EP4220601A1

EP4220601A1 - Method and device for locating a traffic participant, and vehicle

Info

Publication number: EP4220601A1
Application number: EP22153728.5A
Authority: EP
Inventors: Shiyong Cui; Paul Abdelmalak
Original assignee: Continental Autonomous Mobility Germany GmbH
Current assignee: Continental Autonomous Mobility Germany GmbH
Priority date: 2022-01-27
Filing date: 2022-01-27
Publication date: 2023-08-02

Abstract

The invention provides a computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes. A discrete probability distribution is provided which indicates for each traffic lane a probability that the traffic participant is located on said traffic lane. Further, a likelihood function is provided which comprises for each traffic lane a probability distribution which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant is located on said traffic lane. The discrete probability distribution at a next cycle is predicted, based on the discrete probability distribution at a current cycle and using a dynamic model of the traffic participant. A measured lateral position of the traffic participant is determined, based on measurement data from at least one sensor. The predicted discrete probability distribution is updated by Bayesian updating, using the likelihood function and the measured lateral position.

Description

The invention relates to a device and a computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes. The invention further relates to a vehicle comprising such a device for locating a traffic participant.
Advanced driver-assistance systems (ADAS) require a comprehensive understanding of the environment surrounding the ego vehicle. As two indispensable parts, a good knowledge of the traffic participants (TP) and the road is required. Accordingly, the traffic participants and the road must be precisely and accurately detected and modeled.
Common driving functions, like a forward collision avoidance system and an emergency brake assist, demand a precise detection of the interrelation between traffic participants and the road model. The road model typically comprises several traffic lanes that are detected by different sensors. The most critical interrelation between the traffic participants and the road model is the association of traffic participants to the traffic lanes on which they are moving or driving. In other words, one needs to know the traffic lane that each traffic participant occupies.
According to one solution, the geometric relation between a traffic participant and a traffic lane can be calculated by using polygons, which tells the traffic lane occupancy for that traffic participant. However, a drawback of this approach is that it does not compute the uncertainty in the traffic lane assignment, i.e., the amount of trust in the assignment. Furthermore, as the sensor detections of traffic participants and road traffic lanes have much uncertainty, it is not possible to tell that a traffic participant is moving on a certain traffic lane with reliable confidence. In addition, it is hardly feasible to track these geometric assignments. Consequently, pure geometric approaches have problems with reliability and robustness.
Kühnl et al., "Visual ego-vehicle traffic lane assignment using Spatial Ray features," 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia, 2013, pp. 1101-1106, relates to a method for ego-lane index estimation, using only a monocular camera. However, the approach relates only to the ego lane without considering other traffic participants and other traffic lanes.
A general approach of assigning traffic participants to traffic lanes is therefore needed which can also quantify how much the assignment can be trusted.
In view of the above, it is therefore an object of the present invention to provide a way for assigning traffic participants to traffic lanes which can also quantify the reliability of the assignment.
In accordance with the invention, a computer-implemented method and a device for locating a traffic participant on a traffic lane of a plurality of traffic lanes, a vehicle, a computer program product and a non-transitory, computer-readable storage medium as recited in the independent claims are provided. Various preferred features of the invention are recited in the dependent claims.
According to a first aspect, therefore, the invention provides a computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes. A discrete probability distribution is provided which indicates for each traffic lane a probability that the traffic participant is located on said traffic lane. Further, a likelihood function is provided which comprises for each traffic lane a probability distribution which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant is located on said traffic lane. The discrete probability distribution at a next cycle is predicted, based on the discrete probability distribution at a current cycle and using a dynamic model of the traffic participant. A measured lateral position of the traffic participant is determined, based on measurement data from at least one sensor. The predicted discrete probability distribution is updated by Bayesian updating, using the likelihood function and the measured lateral position.
According to the second aspect, the invention provides a device for locating a traffic participant on a traffic lane of a plurality of traffic lanes. The device comprises an interface configured to receive measurement data from at least one sensor. The device further comprises a computing device configured to provide a discrete probability distribution which indicates for each traffic lane a probability that the traffic participant is located on said traffic lane. The computing device is further configured to provide a likelihood function which comprises for each traffic lane a probability distribution which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant is located on said traffic lane. The computing device is further configured to predict the discrete probability distribution at a next cycle, based on the discrete probability distribution at a current cycle and using a dynamic model of the traffic participant. The computing device is further configured to determine a measured lateral position of the traffic participant based on the measurement data received from said at least one sensor. The computing device is further configured to update the predicted discrete probability distribution by Bayesian updating, using the likelihood function and the measured lateral position.
According to a third aspect, the invention provides a vehicle with a device for locating a traffic participant on a traffic lane of a plurality of traffic lanes according to the second aspect.
According to a fourth aspect, the invention provides a computer program product comprising executable program code configured to, when executed by a computing device, perform the computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes according to the first aspect.
According to a fifth aspect, the invention provides a non-transitory, computer-readable storage medium comprising executable program code configured to, when executed by a computing device, perform the computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes according to the first aspect.
The invention allows to consistently and smoothly track the association of a traffic participant to the associated traffic lane. The invention uses a statistical way to determine the location of traffic participants. For each traffic lane, a probability can be computed that a respective traffic participant is located on said traffic lane. Therefore, not only is the traffic lane determined that a traffic participant is associated to, but the probability of said association is also computed. The invention therefore provides all uncertainties in the traffic lane associations by a probabilistic distribution.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, each traffic participant is described by a Markov chain model. The Markov chain model, comprises two kinds of random variables that are connected by directed arrows. The first random variables are hidden variables Xt that need to be inferred by using the measurements, representing the traffic lane association for the corresponding traffic participant (i.e. the discrete probability distribution). The second random variables Yt correspond to the measurements, which are the measured signals like the position and other relevant signals that describe a traffic participant.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the dynamic model is represented by a state transition matrix. The state transition matrix indicates the probability of finding the traffic participant at a next cycle in a second state, given that the traffic participant has been in the current cycle in a first state. Herein, the states are defined by the respective discrete probability distributions.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the state transition matrix is determined using a machine learning algorithm. The machine learning algorithm may comprise a maximum likelihood method, artificial neural network or the like. The machine learning algorithm may be trained using the training data obtained by real driving data.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the dynamic model depends on a road model modelling the traffic lanes. The dynamic model may take the number of the traffic lanes, the type of the traffic lanes (e.g. turning lane or motorway access), a width of the traffic lanes, a type of road and/or traffic rules (e.g. if overtaking is allowed or not) into account. The probability of finding the traffic participant in a second state, given that the traffic participant has previously been in the first state, can depend on said parameters. For instance, if overtaking is not allowed, the probability of finding the traffic participant in a next cycle on an opposite traffic lane is very low. As another example, if the traffic participant is currently driving on an acceleration lane joining a motorway, the probability of moving in a next cycle to an adjacent lane is relatively high. Herein, the exact position of the traffic participant on the acceleration lane can be taken into account. For example, the probability of changing traffic lanes increases when the traffic participant is approaching an end of the acceleration lane.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the dynamic model depends on a velocity of the traffic participant. For example, the probability of changing lanes can reduce with increasing velocity.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the likelihood function comprises for each traffic lane a Gaussian distribution. The Gaussian distribution can be centered at a center of the traffic lane in the lateral direction. A Gaussian distribution models the assumption that the probability of measuring a location of the traffic participant at or close to the center of the traffic lane is relatively high and decreases the farther away the lateral distance is from the center of the traffic lane.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, a variance of the Gaussian distribution depends on a width of said traffic lane. Taking the width of the traffic lane into account takes care of the fact that the traffic participant has more freedom to move inside the traffic lane if the width of the traffic lane is higher.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, for each traffic lane, the variance of the Gaussian distribution is proportional to the width of said traffic lane. A proportionality factor can be predetermined but can also depend on additional parameters, e.g., a driving velocity of the traffic participant.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the measurement data is obtained from at least one sensor of an ego vehicle, wherein the traffic participant is located in a surrounding of the ego vehicle. The sensors may comprise radar sensors, cameras sensors, lidar sensors and the like.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the method is performed by a driver assistance system of said ego vehicle.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, the method further comprises controlling at least one function of an ego vehicle based on the discrete probability distribution. For example, a driver assistance system of the ego vehicle may provide features like lane changing, lane keeping or collision avoidance by taking the current discrete probability distribution into account.
According to a further embodiment of the computer-implemented method for locating the traffic participant on a traffic lane of the plurality of traffic lanes, a plurality of traffic participants are associated with respective traffic lanes. In this case, each traffic participant is treated separately according to the method outlined above and explained further below in the illustrated embodiments.
For a more complete understanding of the invention and the advantages thereof, exemplary embodiments of the invention are explained in more detail in the following description with reference to the accompanying drawing figures, in which like reference characters designate like parts and in which:

Fig. 1: schematically illustrates a block diagram of a vehicle with a device for locating a traffic participant in a traffic lane of a plurality of traffic lanes according to an embodiment of the invention;
Fig. 2: shows an exemplary Markov chain model used for locating the traffic participant on a traffic lane;
Fig. 3: shows an exemplary traffic scenario for illustrating measurement models used for locating the traffic participant on a traffic lane;
Fig. 4: illustrates a flow diagram of a method for locating a traffic participant in a traffic lane of a plurality of traffic lanes according to an embodiment of the invention;
Fig. 5: schematically illustrates a block diagram illustrating a computer program product according to an embodiment of the invention; and
Fig. 6: schematically illustrates a block diagram illustrating a non-transitory, computer-readable storage medium according to an embodiment of the invention.

The accompanying drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification. The drawings illustrate particular embodiments of the invention and together with the description serve to explain the principles of the invention. Other embodiments of the invention and many of the attendant advantages of the invention will be readily appreciated as they become better understood with reference to the following detailed description.
It will be appreciated that common and well understood elements that may be useful or necessary in a commercially feasible embodiment are not necessarily depicted in order to facilitate a more abstracted view of the embodiments. The elements of the drawings are not necessarily illustrated to scale relative to each other. It will further be appreciated that certain actions and/or steps in an embodiment of a method may be described or depicted in a particular order of occurrences while those skilled in the art will understand that such specificity with respect to sequence is not actually required. It will also be understood that the terms and expressions used in the present specification have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective areas of inquiry and study, except where specific meanings have otherwise been set forth herein.
Figure 1 schematically illustrates a block diagram of a vehicle 1 with a device 2 for locating a traffic participant in a traffic lane of a plurality of traffic lanes. Herein, the traffic participants can be other vehicles, cyclists, or the like.
The device 2 comprises an interface 4 and a computing device 5. The computing device 5 can comprise at least one of a central processing unit (CPU) or graphics processing unit (GPU) like a microcontroller (µC), an integrated circuit (IC), an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), a digital signal processor (DSP), a field programmable gate array (FPGA) and the like. The computing device 5 may further comprise a storage device which may be a volatile or non-volatile data memory, e.g. a solid-state disk, memory card or the like.
The interface 4 can be a wireless or wired connection, e.g. a CAN bus or the like. The device 2 receives recent measurement data from vehicle sensors 3. Herein, the sensors 3 may comprise radar sensors, cameras sensors, lidar sensors and the like.
The computing device 5 is configured to locate the traffic participant based on the sensor data. The computing device 5 locates the traffic participant based on a Markov chain model as will be explained in the following.
Figure 2 illustrates the structure of the Markov chain model used for locating the traffic participant on a traffic lane. The Markov chain model is a probabilistic graphic model with a chain shape. If a plurality of traffic participants is considered, each traffic participant has a corresponding Markov chain model.
In the Markov chain model, there are two kinds of random variables that are connected by directed arrows. The first hidden variables Xt need to be inferred by using the measurements, representing the traffic lane association for the corresponding traffic participant. The hidden variables Xt describe the current state of the traffic participant and are given by a discrete distribution at a given cycle (or time stamp) t which indicates for each traffic lane a probability that the traffic participant is located on said traffic lane.
The second variables are variables Yt corresponding to the measurements obtained from the vehicle sensors 3. The measurements may comprise a position and other relevant signals that describe the traffic participant.
At each cycle, the computing device 5 infers an optimal value for Xt in the sense of probability, using the measurements that are obtained up to this cycle. The optimal value is given by the posterior discrete probability distribution p(x_t | y_t).
In order to compute the posterior distribution p(xt | yt), the computing device 5 first associates each node in the chain with a probabilistic model. The first variables Xt are associated with a dynamic model p(xt | x_t-1). The dynamic model characterizes how the traffic lane association changes from one cycle to the next cycle. The dynamic model can be represented by a state transition matrix Pij = p(xj | x_i), denoting the probability to transit from state i at the current cycle to state j at the next cycle. Herein, the state corresponds to the discrete probability distribution of the traffic participant at a given cycle.
The second variables Yt are associated with a measurement model p(yt | xt). The measurement model shows how the measurements are generated, given a value of the current hidden state variable. The measurement model can be represented by a Gaussian distribution given a value to the traffic lane association, i.e., knowing on which traffic lane the traffic participant is moving. The measurement model defines a likelihood function which comprises for each traffic lane a probability distribution which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant is located on said traffic lane.
The dynamic model and the measurement model can each either be modeled precisely or learned using artificial intelligence or machine learning algorithms from training data.
For each measurement cycle, the computing device 5 first predicts the discrete probability distribution at a next cycle, based on the discrete probability distribution at the current cycle and using the dynamic model of the traffic participant. At the current cycle, the estimation of the traffic lane association p(x_t-1 | y_t-1) is available. The computing device 5 may predict the traffic lane association p(xt | y_t-1) in the next cycle using the sum-product rule, i.e., integrating after multiplying the state transition matrix.
The computing device 5 then analyzes the latest measurement data received from the sensors 3. The computing device 5 computes a measured lateral position of the traffic participant based on the measurement data. The computation may involve fusion of sensor data from different vehicle sensors 3. The computation may also involve additional information, such as a road model.
The computing device 5 then updates the predicted discrete probability distribution by Bayesian updating, using the likelihood function and the measured lateral position. Herein, the traffic lane association is updated by applying the Bayesian update rule to the predicated traffic lane association, using the measurement model as the likelihood function. The computing device thereby computes the updated estimate of the traffic lane association, i.e., p(xt | yt) which is the posterior discrete probability distribution.
In Figure 1, the device 2 for locating a traffic participant in a traffic lane of a plurality of traffic lanes has been illustrated as being a component of the vehicle 1. According to further embodiments, at least some or even all of the functions of the device 2 can be carried out by devices outside of the vehicle. For instance, the data can be transmitted to a remote server. According to further embodiments, sensor data from a plurality of vehicles can be obtained by a remote server and the locations of the traffic participants is determined by first fusing the sensor data into fused sensor data and by updating the predicted discrete probability distribution using the fused sensor data.
Figure 3 shows an exemplary traffic scenario for illustrating measurement models used for locating the traffic participant on a traffic lane. In this exemplary case, there are four traffic lanes and each traffic lane has an associated measurement model, which describes the statistical distribution of the traffic participants TP1 to TP4 on that traffic lane. The measurement model for each traffic lane is given by a Gaussian distribution 61 to 64, located in the middle of the traffic lane, where most traffic participants TP1 to TP4 drive. The variance can be selected proportional to a traffic lane width.
Figure 4 illustrates a flow diagram of a method for locating a traffic participant in a traffic lane of a plurality of traffic lanes. The method can be carried out by a computing device, such as the computing device 5 of the device 2 for locating a traffic participant on a traffic lane of a plurality of traffic lanes described above with reference to Figure 1.
In a first step S1, a discrete probability distribution is provided which indicates for each traffic lane a probability that the traffic participant is located on said traffic lane. The discrete probability distribution can be part of a Markov chain model, describing the first (hidden) random variables Xt.
In a second step S2, a likelihood function is provided which comprises for each traffic lane a probability distribution which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant is located on said traffic lane. The likelihood function can correspond to a measurement model of the Markov chain model. The likelihood function may comprise a Gaussian distribution for each traffic lane. The variance of the Gaussian distribution may depend on a width of said traffic lane. For example, the variance can be proportional to the width of the traffic lane.
Given the discrete probability distribution at a certain time stamp (cycle), the discrete probability distribution at a next cycle is predicted, S3, based on the discrete probability distribution at the current cycle and using a dynamic model of the traffic participant. The dynamic model can be represented by a state transition matrix. The state transition matrix can be computed exactly, based on the modeling assumptions. According to further embodiments, the state transition matrix is determined using a machine learning algorithm. The dynamic model may depend on a road model modelling the lane.
In step S4, a measured lateral position of the traffic participant is determined. In order to compute the lateral position, measurement data from at least one vehicle sensor 3 is received. The sensor data may be received from a plurality of vehicle sensors 3 and may then first be fused into fused sensor data. The vehicle sensors 3 may comprise vehicle sensors of an ego vehicle 1, for instance vehicle cameras, radar sensors, lidar sensors and the like.
Based on the sensor data or fused sensor data, the lateral position can be obtained for example by projecting three-dimensional or two-dimensional coordinates on the lateral dimension. The lateral direction can be defined as being orthogonal to the driving direction of the vehicle 1.
In step S5, the predicted discrete probability distribution is updated by Bayesian updating, using the likelihood function and the measured lateral position.
The probability distribution at each cycle may be used by a driver assistance system to control vehicle functions, S6. For example, at least some of adaptive cruise control, lane keeping, lane changing, collision avoidance, collision warning and the like may be performed, taking the present probability distribution into account.
Figure 5 schematically illustrates a block diagram illustrating a computer program product P comprising executable program code PC. The executable program code PC is configured to perform, when executed (e.g. by a computing device 5 described above), the computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes according to the first aspect.
Figure 6 schematically illustrates a block diagram illustrating a non-transitory, computer-readable storage medium M comprising executable program code MC configured to, when executed (e.g. by a computing device), perform the computer-implemented method for locating a traffic participant on a traffic lane of a plurality of traffic lanes according to the first aspect.
Although specific embodiments of the invention have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that a variety of alternate and/or equivalent implementations exist. It should be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration in any way. Rather, the foregoing summary and detailed description will provide those skilled in the art with a convenient road map for implementing at least one exemplary embodiment, it being understood that various changes may be made in the function and arrangement of elements described in an exemplary embodiment without departing from the scope as set forth in the appended claims and their legal equivalents. Generally, this application is intended to cover any adaptations or variations of the specific embodiments discussed herein.
In this document, the terms "comprise", "comprising", "include", "including", "contain", "containing", "have", "having", and any variations thereof, are intended to be understood in an inclusive (i.e. non-exclusive) sense, such that the process, method, device, apparatus or system described herein is not limited to those features or parts or elements or steps recited but may include other elements, features, parts or steps not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, the terms "a" and "an" used herein are intended to be understood as meaning one or more unless explicitly stated otherwise. Moreover, the terms "first", "second", "third", etc. are used merely as labels, and are not intended to impose numerical requirements on or to establish a certain ranking of importance of their objects.

List of Reference Signs

1: vehicle
2: device
3: vehicle sensor(s)
4: interface
5: computing device
61-64: likelihood functions
M: non-transitory, computer-readable storage medium
MC: program code
P: computer program
PC: program code
S1-S6: method steps
TP1-TP4: traffic participants
Xi: first hidden variables
Yi: second hidden variables

Claims

A computer-implemented method for locating a traffic participant (TP1-TP4) on a traffic lane of a plurality of traffic lanes, the method comprising the steps of:
providing (S1) a discrete probability distribution which indicates for each traffic lane a probability that the traffic participant (TP1-TP4) is located on said traffic lane;

providing (S2) a likelihood function which comprises for each traffic lane a probability distribution (61-64) which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant (TP1-TP4) is located on said traffic lane;

predicting (S3) the discrete probability distribution at a next cycle, based on the discrete probability distribution at a current cycle and using a dynamic model of the traffic participant (TP1-TP4);

determining (S4) a measured lateral position of the traffic participant (TP1-TP4) based on measurement data from at least one sensor (3); and

updating (S5) the predicted discrete probability distribution by Bayesian updating, using the likelihood function and the measured lateral position.
The method according to claim 1, wherein the dynamic model is represented by a state transition matrix.
The method according to claim 2, wherein the state transition matrix is determined using a machine learning algorithm.
The method according to any of the preceding claims, wherein the dynamic model depends on a road model modelling the traffic lanes.
The method according to any of the preceding claims, wherein the dynamic model depends on a velocity of the traffic participant (TP1-TP4).
The method according to any of the preceding claims, wherein, for each traffic lane, the likelihood function comprises a Gaussian distribution.
The method according to claim 6, wherein, for each traffic lane, a variance of the Gaussian distribution depends on a width of said traffic lane.
The method according to claim 7, wherein, for each traffic lane, the variance of the Gaussian distribution is proportional to the width of said traffic lane.
The method according to any of the preceding claims, wherein the measurement data is obtained from at least one sensor (3) of an ego vehicle (1), and wherein the traffic participant (TP1-TP4) is located in a surrounding of the ego vehicle (1).
The method according to claim 9, wherein the method is performed by a driver assistance system of said ego vehicle (1).
The method according to any of the preceding claims, further comprising controlling at least one function of an ego vehicle (1) based on the discrete probability distribution.
A device (2) for locating a traffic participant (TP1-TP4) on a traffic lane of a plurality of traffic lanes, comprising:
an interface (4) configured to receive measurement data from at least one sensor (3); and

a computing device (5) configured to:
provide a discrete probability distribution which indicates for each traffic lane a probability that the traffic participant (TP1-TP4) is located on said traffic lane;

provide a likelihood function which comprises for each traffic lane a probability distribution (61-64) which indicates the probability for each lateral position that said lateral position is measured, under the assumption that the traffic participant (TP1-TP4) is located on said traffic lane;

predict the discrete probability distribution at a next cycle, based on the discrete probability distribution at a current cycle and using a dynamic model of the traffic participant (TP1-TP4);

determine a measured lateral position of the traffic participant (TP1-TP4) based on the measurement data received from said at least one sensor (3); and

update the predicted discrete probability distribution by Bayesian updating, using the likelihood function and the measured lateral position.
A vehicle (1) with a device (2) for locating a traffic participant (TP1-TP4) on a traffic lane of a plurality of traffic lanes according to claim 12.
A computer program product (P) comprising executable program code (PC) configured to, when executed by a computing device (5), perform the method according to any of claims 1 to 11.
A non-transitory, computer-readable storage medium (M) comprising executable program code (MC) configured to, when executed by a computing device (5), perform the method according to any of claims 1 to 11.