Control support method for vehicle

Abstract

To provide an improved control support method for a vehicle.SOLUTION: A control support method for a vehicle comprises steps of: receiving GNSS signals of at least one positioning satellite; determining quality parameters of the GNSS signals that represent receiving qualities of the received GNSS signals; determining a traveling state of a vehicle by using the quality parameters of the GNSS signals as a base, with comparisons between values of the quality parameters of the received GNSS signals and a known reference value cluster which includes a plurality of reference values regarding the individual quality parameters of the GNSS signals, allowing each reference value cluster to represent one known traveling state, and allowing each known traveling state to represent one state of the vehicle that affects signal transmission of the GNSS signals; and providing a control support function by using the determined traveling state as a base.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for assisting vehicle control.

Background Art

[0002] For very accurate vehicle position determination, signals from the Global Navigation Satellite System (GNSS) can be used. Usually, for further improving position estimation, relative systems such as wheel speed sensors or inertial measurement units are integrated with this global system. GNSS signals and their reception can be affected by various environmental factors. This GNSS signal, if untreated, can lead to too crude position estimation of the vehicle. Many of the factors affecting signal quality are well understood and can be corrected by appropriate models such as ionospheric models and tropospheric models as well as correction data, whereby position accuracy up to a few decimeters can be accurately estimated. In the case of position determination supported by the Global Navigation Satellite System (GNSS), the actual problem lies in correcting or at least reliably detecting unforeseen disturbing effects on the GNSS signal. Since the data volume and diversity of the received GNSS input signal and relative sensor signals are large, a reliable assertion about the quality of the position solution is very challenging. [[ID=十三]]

Summary of the Invention

Problems to be Solved by the Invention

[0003] Therefore, an object of the present invention is to provide an improved method for assisting vehicle control. [[ID=二十四]]

Means for Solving the Problems

[0004] This problem is solved by the method for assisting vehicle control according to independent claim 1. Advantageous forms are the subject of the dependent claims. Based on one aspect of the present invention, a method for assisting vehicle control, comprising: receiving GNSS signals from at least one positioning satellite of the Global Navigation Satellite System; The steps include determining the quality parameters of the GNSS signal, and these quality parameters representing the reception quality of the received GNSS signal, The process involves determining the vehicle's driving state based on the GNSS signal quality parameters by comparing quality parameters with known reference value clusters, where each reference value cluster contains multiple reference values ​​for each quality parameter of the GNSS signal, each reference value cluster represents one known driving state, and each known driving state represents one state of the vehicle that affects the transmission of the GNSS signal. A step of providing control support functions based on a determined driving state and A method including this is provided.

[0005] This can achieve the technical advantage of providing an improved control assistance method for vehicles. By considering the quality parameters of the received GNSS signal, and comparing the corresponding values ​​of the quality parameters of the received GNSS signal with the reference values ​​of each quality parameter, the driving state in which each vehicle is located at the time the GNSS signal is received can be determined. Subsequently, based on the driving state thus determined, control assistance functions that take each driving state into account can be provided. By comparing the moment-in-time values ​​of the quality parameters of the received GNSS signal with predetermined known reference values, technically simple and precise identification of each driving state can be achieved.

[0006] According to one embodiment, the determination of the driving state is A step of determining, based on an interval measure, the intervals between the quality parameters of the received GNSS signal and the reference values ​​of multiple reference value clusters within a parameter space of at least one dimension defined by multiple quality parameters, The process includes identifying the driving state of the vehicle as the driving state of the vehicle, which is represented by a reference value cluster having the minimum interval for the quality parameters of the received GNSS signal.

[0007] This can achieve a technical advantage: it enables precise and easily implementable determination of the vehicle's driving state based on the real-time values ​​of the GNSS signal quality parameters. In this regard, the vehicle's driving state is identified by a cluster of reference values ​​in a one-dimensional or multi-dimensional parameter space, each having the minimum interval for the real-time values ​​of the received GNSS signal quality parameters. In this regard, the determination of this interval is easy to implement and requires little computational cost. This can achieve fast and computationally efficient determination of the vehicle's driving state during active driving.

[0008] According to one embodiment, the interval measure is a Euclidean interval. This may achieve a technical advantage: an interval measure that can be easily determined may be provided.

[0009] According to one embodiment, the reference values ​​of the reference value cluster are based on quality parameters of the reference GNSS signal received during the reference measurement, and this reference measurement was performed for various driving conditions of a single vehicle.

[0010] This can achieve a technical advantage: the precise assignment of reference values ​​for individual reference value clusters to their respective known driving conditions. In this regard, the reference values ​​are based on corresponding reference measurements, in which GNSS signals are recorded for known driving conditions, and corresponding reference values ​​for quality parameters are recorded. For this purpose, for example, corresponding test runs of equivalent vehicles can be performed to record the GNSS signals necessary for determining the reference values. In this regard, test data can be recorded for the known driving conditions of each vehicle undergoing the test run.

[0011] According to one embodiment, each reference cluster is based on multiple quality parameters of multiple GNSS signals from various positioning satellites. This allows for the technical advantage of considering multiple quality parameters. Various quality parameters enable precise determination of each driving condition. Furthermore, GNSS signals from multiple positioning satellites can be used. This allows for the simulation of the vehicle's actual navigation process, where GNSS signals from multiple different positioning satellites are received and considered in the navigation.

[0012] According to one embodiment, each reference value in a single reference value cluster is obtained by a weighted average of quality parameters of multiple GNSS signals from multiple positioning satellites. This can achieve a technical advantage: it may be possible to provide a precise reference value that can take into account GNSS signals from multiple positioning satellites.

[0013] In one embodiment, the assignment of each determined reference value cluster to a given driving state is provided by a appropriately trained artificial intelligence. This can achieve the technical advantage of enabling precise determination or assignment of each vehicle's driving state to a reference value cluster. To this end, artificial intelligence may be trained to assign multiple reference values ​​recorded during a reference measurement of a correspondingly received GNSS signal to the corresponding driving state in which the reference measurement was performed, for example by performing pattern recognition. The artificial intelligence may also be trained to characterize each known driving state by reference values ​​of quality parameters based on recognized commonalities or patterns.

[0014] According to one embodiment, the assignment of determined reference value clusters to each driving state of a vehicle is brought about by the execution of at least one K-Means algorithm, which is configured to integrate multiple reference values ​​into reference value clusters and to assign at least one reference value cluster to each known driving state, so that each driving state is represented by its respective reference value cluster.

[0015] This can achieve a technical advantage: the ability to assign reference values ​​from a reference value cluster to each driving state of a vehicle as precisely and efficiently as possible. This is achieved by running at least one K-Means algorithm that can assign multiple reference values ​​recorded during reference measurements of the quality parameters of the received GNSS signal for various driving states to each driving state based on recognized commonalities or patterns. This allows for a precise representation of each driving state to be achieved by corresponding reference value clusters. Furthermore, the execution of the K-Means algorithm can be achieved with low computational power.

[0016] According to one embodiment, the assignment of reference value clusters is brought about by the execution of multiple K-Means algorithms, wherein each K-Means algorithm is executed as a single 1-Means algorithm and is configured to integrate the corresponding reference values ​​into a single reference value cluster for exactly one of a plurality of known driving states and to assign them to each predetermined driving state, in which case a single dedicated 1-Means algorithm is executed for each predetermined driving state whose reference values ​​are determined by corresponding reference measurements.

[0017] This achieves a technical advantage: it enables the precise and efficient assignment of quality parameters of the received GNSS signal to various reference value clusters, each of which represents at least one driving state of the vehicle, with each reference value recorded during the reference measurement. The assignment of individual reference values ​​to their respective reference value clusters can be further simplified by running the K-Means algorithm as a single 1-Means algorithm, with respect to this, by running a separate 1-Means algorithm for each driving state to be determined. This 1-Means algorithm allows reference values ​​recorded in corresponding test measurements for one driving state to be integrated into a corresponding reference value cluster. By using a dedicated 1-Means algorithm for each driving state considered by corresponding reference measurements, corresponding reference value clusters can be formed for multiple different driving states. By using a single 1-Means algorithm instead of a single k-Means algorithm for each driving state, it can be ensured that the reference value cluster that best maps the reference value can be found for each driving state. This can reduce the complexity of this method, and consequently, the required computing power may decrease.

[0018] According to one embodiment, one driving state is at least one state from the following list, namely - The vehicle's GNSS antenna is damaged. - The GNSS antenna is covered with ice, snow, dust, mud, and moisture. - The GNSS antenna is at least partially obscured by the vehicle's roof luggage carrier and / or roof luggage and / or roof structure. - When the vehicle is in front of or behind a tunnel, under a bridge, or under a toll booth sign. - The vehicle is next to the truck, next to the building's facade, the vehicle is surrounded by trees, - The vehicle is in urban or intercity traffic. includes.

[0019] Thereby, various driving states that may occur during the running of the vehicle and the signal quality of the received GNSS signal may be impaired can be considered, and a technical advantage that such can be achieved can be achieved.

[0020] According to one embodiment, the quality parameter of the GNSS signal includes at least one parameter selected from the list consisting of signal strength, signal quality, signal frequency, number of received satellites, and signal phase.

[0021] Thereby, technical advantages that various quality parameters of the received GNSS signal, for example, provided by the measurement engine of the Vehicle-Motion-Positioning-Sensor (VMPS), can be considered can be achieved. With as many various quality parameters as possible, the precision of the determination of each driving state can be increased.

[0022] According to one embodiment, the control support function includes at least one function from the following list, - A function of determining the signal integrity of the received GNSS signal based on the determined driving state and providing the determined signal integrity to the navigation module of the vehicle control unit. - A function of instructing the navigation module to correct the positioning based on the received GNSS signal and the determined integrity assertion. - A function of notifying the driver of the vehicle of the driving state of the vehicle. - A function of providing the driving state of the vehicle to the vehicle control unit of the vehicle. - A function of generating an integrity assertion regarding the received GNSS signal based on the driving state and providing the integrity assertion to the vehicle control unit. includes.

[0023] This can achieve the technical advantage of providing efficient control assistance. For example, the signal integrity of the received GNSS signal can be determined based on the determined driving state. Furthermore, by considering GNSS signals with lower integrity with lower priority, appropriate signal integrity can be taken into account when navigating the vehicle based on the received GNSS signal. For example, positioning corrections based on the received GNSS signal can be made based on the determined signal integrity. Alternatively, or in addition to this, the appropriately determined driving state can be provided as additional information to both the vehicle control unit and the vehicle driver. This allows additional information about the vehicle state to influence vehicle control.

[0024] A second aspect of the present invention is provided, which is configured to perform a vehicle control support method based on one of the embodiments described above. A third aspect of the present invention is provided, which includes a computer program product that, when the data processing unit executes the program, includes an instruction that causes the data processing unit to execute a vehicle control support method based on one of the embodiments described above.

[0025] Exemplary embodiments of the present invention will be described with reference to the following drawings. [Brief explanation of the drawing]

[0026] [Figure 1] This is a schematic diagram of a vehicle control support system based on one embodiment. [Figure 2] This is a schematic diagram of the parameter space defined by the quality parameters of the GNSS signal. [Figure 3] This is a flowchart of a vehicle control support method based on one embodiment. [Figure 4] This is a schematic diagram of a computer program product. [Modes for carrying out the invention]

[0027] Figure 1 shows a schematic diagram of a control support system 200 for a vehicle 201 based on one embodiment. In one embodiment shown, the vehicle 201 includes at least one GNSS antenna 217 configured to receive GNSS signals 203 from at least one positioning satellite 205 of the Global Navigation Satellite System. The vehicle 201 further includes a computing unit 219 configured to perform the navigation assistance method according to the present invention for the vehicle 201.

[0028] For control assistance according to the present invention, the vehicle 201 receives a GNSS signal 203 from at least one positioning satellite 205 using a GNSS antenna 217. Subsequently, quality parameters 206, 207, and 208 are determined with respect to the received GNSS signal 203, provided, for example, by the measurement engine of the vehicle motion positioning sensor (VMPS) of the vehicle 201. The quality parameters 206, 207, and 208 may include, for example, signal quality, signal-to-noise ratio, signal frequency, or further parameters representing the signal quality of the received signal. Next, the driving state 209 of the vehicle 201 is determined based on the determined quality parameters 206, 207, and 208, and in particular based on the respective values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203. For this purpose, the values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 are compared with corresponding reference values ​​of stored known reference value clusters 213 and 214, in which case each reference value cluster 213 and 214 represents one individual driving state 209. The values ​​211 of the quality parameters 206, 207, and 208 may, in particular, be shown as a combination of multiple quality parameters 206, 207, and 208, that is, as the centroid or center point of the quality parameters 206, 207, and 208 in parameter space.

[0029] In this regard, reference value clusters 213 and 214 may each contain multiple reference values ​​for quality parameters 206, 207, and 208. In this regard, these reference values ​​can be generated by corresponding reference measurements, which are performed on predetermined driving conditions of the vehicle 201, and during these reference measurements, corresponding GNSS signals 203 having corresponding quality parameters 206, 207, and 208 are received. In this regard, corresponding reference value clusters 213 and 214 can be generated by running correspondingly trained artificial intelligence or by running at least one K-Means algorithm. For this purpose, the reference values ​​of quality parameters 206, 207, and 208 recorded by reference measurements can be integrated into corresponding reference value clusters 213 and 214 based on corresponding commonalities or patterns of multiple reference values. This can be done such that each reference value cluster 213 and 214 represents one individual driving condition 209 of the vehicle 201. In this regard, if the corresponding reference values ​​for the quality parameters 206, 207, and 208 of each reference value cluster 213, 214 were recorded or received when each vehicle was operating in a corresponding driving condition 209, then one reference value cluster 213, 214 represents one driving condition 209. This can be achieved or aided by, for example, performing a corresponding test run to create a reference measurement during a predetermined driving condition of each vehicle used for the test run, thereby ensuring that the reference values ​​of the quality parameters of the GNSS signal recorded during the reference measurement have a common specificity, and this common specificity distinguishes these reference values ​​from reference values ​​recorded during another reference measurement and between corresponding different driving conditions 209 of the vehicle.

[0030] The driving conditions 209 thus determined may be, for example, obtained when the GNSS antenna 217 is covered with ice, snow, moisture, dust, or mud. Alternatively, driving conditions 209 may be obtained when the vehicle is before / after a tunnel or under a bridge or toll recording equipment at the time the GNSS signal is recorded. Alternatively, driving conditions 209 may be characterized by the vehicle operating in urban traffic with many multi-story buildings, or in intercity traffic with wide open spaces beside the roadway. In this regard, each driving condition 209 is characterized by the fact that they affect the signal quality of the received GNSS signal 203 in a characteristic way. In this regard, corresponding values ​​of the quality parameters of the received GNSS signal differ in a characteristic way from the values ​​of the quality parameters of the GNSS signal received while the vehicle is in a different driving condition 209. Thus, by considering the values ​​of the quality parameters 206, 207, and 208, each driving condition 209 in which the vehicle was at the time each GNSS signal 203 was received can be inductively inferred.

[0031] Therefore, by executing a corresponding algorithm, the reference values ​​of quality parameters 206, 207, and 208 determined during the reference measurement can be integrated into corresponding reference value clusters 213 and 214, each representing a separate driving condition 209.

[0032] The appropriately determined reference value clusters 213, 214 or their associated assignments to the respective driving states 209 may be stored, for example, in an appropriate data bank, so that during the navigation of the vehicle 201, during the vehicle's driving at any given time, the predetermined reference value clusters 213, 214 and their associated assignments can be utilized to perform the control support method according to the present invention.

[0033] When vehicle 201 is under active control, in order to determine the current driving state, the values ​​211 of the quality parameters 206, 207, and 208 of the GNSS signal 203 received during vehicle 201's navigation are compared with the respective reference values ​​of known stored reference value clusters 213 and 214. For this purpose, the interval determination of the current values ​​211 of the quality parameters 206, 207, and 208 relative to the reference value clusters 213 and 214 can be determined within the parameter space 215 defined by the multiple quality parameters 206, 207, and 208. In this case, the current values ​​211 of the quality parameters 206, 207, and 208 of the GNSS signal 203 recorded during navigation are assigned to the reference value clusters 213 and 214 that have the minimum interval for the current value 211, respectively. In this regard, the driving conditions 209 represented by the respective reference value clusters 213 and 214 are identified as the driving conditions of the vehicle 201 at any given time. For a detailed explanation of the interval determination or assignment of the values ​​211 of the quality parameters 206, 207, and 208 of the recorded GNSS signal 203 to the respective reference value clusters 213 and 214, please refer to the explanation of Figure 2.

[0034] Furthermore, a control support function is provided based on the determined driving state 209. The control support function may, for example, determine the signal integrity of the received GNSS signal 203 based on the determined driving state 209 and provide this signal integrity to a navigation module, such as a Vehicle Motion Positioning Sensor (VMPS). Furthermore, the navigation module may be instructed to correct the position determination based on the determined driving state 209. Alternatively or in addition to this, the determined driving state 209 may be provided as independent information to both the vehicle control unit and the driver of the vehicle, so that the determined driving state 209 can be taken into consideration in the control of the vehicle.

[0035] Figure 2 shows a schematic diagram of the parameter space 215 defined by the quality parameters 206, 207, and 208 of the GNSS signal 203. In the shown embodiment, the parameter space 215 is defined by three different quality parameters 206, 207, and 208. These quality parameters 206, 207, and 208 can be obtained, for example, from the signal strength, signal-to-noise ratio, and signal frequency of the received GNSS signal 203.

[0036] Within the parameter space 215, two further distinct reference value clusters 213 and 214 are recorded, each of which represents one individual driving condition 209 or 210. The two distinct driving conditions 209 or 210 can be obtained, for example, when one is at least partially obscured by the GNSS antenna 217 of the vehicle 201, for example by a roof luggage carrier installed on the vehicle 201, and when the vehicle is under a bridge. To generate the reference value clusters 213 and 214, corresponding reference measurements can be performed, for example, by corresponding test runs of one vehicle, for different driving conditions 209 or 210. In this regard, the reference values ​​of the quality parameters 206, 207, and 208 of the GNSS signal 203 received during reference measurement, as thus determined, can be generated by appropriately trained artificial intelligence or by appropriately formed K-Means algorithms, for which the appropriate reference values ​​of the quality parameters are integrated into reference value clusters 213 and 214 by the artificial intelligence or K-Means algorithm, depending on the respective specificities that they have based on the respective driving conditions 209 and 210.

[0037] Figure 2 further shows the moment-in-time value 211 of one of the quality parameters 206, 207, and 208 recorded during the navigation process of the controlled vehicle 201 at that time. To determine each driving state 209 in which the vehicle 201 was at the time each GNSS signal 203 was recorded, each moment-in-time value 211 is determined by determining the interval of value 211 with respect to known stored reference value clusters 213 and 214 and each reference value cluster 213 and 214 within the parameter space 215. Based on this interval determination, each moment-in-time value 211 is assigned to the reference value cluster 213 and 214 that has the smallest interval for each value 211. Thus, in Figure 2, the value 211 is assigned to reference value cluster 213 because reference value cluster 213 with interval D1 has a smaller interval for the moment-in-time value 211 than reference value cluster 214 with interval D2. In this case, the current driving state 209 of the vehicle 201 is identified by driving states 209, 210, which are represented by respective reference value clusters 213, 214 to which values ​​211 of the respective quality parameters 206, 207, and 208 are assigned. Thus, in the embodiment shown, the driving state 209 of reference value cluster 213 is identified as the current driving state of the vehicle 201.

[0038] Determining the interval of a given value 211 with respect to two reference value clusters 213 and 214 can be achieved by determining the interval of the given value 211 with respect to the centers of each reference value cluster 213 and 214 in the parameter space.

[0039] Figure 3 shows a flowchart of a control support method 100 for a vehicle 201 based on one embodiment. The control support method 100 according to the present invention for the vehicle 201 can be implemented by a system 200 based on the embodiment shown in Figure 1.

[0040] For this purpose, in process step 101, a GNSS signal from at least one positioning satellite 205 is received. In a further process step 103, the quality parameters 206, 207, and 208 of the received GNSS signal 203 are determined.

[0041] In a further process step 105, based on the quality parameters 206, 207, and 208 of the received GNSS signal 203, the values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 are compared with known reference value clusters 213 and 214 to determine one of the driving states 209 and 210 of the vehicle 201. These reference value clusters 213 and 214 contain multiple reference values ​​for each of the quality parameters 206, 207, and 208 of the GNSS signal 203, and each reference value cluster 213 and 214 represents one of the known driving states 209 and 210.

[0042] In this regard, in process step 109, intervals D1 and D2 of the moment-in-time values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 relative to the reference values ​​of multiple reference value clusters 213 and 214 are determined based on interval measures within a parameter space 215 of at least one dimension defined by multiple quality parameters 206, 207, and 208. The determination of the intervals of the moment-in-time values ​​211 relative to the reference value clusters 213 and 214 can be performed based on the embodiment shown in Figure 2.

[0043] In process step 111, the current driving conditions 209 and 210 of the vehicle 201 are identified as driving conditions 209 and 210, represented by reference value clusters 213 and 214 having minimum intervals D1 and D2 for the current values ​​211 of the quality parameters 206, 207, and 208 of the GNSS signal 203 received during the vehicle 201's navigation process.

[0044] In a further process step 107, control assistance functions are provided based on the determined driving conditions 209 and 210. These control assistance functions may include, for example, providing signal integrity for the received GNSS signal 203.

[0045] According to the present invention, at least one so-called K-Means algorithm can be performed to determine the reference value clusters 213 and 214. The K-Means algorithm is used for cluster analysis of measured values ​​or data points and is configured to identify commonalities and / or patterns in these data points, and to organize these data points into clusters of data points based on patterns or commonalities, in which case the data points in one cluster are more similar to each other with respect to patterns or commonalities than the data points in another cluster. In its basic form, the K-Means algorithm is an algorithm for unsupervised learning. In this case, the goal of the algorithm is to find the minimum value of a quality function of the following form.

[0046]

number

[0047] In the formula, dist represents the distance function, and c i x represents the center of the i-th cluster, x represents the recorded data point, and k represents the number of clusters. In this regard, the distance function dist is given, for example, between the recorded and to be classified data point x from an M-dimensional space and the center of the i-th cluster (c i This can represent the Euclidean distance between (also called "Centroid") and (the clusters). The number of clusters, k, is generally a freely selectable parameter.

[0048] In this invention, the K-Means algorithm is transformed into an algorithm for supervised learning. For k=1, one 1-Means algorithm is used for each cluster to be determined. Thus, individual reference measurements are performed for each driving state 209, 210 and the corresponding GNSS signals 203 are received in order to generate reference clusters 213, 214. The respective quality parameters 206, 207, and 208 of the GNSS signals received for the reference measurements are assigned to the respective reference clusters 213, 214 by finding the minimum value of the distance function using a correspondingly trained 1-Means algorithm. For various known driving states, corresponding reference measurements are performed and individual 1-Means algorithms are executed to generate reference clusters 213, 214.

[0049] One specific use case might be a test drive with a roof structure attached, and another use case might be city driving or driving with an ice-covered GNSS antenna. Since the 1-Means algorithm calculates only one baseline cluster per model, the N baseline clusters are precisely the aggregate points that most precisely characterize each driving condition 209, 210.

[0050] Therefore, depending on the training stage, multiple different reference value clusters 213, 214 can be generated for various driving conditions, mathematically representing different scenarios / surrounding conditions of driving conditions 209, 210. In this regard, each reference value cluster 213, 214 includes multiple quality parameters 206, 207, 208 of the GNSS signal 203 received in the reference measurement. The quality parameters 206, 207, 208 may include, for example, signal quality, signal-to-noise ratio, signal strength, signal frequency, or other information important to quality. The GNSS signal 203 received in the reference measurement may include signals from multiple different positioning satellites 205. The quality parameters 206, 207, 208 of the reference value clusters 213, 214 may be formed as a weighted average of the quality parameters 206, 207, 208 of multiple GNSS signals 203 from multiple different positioning satellites 205.

[0051] The determination of reference value clusters 213 and 214 can be performed offline, that is, before the corresponding algorithms are incorporated into each vehicle. If the accumulation points or reference value clusters 213 and 214 are known, then, in order to determine the driving conditions 209 and 210, an online-corresponding assignment can be made to the already existing reference value clusters 213 and 214 of the quality values ​​206, 207, and 208 of the GNSS signals 203 received by the vehicle 201 and recorded during the navigation process, while the vehicle 201 is online, i.e., during active control of the vehicle 201.

[0052] For each navigation epoch in which the GNSS signal 203 is received, the distances or intervals D1 and D2 of the moment-in-time value 211 to the known reference value clusters 213 and 214 are calculated. The distance function dist can represent the Euclidean distance in a multidimensional parameter space, as already mentioned. The assignment of the moment-in-time value 211 of the quality parameters 206, 207, and 208 to the known reference value clusters 213 and 214 can be expressed mathematically as follows:

[0053]

number

[0054] D corresponds to the distance D1 or D2 to be determined, and each execution of this equation can determine one of the distances D1 or D2. In this regard, the value 211 at any given time is denoted by the variable x in the equation eq.2 shown, while the reference value clusters 213, 214, or the center of the clusters is denoted by the variable c j This is shown, and this j takes into account the number of different clusters, and thus the number of different driving conditions.

[0055] For each navigation epoch in which vehicle navigation is performed based on the GNSS signal 203, the values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 are set to the respective reference value clusters 213, 214, and c j The intervals D1 and D2 for each of these are the minimum reference value clusters 213, 214, and c. j Therefore, in the embodiment shown in Figure 2, the values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 can be assigned to the reference value cluster 213 based on a shorter interval D1.

[0056] In accordance with the assignment of the values ​​211 of the quality parameters 206, 207, and 208 of the received GNSS signal 203 to one of the reference value clusters 213, 214, the driving conditions 209, 210 represented by each reference value cluster are identified as the driving conditions 209, 210 of the vehicle 201 at that time.

[0057] The dimension of the parameter space 215 is defined by the number of quality parameters 206, 207, and 208 considered in the analysis. In the embodiment shown, only three different quality parameters 206, 207, and 208 are considered, such as signal strength, signal-to-noise ratio, and signal frequency, so the parameter space 215 is three-dimensional. However, this is merely an example. In actual embodiments, many quality parameters 206, 207, and 208 may be considered, which results in an N-dimensional parameter space 215.

[0058] Figure 4 shows a schematic diagram of a computer program product 300 that includes instructions to cause the computing unit to execute a control support method 100 for the vehicle 201 when the computing unit executes the program.

[0059] In the embodiment shown, the computer program product 300 is stored on a memory medium 301. In this regard, the memory medium 301 may be any memory medium known in the present art. [Explanation of symbols]

[0060] 100 Control support methods 200 Control Support System 201 vehicles 203 GNSS signals 205 positioning satellites 206, 207, 208 Quality Parameters 209, 210 Driving condition 211 Values ​​of quality parameters 206, 207, and 208 213, 214 Reference Value Cluster 215 Parameter Space 217 GNSS antenna 219 computing units 300 Computer Program Products 301 Memory media D1, D2 interval

Claims

1. A method (100) for assisting in the control of a vehicle (201), comprising: - a step (101) of receiving GNSS signals (203) from at least one positioning satellite (205); - a step of determining (103) quality parameters of the GNSS signals (203), the quality parameters (206, 207, 208) representing the reception quality of the received GNSS signals (203); - a step of determining (105) the driving state (209, 210) of the vehicle (201) based on the quality parameters (206, 207, 208) of the GNSS signals (203) by comparing the values (211) of the quality parameters (206, 207, 208) of the received GNSS signals (203) with known reference value clusters (213, 214), the reference value clusters (213, 214) including a plurality of reference values for the respective quality parameters (206, 207, 208) of the GNSS signals (203), each reference value cluster (213, 214) representing one known driving state (209, 210), and each known driving state (209, 210) representing one state of the vehicle (201) that affects the signal transmission of the GNSS signals (203); - a step (107) of providing a control assistance function based on the determined driving state (209, 210). A method (100) comprising the above steps.

2. The determination (105) of the driving state (209, 210) comprises: - a step (109) of determining, based on an interval measure, the intervals (D1, D2) between the values (211) of the quality parameters (206, 207, 208) of the received GNSS signals (203) and the reference values of the plurality of reference value clusters (213, 214) within at least one-dimensional parameter space (215) defined by the plurality of quality parameters (206, 207, 208); - a step (111) of identifying, as the driving state of the vehicle (201), the driving state (209, 210) represented by the reference value cluster (213, 214) having the minimum interval (D1, D2) with respect to the values (211) of the quality parameters (206, 207, 208) of the received GNSS signals (203). The method (100) according to Claim 1, comprising the above steps.

3. The method (100) according to Claim 2, wherein the interval measure is a Euclidean interval.

4. The method (100) according to claim 1, wherein the reference values of the reference value clusters (213, 214) are based on quality parameters (206, 207, 208) of reference GNSS signals received during reference measurement, and the reference measurement is performed for various driving states (209) of one vehicle (201).

5. The method (100) according to claim 4, wherein each reference value cluster (213, 214) is based on a plurality of quality parameters (206, 207, 208) of GNSS signals (203) of various positioning satellites (205).

6. The method (100) according to claim 5, wherein each reference value of one reference value cluster (213, 214) is obtained by a weighted average value of quality parameters of GNSS signals (203) of a plurality of the positioning satellites (205).

7. The method (100) according to claim 1, wherein the assignment of each of the determined reference value clusters (213, 214) to the driving states (209, 210) is brought about by an appropriately trained artificial intelligence.

8. The method (100) according to claim 1, wherein the assignment of each of the determined reference value clusters (213, 214) of the vehicle (201) to the driving states (209, 210) is brought about by execution of at least one K-Means algorithm, and the at least one K-Means algorithm is set to integrate a plurality of the reference values into reference value clusters (213, 214) and to assign at least one reference value cluster to each known driving state (209, 210), such that each of the driving states is represented by each of the reference value clusters.

9. The assignment of the reference value clusters (213, 214) is brought about by the execution of a plurality of K-Means algorithms, in which each K-Means algorithm is executed as one 1-Means algorithm and integrates the corresponding reference values into one reference value cluster (213, 214) and assigns them to exactly one of the plurality of known driving states, and in this case, for each predetermined driving state (209, 210) for which the reference value is determined by a corresponding reference measurement, one dedicated 1-Means algorithm is executed, the method (100) according to claim 7.

10. One driving state (209, 210) is at least one state from the following list, - a state in which the GNSS antenna (217) of the vehicle (201) is damaged, - a state in which the GNSS antenna (217) is covered by ice, snow, dust, mud, moisture, - a state in which the GNSS antenna (217) is at least partially covered by the roof luggage carrier and / or roof luggage and / or roof structure of the vehicle (201), - a state in which the vehicle is in front of or behind a tunnel, under a bridge, under a toll signboard, - a state in which the vehicle (201) is beside a truck, beside a soundproof wall, beside a building facade, a state in which the vehicle is surrounded by trees, - a state in which the vehicle is in urban traffic or intercity traffic, comprising the method (100) according to claim 1.

11. The quality parameters (206, 207, 208) of the GNSS signal (203) are at least one parameter from the following list, namely - signal strength, signal-to-noise ratio, signal quality, signal frequency, the number of received satellites, signal phase, comprising the method (100) according to claim 1.

12. The control support function is at least one function from the following list, - a function of determining the signal integrity of the received GNSS signal (203) based on the determined driving state (209, 210) and providing the determined signal integrity to the navigation module of the vehicle control unit, - A function of instructing the navigation module to correct the positioning based on the received GNSS signal (203) and the determined integrity assertion; - A function of notifying the driver of the vehicle (201) of the driving state of the vehicle (201); - A function of providing the driving state (209, 210) of the vehicle (201) to the vehicle control unit of the vehicle (201); The method (100) according to claim 1, comprising the above.

13. A computing unit (219) configured to execute the control assistance method (100) of the vehicle (201) according to any one of claims 1 to 12.

14. A computer program product (300) including instructions for causing the data processing unit to execute the control assistance method (100) of the vehicle according to any one of claims 1 to 12 when the data processing unit executes a program.

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