System and method for map matching the GNSS position of a vehicle

Abstract

The invention relates to a computer-implemented method and system for map matching of a GNSS position (G1, G2, G3) of a vehicle (1) with location information (P1a, P1b, P1c, P2a, P2b, P3a, P3b) of a digital road map (K), comprising map matching of a determined distance (E1, E2) between each two consecutive GNSS positions (G1, G2, G3) of the vehicle (1) with a route length (L1, L2) between each two consecutive GNSS positions (G1, G2, G3) of the vehicle (1) on the digital road map (K). The invention also relates to a computer program and a computer-readable data carrier.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method for map-matching a GNSS position of a vehicle with location information of a digital road map. The present invention also relates to a system for map-matching a GNSS position of a vehicle with location information of a digital road map. Background Art

[0002] When performing map matching, a sequence of GNSS positions is mapped onto the road network of a digital road map. This map matching method is intended to relatively improve the mapping accuracy, for example the mapping accuracy of the vehicle's position onto the corresponding connecting roads. Here, for each GNSS position it is determined which road the vehicle is traveling on.

[0003] A distinction is made here between online and offline map matching. In online map matching, each GNSS position is matched to the road in real time without knowing the subsequent GNSS position, while in offline map matching, the GNSS positions are matched after recording a journey or a journey segment. Online and offline map matching can be performed both in the vehicle and in the backend after the GNSS position has been transmitted. However, online map matching is often used in the vehicle and offline map matching is often used in the backend.

[0004] The GNSS position of the vehicle is determined in the vehicle at a frequency of 1 Hz and can be collected at this frequency or at a lower frequency for further processing. In addition, dead reckoning is used in order to determine the vehicle position more accurately.

[0005] For different applications, the vehicle position, which is optionally improved by dead reckoning, is mapped onto the road network in the vehicle or after transmission to the backend using offline map matching. Carrying out map matching in the backend instead of in the vehicle has the advantage that the latest digital road map is always available in the backend. In addition, offline map matching can usually achieve better results than map matching during driving.

[0006] As described, for example, in Newson, Paul, and John Krumm, "Hidden Markov map Matching through noise and sparseness", Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2009, a road network can be modeled as a graph consisting of directed and undirected edges. Unlike the publication by Newson and Krumm, a directed edge does not necessarily mean a one-way street, since a road that can be driven in both directions can also be modeled as two directed edges. Each edge has a description of its geometry, for example as a polyline (i.e., a line consisting of several segments). Map manufacturers provide maps in different formats with different modeling. Thus, in some modelings, links can only end at intersections, or there are only directed edges. However, the above modeling represents the most general case.

[0007] Newson and Krumm describe a map matching method based on a hidden Markov model (HMM). The method uses the Viterbi algorithm to calculate the most likely sequence of connecting lines that the vehicle has traveled. In this case, each GNSS position is mapped to a so-called match, i.e. a combination of a connecting line and a position on the connecting line (in short: connecting line, position on connecting line). The position on the connecting line can be, for example, a fraction, i.e. a number between 0 and 1.

[0008] However, the above-described method according to Newson and Krumm is susceptible to map errors in road topology (eg, roads missing in the road map) and road geometry. Summary of the invention

[0009] Therefore, there is a need to provide an improved map matching method and system with higher accuracy.

[0010] This object is achieved by means of a computer-implemented method according to the invention for map matching of a GNSS position of a vehicle with location information of a digital road map.

[0011] The object is also achieved by means of a system according to the invention for map matching of a GNSS position of a vehicle with location information of a digital road map.

[0012] Furthermore, the object is achieved by means of a computer-readable data medium according to the invention.

[0013] The present invention provides a computer-implemented method for map matching a GNSS position with location information of a digital road map. The location information includes, for example, location-specific coordinates of the digital road map.

[0014] The method includes detecting a GNSS position of a vehicle along a route of the vehicle.

[0015] The method further comprises recording a distance between each two consecutive GNSS positions of the vehicle detected using vehicle sensors, in particular wheel speed sensors. Furthermore, the method comprises map matching the distance determined between each two consecutive GNSS positions of the vehicle detected with a route length between each two consecutive GNSS positions of the vehicle detected on a digital road map.

[0016] In this method, the matching candidate C in time step t is calculated using the following formula t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of is:

[0017]

[0018] Where r is C t,i With C t+1,j The length of the fastest route between t and t+1, d is the distance covered between times t and t+1, determined using the vehicle sensors, p or p(r , d) is the probability density, σ is the standard deviation of the detected GNSS position, i and j are numeric placeholders for location information, and the matching candidate is the location information.

[0019] Furthermore, the present invention provides a system for map matching a GNSS position of a vehicle with location information of a digital road map. The system comprises means for detecting the GNSS position of the vehicle along a route of the vehicle.

[0020] The system further comprises means for recording a distance between each two consecutive GNSS positions of the vehicle detected, determined using vehicle sensors, in particular wheel speed sensors. The system further comprises means for map-matching the distance determined between each two consecutive GNSS positions of the vehicle detected, with a length of a route between each two consecutive GNSS positions of the vehicle detected on a digital road map.

[0021] In this system, the matching candidate C in time step t is calculated using the following formula t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of is:

[0022]

[0023] Where r is C t,i With C t+1,jThe length of the fastest route between t and t+1, d is the distance covered between the time points t and t+1, determined using the vehicle sensors (10), p or p(r , d) is the probability density, σ is the standard deviation of the detected GNSS position, i and j are numeric placeholders for location information, and the matching candidate is the location information.

[0024] The invention further provides a computer program having a program code for executing the method according to the invention when the computer program is executed on a computer.

[0025] The invention further provides a computer-readable data carrier having a program code of a computer program for executing the method according to the invention when the computer program is executed on a computer.

[0026] The idea of ​​the invention is to record the distance covered by the odometer of the vehicle since the start of the journey for each GNSS position and thereby increase the accuracy of the map matching. This is achieved by matching the distance covered between each two successive GNSS positions determined by the odometer to the length of the route between two matching GNSS positions on the digital road map. This advantageously allows a higher accuracy of the map matching to be achieved.

[0027] Advantageous embodiments and refinements emerge from the description with reference to the figures.

[0028] According to a preferred embodiment, map matching is performed in real time, after recording a journey or after recording a journey section, using a vehicle-internal computing device and / or a vehicle-external server, and a timestamp is assigned to each detected GNSS position of the vehicle. Thus, the best possible implementation including online and / or offline map matching can be used in an advantageous manner, depending on the system requirements.

[0029] According to another preferred embodiment, the following formula is used to calculate the matching candidate C in time step t: t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of is:

[0030]

[0031] Where r is C t,i With C t+1,j The length of the fastest route between time points t and t+1; d is the distance traveled between time points t and t+1; p or p(r ,d) is the probability density, σ is the standard deviation of the detected GNSS position, and i and j are digital placeholders for ground truth road segment and location information. The location information in this embodiment is represented by P1a, P1b, P1c, P2a, P2b, P3a, and P3b.

[0032] In this embodiment, the matching candidates are represented by M1a, M1b, M1c, M2a, M2b, M3a, and M3b. t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of , for example, may represent the probability density of transitioning from matching candidate M1c to matching candidate M2b.

[0033] In another preferred embodiment, a kernel density estimator is used instead of the above equation. Thus, the probability distribution can be advantageously calculated taking into account the length of the fastest route and the distance covered, without having to make assumptions about the structure of the probability distribution.

[0034] According to another preferred embodiment, it is provided that the standard deviation σ is determined by the square root of the sample variance of samples of GNSS positions and vehicle sensor distance measurements over a plurality of trips in which the route traveled is known. The standard deviation is advantageously determined here by the square root of the sample variance of samples of ground truth data. GNSS positions and odometer measurements over a plurality of trips in which the route traveled is known are used as ground truth data.

[0035] According to another preferred embodiment, provision is made for a transition between matching candidates to be excluded if the route length of the fastest route between the matching candidates is shorter or longer than the distance determined using the vehicle sensors by a predetermined factor. Thus, a further criterion can be advantageously taken into account which allows for a better determination of the best possible matching candidate.

[0036] According to another preferred embodiment, the GNSS positions, current speeds and / or current average speeds of the plurality of vehicles are periodically transmitted to a vehicle-external server, which uses this information to calculate traffic information, in particular expected arrival times and / or average speeds for the route segments, and provides them to the plurality of vehicles. Map matching can therefore also be used to calculate more accurate predictions of expected arrival times and / or average speeds for the route segments and provide them to the plurality of vehicles in the fleet.

[0037] According to another preferred embodiment, it is provided that the hazard data detected by the vehicle sensors, in particular hazard data about slippery roads, traffic accidents and / or airbag activations, are transmitted together with the GNSS position of the vehicle to a vehicle-external server, which performs map matching on the hazard data and makes them available to the vehicle whose planned route passes through the identified hazard. Thus, map matching can also be used in an advantageous manner to provide hazard information to other vehicles in the fleet.

[0038] According to another preferred embodiment, the driver's personal route is recorded as a sequence of GNSS positions and sent to a vehicle-external server, which performs map matching of the GNSS positions, transmits the learned driver's personal route to the vehicle, and suggests the driver a destination guide for the learned personal route at a predetermined time. Thus, the learning of the driver's personal route can be used in an advantageous manner to suggest this route to the driver at a suitable time at which the system considers it possible to travel this route based on the collected data.

[0039] According to another preferred embodiment, the distance between each two detected successive GNSS positions of the vehicle, determined using the vehicle sensors, is used for a plausibility test of the map matching result, wherein the length of the fastest route between two adjacent matched GNSS positions is calculated in each case, and if this length is shorter or longer than the distance determined using the vehicle sensors by a predetermined factor, at least one of the two determined GNSS positions of the vehicle is evaluated as implausible. This plausibility test of the map matching result also advantageously contributes to a higher map matching accuracy.

[0040] The described embodiments and refinements can be combined with one another as desired.

[0041] Further possible embodiments, refinements and implementations of the invention also include combinations of features of the invention which have not been explicitly mentioned which have been described above or below with reference to the exemplary embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are intended to provide a further understanding of the embodiments of the present invention. They illustrate the embodiments and, together with the description, are used to explain the principles and schemes of the present invention.

[0043] Further embodiments and a number of advantages among those mentioned will be apparent from the drawings. The elements shown in the drawings are not necessarily shown to scale with respect to one another.

[0044] The attached pictures are as follows:

[0045] Figure 1A schematic diagram showing a system for map matching a GNSS position of a vehicle with location information of a digital road map according to a preferred embodiment of the present invention;

[0046] Figure 2 A schematic diagram showing a digital road map for illustrating a method for map matching a GNSS position of a vehicle with location information of a digital road map according to a preferred embodiment of the present invention; and

[0047] Figure 3 A flow chart illustrating a computer-implemented method for map matching a GNSS position of a vehicle with location information of a digital road map. DETAILED DESCRIPTION

[0048] exist Figure 1 The system for map matching a GNSS position of a vehicle 1 with location information of a digital road map shown in comprises a vehicle 1 having a device 20 for detecting the GNSS position of the vehicle 1 along a route of the vehicle 1. Within the scope of the present embodiment, GPS is the preferred form of GNSS.

[0049] The system further comprises a device 22 for recording a distance determined using vehicle sensors 10, in particular wheel speed sensors, between each two consecutive GNSS positions of the vehicle 1. The system further comprises a device 24 for map-matching the determined distance between each two consecutive GNSS positions of the vehicle 1 with a route length between each two consecutive GNSS positions of the vehicle on a digital road map.

[0050] The map matching is preferably performed in real time using the vehicle-internal computing device 12 and the vehicle-external server 14 , wherein each detected GNSS position of the vehicle 1 is assigned a time stamp.

[0051] As an alternative, the map matching can be performed, for example, after recording a journey or after recording a journey section using the vehicle-internal computing device 12 and the vehicle-external server 14 .

[0052] As an alternative, the map matching device 24' may be as follows Figure 1 The example shown is provided on a vehicle-external server 14 .

[0053] Vehicle 1 also has other Figure 1 The sensors shown in the figure include a speed sensor, a sensor for detecting airbag activation, a sensor for detecting a slippery road, and / or a camera sensor.

[0054] The vehicle external server 14 has no Figure 1 In addition, the vehicle external server 14 performs calculations of traffic information, calculations of hazard warnings and / or learning of the driver's personal route based on the data received by the vehicle 1. The calculated data is then provided to other vehicles of the fleet.

[0055] Figure 2 A schematic diagram of a digital road map is shown for explaining a method for map matching a GNSS position of a vehicle with location information of a digital road map according to a preferred embodiment of the present invention.

[0056] In this diagram, three GNSS positions G1, G2, G3 of the vehicle 1 are exemplarily shown on the digital road map K. Since the GNSS positions G1, G2, G3 of the vehicle 1 are plotted in the road map K in a staggered manner relative to the corresponding roads of the digital road map K in this embodiment, in order to calculate the movement path of the vehicle 1, it is necessary to map match the GNSS positions G1, G2, G3 of the vehicle 1 with the location information of the digital road map K.

[0057] The first GNSS position G1 of the vehicle 1 is located near, for example, three road sections or location information P1a, P1b, P1c.

[0058] Likewise, the second GNSS position G2 of the vehicle 1 is located near two road sections or location information P2a, P2b. In addition, the third GNSS position G3 of the vehicle 1 is located near two roads or road sections or location information P3a, P3b.

[0059] Therefore, firstly the GNSS positions G1, G2, G3 of the vehicle 1 are detected along the route of the vehicle 1. Subsequently, the distances E1, E2 determined using vehicle sensors, in particular wheel speed sensors, between each two detected successive GNSS positions of the vehicle 1 are recorded, for example the distance E1 between the first GNSS position G1 and the second GNSS position G2.

[0060] The determined distance E1 between the first GNSS position G1 and the second GNSS position G2 of the vehicle 1 is then matched to the first route length L1 on the digital road map K.

[0061] Likewise, a second distance E2 between the second GNSS position G2 and the third GNSS position G3 is detected and the determined distance E2 between the second GNSS position G2 and the third GNSS position G3 is matched to the route length L2 on the digital road map K.

[0062] In addition, the matching candidate C in time step t is calculated using the following formula t,i Transition to matching candidate C at time step t+1 t+1,jThe probability density of is:

[0063]

[0064] Where r is C t,i With C t+1,j The length of the fastest route between time points t and t+1; d is the distance traveled between time points t and t+1; p or p(r , d) is the probability density, σ is the standard deviation of the detected GNSS position, and i and j are digital placeholders for ground truth road segment and location information. The location information in this embodiment is represented by P1a, P1b, P1c, P2a, P2b, P3a, and P3b.

[0065] In this embodiment, the matching candidates are represented by M1a, M1b, M1c, M2a, M2b, M3a, and M3b. t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of , for example, may represent the probability density of transitioning from matching candidate M1c to matching candidate M2b.

[0066] The standard deviation σ is determined by taking the square root of the sample variance of samples of the GNSS positions G1 , G2 , G3 and the vehicle sensor distance measurements over a number of trips in which the route travelled is known.

[0067] If the route length L1, L2 of the fastest route between the matching candidates M1a, M1b, M1c, M2a, M2b, M3a, M3b is shorter or longer than the distance E1, E2 determined when using the vehicle sensor 10 by a predetermined factor, the transition between the matching candidates M1a, M1b, M1c, M2a, M2b, M3a, M3b is excluded.

[0068] The GNSS positions G1, G2, G3, current speeds and / or current average speeds of multiple vehicles are periodically transmitted to the vehicle external server 14, and by using this information, the vehicle external server 14 calculates traffic information, especially expected arrival time and / or average speed of the road section, and provides it to multiple vehicles.

[0069] Hazard data detected by the vehicle sensors, in particular data about slippery roads, traffic accidents and / or airbag activations, are transmitted together with the GNSS position G1, G2, G3 of the vehicle 1 to a vehicle-external server 14, which performs map matching of the hazard data and provides these data to the following vehicles 1, whose planned routes pass through the identified hazards.

[0070] The driver's personal route is recorded as a sequence of GNSS positions G1 , G2 , G3 and sent to the vehicle-external server 14 .

[0071] The vehicle external server 14 performs map matching of the GNSS positions G1 , G2 , G3 , transmits the learned personal route of the driver to the vehicle 1 , and suggests a destination guide of the learned personal route to the driver at a predetermined time point.

[0072] The distances E1 , E2 between each two detected successive GNSS positions G1 , G2 , G3 of the vehicle 1 , which are determined using the vehicle sensors 10 , are used for a plausibility test of the map matching result.

[0073] The length of the quickest route between two adjacent matching GNSS positions G1, G2, G3 is calculated in each case. If this length is shorter or longer than the distance E1, E2 determined using the vehicle sensor 10 by a predetermined factor, at least one of the two determined GNSS positions G1, G2, G3 of the vehicle 1 is evaluated as implausible.

[0074] Figure 3 A flow chart illustrating a computer-implemented method for map matching a GNSS position of a vehicle with location information of a digital road map.

[0075] The method comprises detecting S1 GNSS positions G1 , G2 , G3 of the vehicle 1 along a route of the vehicle 1 .

[0076] The method further comprises recording S2 a distance E1 , E2 between each two detected successive GNSS positions G1 , G2 , G3 of vehicle 1 , which distance was determined using vehicle sensors 10 , in particular wheel speed sensors.

[0077] Furthermore, the method comprises map matching S3 of the determined distances E1, E2 between each two detected successive GNSS positions G1, G2, G3 of the vehicle 1 with the route lengths L1, L2 between each two detected successive GNSS positions G1, G2, G3 of the vehicle 1 on the digital road map K.

[0078] The term "vehicle" includes cars, trucks, buses, RVs, motorcycles, etc., which are used to transport people, goods, etc.

[0079] The term includes in particular motor vehicles for transporting people. Additionally or alternatively, a hybrid vehicle or electric vehicle can be a pure electric vehicle (BEV) or a plug-in hybrid vehicle (PHEV), depending on the embodiment. However, other drive types can also be used, for example in the form of a diesel or gasoline-driven vehicle. The vehicle can also be in the form of a rail vehicle.

[0080] Although the present invention has been shown and described in more detail by means of the illustrated embodiments, the present invention is not limited to the disclosed embodiments and a person skilled in the art may derive other variants therefrom without departing from the scope of protection of the present invention.

[0081] It is therefore clear that there are many variant possibilities. The exemplary embodiments mentioned represent only examples and they should not be understood in any way as limiting, for example, the scope of protection, the application possibilities or the configuration of the invention.

[0082] More specifically, the above description and the accompanying drawings enable a skilled person to concretely implement exemplary embodiments, wherein the skilled person, while understanding the disclosed inventive concept, may make various changes, for example regarding the function or arrangement of the various elements mentioned in an exemplary embodiment, without departing from the scope of protection defined by the claims and their legal equivalents, as further explained in the specification.

[0083] As an alternative, for example, the distance between two arbitrary GNSS positions G1 , G2 , G3 of the vehicle 1 may be map matched S3 to the route lengths L1 , L2 between every two corresponding arbitrary GNSS positions G1 , G2 , G3 of the vehicle 1 on the digital road map K.

Claims

1. A computer-implemented method for map matching a GNSS position (G1, G2, G3) of a vehicle (1) with location information (P1a, P1b, P1c, P2a, P2b, P3a, P3b) of a digital road map (K), the method comprising the steps of: Detecting (S1) a GNSS position (G1, G2, G3) of a vehicle (1) along a route of the vehicle (1); recording (S2) a distance (E1, E2) between each two detected successive GNSS positions (G1, G2, G3) of the vehicle (1), determined using the vehicle sensor (10); and map matching (S3) a determined distance (E1, E2) between each two consecutive detected GNSS positions (G1, G2, G3) of the vehicle (1) with a route length (L1, L2) between each two consecutive detected GNSS positions (G1, G2, G3) of the vehicle (1) on a digital road map (K); in, The matching candidate C in time step t is calculated using the following formula t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of is: Where r is C t,i With C t+1,j The length of the fastest route between t and t+1, d is the distance covered between the time points t and t+1, determined using the vehicle sensors (10), p or p(r , d) is the probability density, σ is the standard deviation of the detected GNSS position, i and j are numeric placeholders for location information, and the matching candidate is the location information.

2. The computer-implemented method of claim 1, wherein: In real time, after recording a journey or after recording a journey segment, map matching (S3) is performed using a vehicle-internal computing device (12) and / or a vehicle-external server (14) and a timestamp is assigned to each detected GNSS position (G1, G2, G3) of the vehicle (1).

3. The computer-implemented method according to claim 1 or 2, characterized in that: The vehicle sensor (10) is a wheel speed sensor.

4. The computer-implemented method according to claim 1 or 2, characterized in that: The standard deviation σ is determined by taking the square root of the sample variance of samples of GNSS positions ( G1 , G2 , G3 ) and vehicle sensor distance measurements over a number of trips where the route travelled is known.

5. The computer-implemented method of claim 1 or 2, wherein: A transition between matching candidates (M1a, M1b, M1c, M2a, M2b, M3a, M3b) is excluded if the route length (L1, L2) of the fastest route between the matching candidates (M1a, M1b, M1c, M2a, M2b, M3a, M3b) is shorter or longer by a predetermined factor than the distance (E1, E2) determined using the vehicle sensor (10).

6. The computer-implemented method of claim 2, wherein: The GNSS positions (G1, G2, G3), current speeds and / or current average speeds of a plurality of vehicles are periodically transmitted to a vehicle external server (14). By using the GNSS positions, current speeds and / or current average speeds of the plurality of vehicles, the vehicle external server (14) calculates traffic information and provides the traffic information to the plurality of vehicles.

7. The computer-implemented method of claim 6, wherein: The traffic information is expected arrival time and / or average speed on a road section.

8. The computer-implemented method of claim 2, wherein: Hazard data detected by the vehicle sensors is transmitted together with the GNSS position (G1, G2, G3) of the vehicle (1) to a vehicle-external server (14), which performs map matching of the hazard data and provides these data to the following vehicles (1), whose planned routes pass through the identified hazards.

9. The computer-implemented method of claim 8, wherein: The hazard data are data about slippery road surfaces, traffic accidents and / or airbag activation.

10. The computer-implemented method of claim 2, wherein: The driver's personal route is recorded as a sequence of GNSS positions (G1, G2, G3) and sent to a vehicle-external server (14), which performs map matching of the GNSS positions (G1, G2, G3), transmits the learned driver's personal route to the vehicle (1), and suggests a destination guide of the learned personal route to the driver at a predetermined time point.

11. The computer-implemented method of claim 1 or 2, wherein: A distance (E1, E2) between each two consecutive GNSS positions (G1, G2, G3) detected by the vehicle (1) is used for a plausibility test of a map matching result, wherein the length of the fastest route between two adjacent matching GNSS positions (G1, G2, G3) is calculated, and if this length is shorter or longer than the distance (E1, E2) determined by the vehicle sensor (10) by a predetermined factor, at least one of the two determined GNSS positions (G1, G2, G3) of the vehicle (1) is evaluated as untrustworthy.

12. A system for map matching a GNSS position (G1, G2, G3) of a vehicle (1) with location information (P1a, P1b, P1c, P2a, P2b, P3a, P3b) of a digital road map (K), the system comprising: Means (20) for detecting the GNSS position (G1, G2, G3) of a vehicle (1) along a route of the vehicle (1); A device (22) for recording a distance (E1, E2) between every two consecutive detected GNSS positions (G1, G2, G3) of a vehicle (1), determined using a vehicle sensor (10); and Device (24) for map matching a determined distance (E1, E2) between each two consecutive detected GNSS positions (G1, G2, G3) of the vehicle (1) with a route length (L1, L2) between each two consecutive detected GNSS positions (G1, G2, G3) of the vehicle (1) on a digital road map (K); Among them, the matching candidate C in time step t is calculated using the following formula t,i Transition to matching candidate C at time step t+1 t+1,j The probability density of is: Where r is C t,i With C t+1,j The length of the fastest route between t and t+1, d is the distance covered between the time points t and t+1, determined using the vehicle sensors (10), p or p(r , d) is the probability density, σ is the standard deviation of the detected GNSS position, i and j are numeric placeholders for location information, and the matching candidate is the location information.

13. The system according to claim 12, characterized in that The vehicle sensor (10) is a wheel speed sensor. 14 . A computer-readable data carrier having a program code of a computer program for executing the method according to claim 1 when the computer program is executed on a computer.

Images