefore, for the autonomous control of a vehicle, it is crucial to select a route for which a reliable detection of a sufficient number of objects is expected for matching with the corresponding indices of the index map.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Therefore, the object of the present invention is to provide an improved method for predicting the availability of index-based positioning of a vehicle, an improved method for route planning for an autonomously controllable vehicle, and an improved method for controlling a vehicle.

Means for Solving the Problems

[0004] This problem is solved by the methods for predicting the availability of index-based positioning of a vehicle, for route planning for an autonomously controllable vehicle, and for controlling a vehicle, respectively, of the independent claims. Preferred embodiments are the subject matter of the dependent claims.

[0005] According to one aspect of the present invention, a method for predicting the availability of vehicle location based on indicators is provided. The process involves receiving map data for an indicator map of the road traffic network, wherein the indicator map includes indicator information for multiple roadways that can be traveled by vehicles, and the indicators are set up to be detected by vehicles traveling on the roadways through sensor data from sensors in the vehicle's surrounding area and used for determining the vehicle's position. The index is determined based on the index information of the index map for at least one roadway that should be traveled by vehicles, The calculation of the availability value of at least one roadway indicator to be traveled by a vehicle, with respect to the availability criterion, wherein the availability value represents a quantitative measure of the usefulness of the indicator for determining the vehicle's position when the roadway is traveled by a vehicle, To output availability information, including availability values, regarding indicators of roads that should be traveled on, Includes.

[0006] This provides a technical advantage in that it offers an improved method for predicting the availability of vehicle marker-based positioning. The method according to the present invention particularly serves to plan routes for vehicles, for example, autonomously controlled vehicles. That is, for a route to be traveled by a vehicle, an index on an index map is determined for each roadway of the route, and an availability value is calculated for each index. The availability value represents a quantitative indicator of the usefulness of the index for vehicle positioning when the selected roadway is traveled. An index on the index map is useful for vehicle positioning if the respective index can be detected through the vehicle's surrounding area sensors when the vehicle travels along each roadway of the selected route. In this case, the index on the index map corresponds to a corresponding object in the surrounding area. The object may be placed along each roadway to be traveled by the vehicle. The object may be placed, for example, on the edge of the roadway to be traveled. However, depending on the vehicle's position on the roadway, the type of surrounding area sensors, or the surrounding area conditions, such as weather conditions and traffic volume, it is not possible to identify all objects through the vehicle's surrounding area sensors at any given time or each time a vehicle passes over the roadway, and therefore, they cannot be used for vehicle positioning. After availability values ​​are calculated for multiple indicators on various roadways, corresponding availability information for the indicators on the roadway to be traveled is output. Using the output availability information, a suitable route can be selected based on the availability of indicators or objects placed along the individual roadways of the selected route. For example, when planning a route, in addition to the route with the shortest travel time, the driver can also be provided with the route with the highest availability of the indicators based on the availability information. In this case, availability is expressed as a percentage or probability value, indicating how many objects can be identified or how likely it is that an object will be detected.

[0007] According to one embodiment, the availability criterion includes the detectability of an indicator by a vehicle's surrounding area sensor based on the sensor type, and / or the directional characteristics of the vehicle's surrounding area sensor, and the availability value is calculated as follows: Determining the sensor type and / or directional characteristics of at least one peripheral area sensor of the vehicle, To determine the detectability of roadway indicators to be traveled by the vehicle for the sensor type and / or directional characteristics of at least one peripheral area sensor, Includes.

[0008] This provides a technical advantage: the ability to accurately calculate the availability of indicators stored in an indicator map for vehicle positioning. For this purpose, the availability criterion considered for calculating the availability value is defined as the detectability of indicators by the vehicle's surrounding area sensors as it travels along the roadway, based on the sensor type and / or directional characteristics of the vehicle's surrounding area sensors. The sensor type of the surrounding area sensors can include configurations such as camera sensors, radar sensors, and LiDAR sensors. In this invention, directional characteristics are understood to be the corresponding directional sensitivity of each surrounding area sensor. Thus, based on the sensor type and directional characteristics, the detectability of indicators positioned along the roadway to which the vehicle should travel can be calculated for each of the vehicle's surrounding area sensors. In this way, the detectability of an indicator by each surrounding area sensor may, on the one hand, depend on the relative position of the indicator to the vehicle, as each indicator may be reflected, partially or entirely, or not at all, by the directional characteristics of each surrounding area sensor. Similarly, the detectability of an indicator can be determined by the properties of the indicator itself, which means that depending on the indicator's reflectivity, it may be easily, poorly, or completely undetectable by various types of ambient area sensors. For example, an object that can be easily detected by a radar sensor may be almost undetectable by a camera sensor at night or under insufficient lighting, making it useless for determining the location of a vehicle.

[0009] According to one embodiment, the availability criterion includes the detectability of an indicator by a vehicle's surrounding area sensor, taking into account the traffic volume on the roadway to be traveled, and the availability value is calculated as follows: To determine the volume of traffic on the roadway that vehicles should travel on, By calculating the occlusion of roadway indicators due to roadway traffic volume, the detectability of roadway indicators that should be driven on by vehicles is determined, Includes.

[0010] This provides a technical advantage: it enables further improvements in the calculation of availability information for individual indicators on the indicator map. To this end, the availability criterion is calculated by the vehicle's surrounding area sensors, taking into account the traffic volume on the roadway that the vehicle is traveling on. Since individual indicators are typically located along the roadway that the vehicle is traveling on, the detectability of an indicator by a vehicle's surrounding area sensors can be significantly impaired by other road traffic vehicles located in the vehicle's surrounding area, in addition to the characteristics of each surrounding area sensor. When traffic volume is high and a correspondingly large number of other vehicles are located in the vehicle's surrounding area, each preventing the vehicle's surrounding area sensors from detecting indicators located at the edge of the roadway, a correspondingly low availability of each roadway indicator is achieved. This is because, due to other blocking vehicles, the indicator is not detectable, or cannot be detected, by the surrounding area sensors.

[0011] According to one embodiment, determining traffic volume includes determining the average number of vehicles per unit time and / or average speed on the roadway that vehicles are to travel on, and determining detectability includes, Calculating the average occlusion of a roadway indicator based on the average number of vehicles and / or speed per unit time, Includes.

[0012] This provides the technical advantage of accurately predicting traffic volume for each roadway that vehicles are to travel on. This, in turn, contributes to improving the determination of availability information for each roadway. Since the method according to the present invention is primarily intended for route planning, the traffic volume that will likely occur on each roadway at the time vehicles travel on it must be predicted in order to determine the availability of indicators for each roadway that vehicles will travel on in the future. By calculating the average traffic volume in this way, and based on this, the average occlusion of the traffic volume indicator by vehicles can be calculated, thereby enabling accurate and probabilistic predictions of the traffic volume that will occur at any point in the future. In this case, the average traffic volume can be calculated based on the average number of vehicles or the average speed on each roadway.

[0013] According to one embodiment, calculating the availability value involves determining at least one feature of the index, which includes the type of index, the number of indexes, the number of indexes of a particular type or category, the spread and / or size of the index, the reflectivity of the index; and determining the detectability is done with respect to at least one specific feature of the index, and the detectability-corrected feature is calculated.

[0014] This provides a technical advantage in that it enables greater accuracy in calculating availability values ​​or availability information. For example, the detectability of multiple indicators on each roadway can be further quantified through various features of the indicators, such as the type of indicator, the number of indicators, or the spatial extent of the indicators. These features can be extracted from the detected indicators, in particular by running a correspondingly trained artificial intelligence. According to the present invention, a feature is, in particular, a statistic that describes the set of indicators as a whole.

[0015] According to one embodiment, calculating the availability value includes comparing at least one feature of the map display indicator with a corresponding feature whose detectability has been corrected.

[0016] This provides a technical advantage: it enables greater accuracy in calculating availability values. To achieve this, the characteristics of the indicators in the indicator map are compared with the characteristics of the corresponding indicators, which have been corrected for detectability. For example, the number of indicators in the indicator map can be compared to, or related to, the number of indicators predicted for a given traffic volume, which has been appropriately reduced. Due to the corresponding traffic volume and other vehicles positioned in the vehicle's surrounding area at any given time, it is not possible for a vehicle to detect all indicators on the roadway. In this way, a quantitative value for the availability of each indicator in the indicator map can be calculated by comparing the indicators in the indicator map with the number of indicators detectable by the vehicle's surrounding area sensors, calculated for each traffic volume at any given time.

[0017] According to one embodiment, a determination of detectability is made for at least one known position of a vehicle on a roadway, or for various known positions of a vehicle on various driving lanes of a roadway.

[0018] This enables the technical advantage of determining the location-dependent detectability of roadway indicators each time they are to be traveled. Considering location allows for greater accuracy in determining the availability of indicators on the indicator map. Considering the various lanes of the roadway to be traveled further enhances the accuracy of availability determination.

[0019] According to one embodiment, the determination of detectability is performed by a corresponding trained artificial intelligence, which is trained with peripheral sensor data from at least one vehicle's peripheral sensor, and the peripheral sensor data is recorded under multiple different traffic volumes during multiple runs of the vehicle along at least one roadway.

[0020] This provides the technical advantage of enabling accurate, rapid, and reliable determination of detectability.

[0021] According to one embodiment, the indicator is a characteristic object arranged at the edge of the roadway, including buildings, traffic signs, and lane markings.

[0022] Thereby, it is possible to realize the technical advantage that comprehensive consideration of various indicators becomes possible. In particular, the characteristic object is provided by any object detectable by radar, lidar, ultrasonic, and video that is suitable for positioning, including direction arrows, driving lane markings, especially broken lines, trees, other plants, bridges, scattered structures such as poles and posts; members for separating roadways: paving stones, guardrails, and vehicle stops. The enumeration here should not be understood as exhaustive.

[0023] In addition, indicators that cannot be semantically named but can be detected and utilized by trained artificial intelligence for correspondence from the raw data of the surrounding area sensors can be detected. The artificial intelligence usually recognizes or detects key points in the surrounding area sensor data and assigns a vector representing the type of this point to it. Although it is not understood by the operator of the artificial intelligence and cannot be semantically named, such key points that are surely detected by the corresponding artificial intelligence can also be used as characteristic objects in the meaning of the present invention.

[0024] According to another aspect, a method for route planning for an autonomously controllable vehicle is provided, Regarding a plurality of possible routes of the vehicle between a predetermined starting point and a predetermined ending point, executing a method for predicting the availability of vehicle indicator-based positioning according to one of the above embodiments, Based on the executed availability prediction, determining the route of the vehicle with the highest predicted availability of vehicle indicator-based positioning, including.

[0025] This provides the technical advantage of improved route planning for autonomously controllable vehicles, which offers not only the fastest route but also a route that offers the best availability of the indicators necessary for positioning, and therefore the highest probability of fully autonomous vehicle control.

[0026] In another embodiment, a method for controlling a vehicle is provided, and this method is: To implement a method for route planning for autonomously controlled vehicles, Driving the vehicle based on the selected route, Includes.

[0027] This enables the technical advantage of improved control of autonomously controllable vehicles, which relies on improved route planning and improved availability prediction, both of which offer the technical advantages mentioned above.

[0028] According to one embodiment, driving a vehicle includes executing an operation plan, which includes driving operations of the vehicle that enable the best availability of indicators on an indicator map.

[0029] This enables the realization of technical advantages such as improved vehicle control. In particular, the vehicle can perform driving operations that optimize availability based on the results of availability determination. For example, the driving operation may intend to change the driving lane on the roadway if differences in the availability of indicators are determined for different driving lanes of the roadway.

[0030] In another embodiment, a computing unit is provided configured to perform a method for predicting the availability of an indicator-based location of a vehicle, and / or a method for planning a route for an autonomously controllable vehicle, and / or a method for controlling a vehicle, according to one of the embodiments described above.

[0031] In another embodiment, a computer program product is provided which, when the program is executed by a data processing unit, includes commands that cause the unit to perform a method for predicting the availability of indicator-based location of a vehicle, and / or a method for planning a route for an autonomously controllable vehicle, and / or a method for controlling a vehicle, according to one of the embodiments described above.

[0032] Embodiments of the present invention will be described with reference to the following drawings. The drawings show the following: [Brief explanation of the drawing]

[0033] [Figure 1] This is a schematic diagram showing a system for controlling a vehicle. [Figure 2] This is a flowchart of a method for predicting the availability of vehicle location based on indicators, according to one embodiment. [Figure 3] This is a flowchart illustrating a method for route planning for an autonomously controllable vehicle according to one embodiment. [Figure 4] This is a flowchart of a method for controlling a vehicle according to one embodiment. [Figure 5] This is a schematic diagram illustrating a computer program product for performing indicator-based location availability predictions for vehicles, and / or route planning for autonomously controllable vehicles, and / or controlling vehicles. [Modes for carrying out the invention]

[0034] Figure 1 shows a schematic diagram of the system 400 for controlling vehicle 401.

[0035] Image A in Figure 1 shows a vehicle 401 traveling on a roadway 403. Various indicators 413 are arranged along the roadway 403. The indicators 413 are composed of various objects, such as buildings, plants, and traffic signs, which enable the direction of the vehicle 401. Furthermore, Image A shows an indicator map 411. The indicator map 411 shows the illustrated roadway 403 with the correspondingly arranged indicators 413. Furthermore, Image A shows a computing unit 415 on which artificial intelligence 419 is installed.

[0036] Here, the calculation unit 415 is configured to carry out the method of the present invention for predicting the availability of indicator-based location determination of the vehicle 401.

[0037] Furthermore, the vehicle 401 has at least one peripheral area sensor 405, which enables peripheral area recognition of the area surrounding the vehicle 401. In addition, the vehicle 401 has a computing unit 417. The computing unit 417 may be configured, for example, for autonomous or semi-automated control of the vehicle.

[0038] The computing unit 415 may be configured, for example, as an external server unit or as an external cloud server.

[0039] To carry out the method of the present invention for predicting the availability of indicator-based location determination of a vehicle 401, the computing unit 415 first receives an indicator map 411 in the form of corresponding map data 412. The computing unit 415 is further provided with sensor information data 407 of the vehicle 401's surrounding area sensors 405. The sensor information data 407 may include, for example, information about the sensor type of the surrounding area sensors or the directional characteristics of each of the vehicle 401's surrounding area sensors 405. The computing unit 415 may also be provided with traffic volume 409 data relating to the roadway 403. The traffic volume 409 data may be the most recent traffic volume data at a given point in time for one particular roadway or for several different roadways. Alternatively, the data may cover past points in time. Alternatively, the data may represent average traffic volume. The data 409 may transmit, for example, information about other vehicles that traveled on each roadway at a given point in time. Alternatively, the data 409 may be archived data. The data 409 may also include the number of vehicles and / or their speeds.

[0040] To calculate an indicator-based availability prediction for the location of vehicle 401, the calculation unit 415 calculates an availability value for the roadway 403 that vehicle 401 should travel on, based on the map data 412 of the indicator map 411 for indicators 413 located along the roadway 403, taking availability criteria into consideration.

[0041] According to one embodiment, the availability criterion for each indicator 413 can take into account the detectability of the indicator 413 by the surrounding area sensor 405 of each vehicle 401. For this purpose, information from sensor information data 407 relating to the sensor type and / or directional characteristics 410 of each peripheral area sensor 405 can be taken into consideration. In this way, based on the index information of the index map 411, for each index 413 of the index map 411 arranged along the roadway 403 to be traveled by the vehicle 401, the corresponding detectability of each index 413 by the peripheral area sensors 405 can be calculated according to the respective sensor type or directional characteristics 410 of each peripheral area sensor 405 of the vehicle 401. Here, detectability represents a quantifiable amount that each index 413 can be detected by the peripheral area sensors 405 of the vehicle 401. Such detectability may depend, for example, on the properties of each index 413 and / or the properties of the peripheral area sensor. For example, an index 413 may have a favorable reflectivity for a LiDAR sensor or radar sensor, but may be difficult for a camera sensor to recognize, for example, due to low ambient light.

[0042] Alternatively or as an addition, the detectability of indicator 413 can be calculated taking into account the expected traffic volume on each roadway 403. Based on the corresponding data 409 of the expected current or average traffic volume on each roadway 403, the traffic volume at the time when vehicle 401 travels on the corresponding roadway 403 can be calculated. For the calculation, for example, the expected average speed or average number of vehicles on roadway 403 at a future point in time can be used to calculate the average occlusion. In this case, the average occlusion represents the occlusion of indicator 413 positioned at the edge of roadway 403 by other vehicles that, on average, are positioned on roadway 403 when vehicle 401 is traveling on it, based on the traffic volume in the area surrounding vehicle 401.

[0043] Figure B illustrates this type of calculation of the detectability of individual indicators 413 for a peripheral area sensor 405 of a vehicle 401 traveling on a roadway 403. In the illustrated figure, vehicle 401 is located in the left lane 404 of the roadway 403. Further, three other vehicles with expected traffic volume 409 are shown in the right lane 404 of the roadway 403. Figure B also shows the three directional characteristics 410 of the three different peripheral area sensors 405 of vehicle 401. Here, one directional characteristic 410 is directed towards the area in front of vehicle 401, while the other two directional characteristics 410 are directed towards the area behind vehicle 401. In the illustrated figure, indicator 413 located on the right edge of the roadway 403 is occluded from the peripheral area sensor 405 of vehicle 401 by the other vehicles with traffic volume 409. This is illustrated in Figure B by the average occlusion 414. The average occlusion 414 may be expressed, for example, as a percentage. Of the six indicators 413 located on the left edge of the roadway 403, only five indicators 413 are within the region of the directional characteristics 410 in the illustrated diagram. That is, the indicators 413 that are not occluded by the directional characteristics 410 cannot be detected by the vehicle 401 at the illustrated position P. According to the present invention, the availability of the indicators 413 of the indicator map 411 can be calculated for each position for various positions P of the vehicle 401 on the roadway 403. That is, in Figure B, for the position P of the vehicle 401, only five of the six indicators 413 on the left edge of the roadway 403 can be detected by the vehicle 401's surrounding area sensor 405.

[0044] According to one embodiment, the characteristics of an indicator can be determined for the calculation of the availability values ​​of various indicators 413. The characteristics may include, for example, the type of indicator 413, the number of indicators 413 in the area of ​​the roadway 403 to be traveled, the spatial extent or size of the indicator 413, or the reflectivity of the indicator 413. To determine detectability, these characteristics of the indicator 413 can be taken into account by calculating the corresponding detectability-corrected characteristics. In the example of the number of indicators 413 as a characteristic, the detectability-corrected number of indicators 413 can be calculated as the number of indicators 413 detectable by the surrounding area sensor 405 for each sensor type or directional characteristic 410 of the surrounding area sensor 405, or for the calculated and predicted traffic volume 409.

[0045] In this way, the availability values ​​calculated for each indicator 413 for the selected roadway 403 can be expressed, for example, as a percentage of the number of indicators 413 for the roadway 403. That is, the corresponding availability information can indicate that for each roadway 403, a certain percentage of the indicators 413 on the indicator map 411 are available for the location of the vehicle 401.

[0046] In this way, for appropriate route planning for vehicle 401, in addition to the calculated route with the shortest travel time, the route with the highest availability of the calculated route indicator 413 can also be provided to vehicle 401 or its driver, based on the calculated availability information of various roadways 403 along each route.

[0047] The corresponding route data 421 can be provided in each vehicle 401 by a computing unit 415 configured as an external server unit.

[0048] In this way, the external server unit 415 is configured to calculate the availability of indicators 413 or objects placed on each roadway 403 for the vehicle 401 and the corresponding route or roadway 403 to be traveled, and to determine the corresponding route with the maximum availability of indicators 413. This information can be provided to the vehicle 401 that communicates with the external server unit 415.

[0049] Thus, according to the present invention, for route calculation, a data connection can be achieved between a computing unit 415, which is configured as an external server unit, and a vehicle 401, for example, in the form of a wireless data connection. At this time, sensor information data 407 from the surrounding area sensors 405 of the vehicle 401 can be transmitted to the computing unit 415. In addition to data 409 related to average traffic volume, the computing unit 415 or the external server unit can calculate the corresponding route with maximum availability using the availability prediction method and route planning method of the present invention and provide it to the vehicle 401 via wireless communication.

[0050] Figure 2 shows a flowchart of a method 100 for predicting the availability of an indicator-based location of a vehicle 401, according to one embodiment.

[0051] According to the present invention, in the first method step 101, map data 412 of an indicator map 411 of a road traffic network is received, and the indicator map 411 includes indicator information for a plurality of indicators 413 of a plurality of roadways 403 that can be traveled on by a vehicle 401, and the indicators 413 are set to be detected by a vehicle 401 traveling on a roadway 403 through sensor data 406 of a surrounding area sensor 405 of the vehicle 401 and used for determining the location of the vehicle 401.

[0052] In the next step 103, based on the index information of the index map 411, an index 413 is determined for at least one roadway 403 that should be traveled by the vehicle 401.

[0053] In the next step 105 of the method, the availability value of the indicator 413 of at least one roadway 403 that the vehicle 401 is to travel on is calculated with respect to the availability criterion, the availability value representing a quantitative measure of the usefulness of the indicator 413 for locating the vehicle 401 when the roadway 403 is traveled on by the vehicle 401.

[0054] For this purpose, in the illustrated embodiment, the sensor type and / or directional characteristics 410 of the peripheral area sensor 405 of the vehicle 401 are determined in the following method step 109.

[0055] Furthermore, traffic volume is determined in another method, step 113.

[0056] For this purpose, in step 115 of the method, the average number of vehicles and / or average speed of vehicles on the roadway 403 that the vehicles should travel on is determined.

[0057] In the next step 117, the average occlusion 414 of the vehicle-based indicator 413 is calculated based on the average number of vehicles or average speed.

[0058] Furthermore, the characteristics of the indicator 413 are determined in method step 119. These characteristics may include, for example, the number of indicators, their properties, or their spatial extent.

[0059] In step 111 of the method, the detectability of individual indicators by the surrounding area sensors 405 of the vehicle 401 is calculated based on the sensor type or directional characteristics and / or based on the calculated traffic volume or average occlusion.

[0060] In the next method step, at least one feature of index 413 is compared to a feature whose detectability was corrected in method step 119. The comparison can provide availability values ​​in quantitative numerical form for various features. This may be expressed, for example, as a percentage.

[0061] In the next method step 107, availability information based on the availability value calculated in method step 105 is output for index 413 of index map 411 for the roadway 403 that the vehicle 401 is to travel on.

[0062] Figure 3 shows a flowchart of a method 200 for route planning for an autonomously controllable vehicle 401 according to one embodiment.

[0063] According to the present invention, first, in method step 201, a method 100 for predicting the availability of indicator-based location of a vehicle 401 is performed for a number of possible routes of the vehicle 401 between a predetermined starting point and a predetermined ending point.

[0064] In the next step 203 of the method, based on the availability predictions performed, the route 421 of vehicle 401 with the highest predicted availability based on the indicator-based location of vehicle 401 is determined.

[0065] In one embodiment, driving the vehicle 401 may include the execution of an operation plan. In particular, the vehicle 401 may plan and execute driving operations that improve the availability of indicators on the indicator map for the roadway on which the vehicle 401 is to travel. For example, such driving operations may include changing to a specific lane on the roadway on which the vehicle is to travel, for which higher availability of indicators is expected. Alternatively, the driving may include other driving operations that can improve the availability of indicators, such as reducing the planned speed or activating the vehicle's headlights, thereby making objects located along the lane on which the vehicle is to travel more visible to the vehicle's surrounding area sensors.

[0066] Figure 4 shows a flowchart of a method 300 for controlling a vehicle 401 according to one embodiment.

[0067] According to the present invention, first, in method step 301, a method 200 for planning a route for an autonomously controllable vehicle is performed.

[0068] In the next step 303, the vehicle 401 is controlled based on the selected route.

[0069] Figure 5 shows a schematic diagram of a computer program product 500, which includes commands that cause a computing unit to perform a method 100 for predicting the availability of an indicator-based location of a vehicle 401 and / or a method for planning a route for an autonomously controllable vehicle 401 and / or a method for controlling the vehicle 401 when the program is executed.

[0070] In the illustrated embodiment, the computer program product 500 is stored in a storage medium 501. In this case, the storage medium 501 may be any storage medium known from the prior art.

Claims

1. A computer implementation method (100) for predicting the availability of location identification of a vehicle (401) based on indicators, by a computer, Step (101) of receiving map data (412) of an indicator map (411) of a road traffic network, wherein the indicator map (411) includes indicator information for a plurality of indicators (413) of a plurality of roadways (403) that can be traveled by a vehicle (401), and the indicators (413) are configured to be detected by a vehicle (401) traveling on the roadway (403) through sensor data (406) of a surrounding area sensor (405) of the vehicle (401), and used for determining the location of the vehicle, Step (103) of determining an index (413) based on the index information of the index map (411) for at least one roadway (403) that the vehicle (401) is to travel on, Step (105) of calculating the availability value for the indicator (413) of the at least one roadway (403) on which the vehicle (401) is to travel, with respect to an availability criterion, wherein the availability value represents a quantitative measure of the usefulness of the indicator (413) for locating the vehicle (401) when the vehicle (401) is traveling on the roadway (403), Step (107) of outputting availability information including the availability value related to the indicator (413) of the roadway (403) to be traveled on, Includes, The availability criterion includes the detectability of the index (413) by the surrounding area sensor (405) of the vehicle (401) based on the sensor type and / or directional characteristics (410) of the surrounding area sensor (405) of the vehicle (401), and the step (105) of calculating the availability value is, Step (109) of determining the sensor type and / or directional characteristics (410) of at least one peripheral area sensor (405) of the vehicle (401), Step (111) of determining the detectability of the indicator (413) of the roadway (403) on which the vehicle (401) is to travel, with respect to the sensor type and / or directional characteristics (410) of the at least one peripheral area sensor (405), Includes, The step of determining the detectability (111) is performed by the corresponding trained artificial intelligence (419), A computer implementation method wherein the artificial intelligence (419) is trained with peripheral area sensor data (406) from the peripheral area sensor (403) of at least one vehicle (401), and the peripheral area sensor data (406) is recorded under multiple different traffic volumes (409) during multiple runs of the vehicle (401) along the at least one roadway (403).

2. The usability criterion includes the detectability of the indicator by the vehicle's surrounding area sensors, taking into account the traffic volume on the roadway to be traveled, and the step (105) of calculating the usability value is, The steps include: (113) determining the traffic volume on the roadway to be traveled by the vehicle, (111) A step of determining the detectability of the indicator on the roadway to be traveled by the vehicle by calculating the coverage of the indicator on the roadway based on the traffic volume on the roadway, The computer implementation method (100) according to claim 1, including the method described in claim 1.

3. The step (113) of determining the traffic volume (409) is, The step (115) includes determining the average number of vehicles per unit time and / or average speed on the roadway to which the vehicles are to travel, wherein the step (111) determines the detectability, Step (117) of calculating the average coverage (414) of the indicator (413) of the roadway based on the average number of vehicles and / or speed per unit time, The computer implementation method (100) according to claim 2, including the method described in claim 2.

4. The step (105) of calculating the availability value is The computer implementation method (100) according to claim 1, comprising the step (119) of determining at least one feature of the index (413), wherein the at least one feature includes the type of the index, the number of the index, the number of the index of a particular type or category, the recognition level of the index, the spread and / or size of the index, and the reflectivity of the index (413), and the step (111) of determining detectability is performed with respect to at least one particular feature of the index, and a detectability-corrected feature is calculated.

5. The step (105) of calculating the availability value is The computer implementation method (100) according to claim 4, comprising the step (121) of comparing the at least one feature in the index (413) of the index map (411) with the corresponding feature whose detectability has been corrected.

6. The computer implementation method (100) according to claim 1, wherein the step of determining detectability (111) is performed for at least one known position (P) of the vehicle (401) on the roadway (403) or for different known positions of the vehicle (401) on different driving lanes (404) of the roadway (403).

7. The computer implementation method (100) according to claim 1, wherein the indicator (413) is a distinctive object placed on the edge of the roadway (403), and includes buildings, traffic signs, and roadway markings.

8. A computer implementation method (200) for route planning for an autonomously controllable vehicle (401), wherein a computer performs the following: Step (201) of performing the computer implementation method (100) for predicting the availability of indicator-based location identification of the vehicle (401) according to claim 1, with respect to a plurality of possible routes of the vehicle (401) between a predetermined starting point and a predetermined ending point, Step (203) of determining the route (421) of the vehicle (401) that has the highest predicted availability for the indicator-based location of the vehicle (401), based on the availability prediction performed above, Computer implementation method (200), including.

9. A computer implementation method (300) for controlling a vehicle, wherein a computer enables, Step (301) of implementing the computer implementation method (200) for planning a route for an autonomously controllable vehicle as described in claim 8, Step (303) of controlling the vehicle (401) based on the determined route, A computer implementation method (300), including the above.

10. The computer implementation method (300) according to claim 9, wherein the step of controlling the vehicle includes the step of executing an operation plan, the operation plan includes driving operations of the vehicle that enable the best availability of the indicators of the indicator map.

11. Computing units (415, 417) configured to perform the computer implementation method (100) for predicting the availability of an indicator-based location of a vehicle according to any one of claims 1 to 7, and / or the computer implementation method (200) for planning a route for an autonomously controllable vehicle according to claim 8, and / or the computer implementation method (300) for controlling a vehicle according to claim 9 or 10.

12. A computer program that, when executed by a data processing unit, includes instructions to cause the unit to perform the computer implementation method (100) for predicting the availability of indicator-based location identification of a vehicle according to any one of claims 1 to 7, and / or the computer implementation method (200) for planning a route for an autonomously controllable vehicle according to claim 8, and / or the computer implementation method (300) for controlling a vehicle according to claim 9 or 10.

Images