Patent Publication Number: US-2023154317-A1

Title: Method for controlling a mobility system, data processing device, computer-readable medium, and system for controlling a mobility system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to pending EP patent application serial number 21209078.1, filed Nov. 18, 2021 and entitled “METHOD FOR CONTROLLING A MOBILITY SYSTEM, DATA PROCESSING DEVICE, COMPUTERREADABLE MEDIUM, AND SYSTEM FOR CONTROLLING A MOBILITY SYSTEM,” the entirety of which is hereby incorporated by reference herein. 
     TECHNICAL FIELD 
     The present disclosure relates to a method for controlling a mobility system, the mobility system comprising a roadway infrastructure and a plurality of participants being able to travel within the roadway infrastructure. 
     Furthermore, the disclosure is directed to a data processing device comprising means for carrying out the above method. 
     Moreover, the disclosure relates to a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the above method. 
     Additionally, the disclosure is directed to a system for controlling a mobility system. 
     BACKGROUND 
     Mobility systems can be controlled on a participant-level which can also be designated a device-level or agent-level. In this case, each participant executes control functionalities, wherein it is also possible to take into account the presence and/or properties of other participants. Such control functionalities can rely on the communication of control parameters between participants, so-called participant-to-participant communication. 
     The participants of the mobility system include for example vehicles. 
     SUMMARY 
     It is an objective of the present disclosure to improve the control of a mobility system. Especially, the control of the mobility system shall be improved in respect of operational safety and operational efficiency. 
     The problem is at least partially solved or alleviated by the subject matter of the independent claims of the present disclosure, wherein further examples are incorporated in the dependent claims. 
     According to a first aspect, there is provided a method for controlling a mobility system, the mobility system comprising a roadway infrastructure and a plurality of participants being able to travel within the roadway infrastructure, comprising:
         providing a representation of the roadway infrastructure in the form of a graph having a plurality of nodes and a plurality of edges, each of the edges connecting two out of the plurality of nodes, wherein each of the nodes represents a road segments of the roadway infrastructure and each of the edges represents a travel path, and wherein each of the road segments is described by at least one road segment attribute of the corresponding node and each of the travel paths is described by at least one travel path attribute of the corresponding edge,   receiving at least one travel experience parameter from at least one of the plurality of participants, the travel experience parameter describing directly or indirectly a travel route within the roadway infrastructure having been traveled by the participant or currently being travelled by the participant, and/or receiving at least one infrastructure experience parameter from an element of the roadway infrastructure, and   determining an influence of the travel experience parameter and/or the infrastructure experience parameter on at least one of the road segment attribute or the travel path attribute and amending the at least one of the road segment attribute and the travel path attribute such that the influence of the at least one travel experience parameter and/or the infrastructure experience parameter is reflected in at least one of an amended road segment attribute or an amended travel path attribute.       

     In the present context, the roadway infrastructure can alternatively be called a road network. The representation of the roadway infrastructure or the road network may be stored in a data processing device, e.g. a computer. Such a representation can be designated a digital twin. 
     The participants of the mobility system is any entity being able to travel within the roadway infrastructure. The participants can be motorized or non-motorized. For example, the participants of the mobility system can include at least one of a vehicle, a pedestrian or a cyclist. 
     In the representation, the nodes of the graph describe road segments, i.e. static aspects of the roadway infrastructure. The edges of the graph describe movements between the road segments, thus, each edge describes a journey from one road segment to another road segment. Consequently, both static and dynamic aspects of the roadway infrastructure can be represented. In this context, a graph is an efficient way for representing a roadway infrastructure in a memory of a data processing device, e.g. a computer or control unit. The at least one road segment attribute or the at least one travel path attribute allow for representing details of the roadway infrastructure. 
     The graph may be stored in a graph database. 
     In an example, the graph may be a cluster graph. This provides the advantage that patterns between different types of data or different types of attributes can be identified efficiently. 
     The travel experience parameter is to be understood as an alphanumeric value describing a traveling activity within the real roadway infrastructure. In this context, the participant may be seen as a sensor and the travel experience parameter may be considered a sensor value which is detected by the sensor. If the travel experience parameters of a plurality of participants are used, the acquisition of these parameters can be designated crowdsourcing. Alternatively or additionally, the travel experience parameter may comprise information relating to at least one of a type of vehicle, e.g. electric vehicle, a size of the vehicle, a drive mode used in the vehicle or historic travel date of the vehicle. 
     Also the infrastructure experience parameter is to be understood as an alphanumeric value. In the present context, an element of the roadway infrastructure can be seen as a sensor and the infrastructure experience parameter can be seen as a sensor value being provided by the respective infrastructure element. If more than one infrastructure element provides infrastructure experience parameters, also these parameters can be considered to be crowdsourced. Exemplary infrastructure elements being able to provide infrastructure experience parameters may include a weather station being able to provide information about current weather conditions, a base station of a mobile communication network being able to provide information about a number of communication devices being located in its field of detection, a ticketing system of a public transport means being able to provide an information about a number of tickets being purchased during a predefined time range, a parking sensor being able to provide an information about its operational state, i.e. occupied or not, or a ticketing system of an event location being able to provide an information about a number of persons entering the event location. An event location is for example a football stadium, a theater, a concert hall or a multi-purpose hall. 
     By determining an influence of the travel experience parameter on at least one of the road segment attribute at the travel path attribute and by amending the at least one of the road segment attribute or the travel path attribute accordingly, the representation of the roadway infrastructure is kept up-to-date and accurate. In doing so, a representation error, i.e. differences between the real roadway infrastructure and its representation, is reduced. In other words and again considering the participant to be a sensor, the representation of the roadway infrastructure is fed by sensor values. This leads to a representation with high precision and actuality. 
     The basic idea underlying the present disclosure has two aspects. 
     The first aspect comprises representing the roadway infrastructure of the mobility system using a graph. In this graph the nodes represent road segments of the roadway infrastructure. The edges of the graph represent travel paths between the road segments. This type of representation has the advantage that movements and corresponding parameters, i.e. parameters characterizing the movement, can be represented within the graph. In other words, each edge of the graph describes a journey from one road segment being represented by a node delimiting the edge to another road segment being represented by another node delimiting the edge. Consequently, information relating to the movement between road segments can be stored in the graph in an efficient manner. Moreover, such data can be retrieved and processed in a computationally efficient manner. The possibility to efficiently process the data is advantageous if optimization methods are to be used which use such data as input parameters. 
     It is noted that a representation by a graph can be changed dynamically. Since the roadway infrastructure of the mobility system is represented by the graph, it is possible to reflect changes in the roadway infrastructure therein. Thus, when using optimization methods, changes in the infrastructure can be taken into account and used as a control measure for the mobility system, e.g. the switching of traffic lights or the deployment of speed bumps. 
     Moreover, this makes it possible to adapt the infrastructure in a way that selected participants can enjoy prioritized travel, e.g. emergency vehicles. This concept can also be extended to environmentally friendly participants or participants fulfilling different, predefined criteria. 
     In other words, regulations of the mobility system can be imposed in a dynamic way. 
     The second aspect relates to the fact that the participants forming part of the mobility system acquire and provide travel experience parameters. The same applies to elements of the roadway infrastructure being able to acquire and provide infrastructure experience parameters. These parameters describe real situations of the participants and the infrastructure respectively. The travel experience parameters and the infrastructure experience parameters can be acquired and provided in real time. The parameters are used to improve the representation of the roadway infrastructure. Generally speaking, the travel experience parameters are acquired on the participant-level or device-level and then aggregated on a system-level. The same is true for the infrastructure experience parameters. Subsequently, the improved representation can be at least partially provided to the participants and/or elements of the roadway infrastructure. Generally speaking, the improved representation can be provided to any type of an external entity. In this context, an external entity can be understood as any entity that is external to the entity comprising the improved representation. This means that insights gained on the system-level can be provided to the participant-level or, more generally speaking, to the external entity. Moreover, on the system-level the representation and the travel experience parameters can be used for running data analysis and optimizations. The results thereof can be provided to the participants as travel objective parameters or to the elements of the roadway infrastructure as infrastructure objective parameters. In other words, insights generated on system-level are provided to the participant-level or the infrastructure. Consequently, a closed control loop can be provided between the system-level and the participant-level and/or infrastructure-level. In other words, a so-called plan-do-check-act-cycle can be established at the level of the mobility system. 
     The disclosure is especially directed to mobility systems comprising autonomous or partially autonomous vehicles. The above-mentioned effects are particularly advantageous in such systems since the efficient storage and processing of data relating to movements can easily be performed by standard control units of autonomous or partially autonomous vehicles. 
     According to an example, the method comprises providing the at least one of the amended road segment attribute or the amended travel path attribute to an external entity or deriving at least one travel objective parameter from the at least one of the amended road segment attribute or the amended travel path attribute and providing the travel objective parameter to the external entity. Again, the external entity is to be understood as an entity external to the one comprising the amended road segment attribute or the amended travel path attribute or the travel objective parameter. 
     In this example, the method covers two alternatives. In the first alternative at least one of the amended road segment attribute or the amended travel path attribute is provided to the external entity. This means that the external entity disposes of an improved representation of the roadway infrastructure. Consequently, the external entity can take for example better traveling decisions. Thus, the operational safety and efficiency of the external entity is improved. 
     If the external entity is an autonomous vehicle, the traveling decisions can be taken autonomously and if the external entity is a vehicle driven by a human driver, the human driver can be assisted in taking better traveling decisions. 
     In the second alternative, a travel objective parameter is derived from the at least one of the amended road segment attribute or the amended travel path attribute. The travel objective parameter is provided to the external entity. The travel objective parameter for example comprises an instruction on how to travel. Also in this alternative operational safety and efficiency of the external entity are improved. 
     The amended travel path attribute, the amended road segment attribute and/or the travel objective parameter can be provided to the external entity via a data push or a data pull. 
     In an example, the method is at least partially performed periodically. Consequently, the representation of the roadway infrastructure is periodically updated and adapted to the conditions in the real roadway infrastructure. 
     The external entity may be at least one of a traffic monitoring system, at least one of the plurality of participants, a cloud service or an external unit. Again, the external entity is to be understood as an entity being external to the entity providing the relevant information or data. Thus, the insights as explained above can be used within the mobility system on a participant level and on a system level. Moreover, a use outside the mobility system is possible. More generally, the insights revealed in any portion of the mobility system can be used in any other portion thereof or even outside the mobility system. The travel objective parameter may for example be communicated to an emergency coordination center being an external unit. In this context, the travel objective parameter may comprise the information about an accident that has already happened or information about a high accident risk. In the latter case, corresponding emergency vehicles may already be prepared. 
     In an example, providing the travel objective parameter may comprise applying at least one of a pattern recognition technique, a statistical analysis, or a machine learning technique to at least one of the amended road segment attribute or the amended travel path attribute. It is of course possible to apply the at least one of a pattern recognition technique, a statistical analysis or a machine learning technique to a plurality of amended road segment attributes and/or a plurality of amended travel path attributes. Also, the at least one of a pattern recognition technique, a statistical analysis or a machine learning technique can be applied to all available amended road segment attributes and/or all available amended travel path attributes. In doing so, patterns, statistical relations and/or correlations can be detected which provide insights into the mobility system. These insights are then transformed into the travel objective parameter. Consequently, the participants of the mobility system can take into account these insights when travelling within the roadway infrastructure. 
     In general, the at least one of a pattern recognition technique, a statistical analysis or a machine learning technique being applied to a graph can be designated a graph analytics technique. 
     Using the at least one of a pattern recognition technique, a statistical analysis or a machine learning technique can be used for making predictions about a future state of the mobility system. 
     The machine learning technique comprises for example a link prediction technique or a graph embedding technique. Also a graph neural network can be used. 
     The travel objective parameter may comprise at least one of a warning message, a travelling behavior recommendation, a travelling behavior instruction or a travelling behavior limitation. The travel objective parameter may be adapted to be used in a participant being a fully autonomous vehicle, in a partially autonomous vehicle and in a non-autonomous vehicle, i.e. a vehicle which is fully controlled by a human driver. A warning message may comprise a warning of a slippery road, high accident risk, and incident on the road, a traffic jam etc. A traveling behavior recommendation may comprise a speed recommendation or a route recommendation. Also, a traveling behavior instruction and a traveling behavior limitation may relate to speed or a route. However, an instruction may differ from a recommendation in that the instruction cannot be overruled. A limitation may relate to an interval which is closed at one side only, i.e. a maximum speed. Independent from the specific aspects to which the travel objective parameter is directed, this parameter enhances travel security and operational efficiency of the mobility system. In an illustrative scenario, the travel objective parameter relates to a maximum speed. This reduces the risk of accidents. In an alternative scenario, the travel objective parameter may relates to a speed range guaranteeing smooth participant flow within the mobility system. If a defined speed is communicated to a plurality of participants in the form of a travel objective parameter, the speed of the individual participants can be adopted such that on a system-level and optimum participant flow is reached. This can be designated a connected cruise control. 
     The road segment attribute and the amended road segment attribute each may comprise at least one of an identification parameter, a location parameter, a road class parameter, a road geometry parameter, a road friction parameter, a traffic flow parameter, a traffic regulation parameter, an accident probability parameter or a hazard probability parameter. In this context, and identification parameter is an alphanumeric value being suitable for identifying the corresponding road segment. A location parameter comprises information regarding the location of the corresponding road segment. The road class parameter defines a road-class to which the corresponding road segment belongs. Exemplary road classes are highway, city roads, dirt track, sidewalk, cycle path, multiple carriageway, single carriageway, roundabout, traffic square, sliproad. A road geometry parameter characterizes the geometry of the road, e.g. in the form of a polynomial curve. A road friction parameter characterizes the friction of the corresponding road segment. The friction may vary according to weather conditions, e.g. rain and ice. The road friction parameter may be a number between 0 and 1, wherein 0 means there is no friction at all and 1 means that frictional force equals normal force. The traffic flow parameter characterizes the corresponding road segment by its capacity for traffic flow. In one example the traffic flow parameter relates to a number of lanes on the corresponding road segment. Alternatively, the traffic flow parameter may relate to a speed on the corresponding road segment or a number of participants that can travel the road segment during a predefined time unit. A traffic regulation parameter is related to a traffic regulation feature of the corresponding road segment. Such a traffic regulation feature may be a traffic light or a deployable speed bump. By amending the traffic regulation parameter, an operational state or a way of operating of the traffic regulation feature may be adapted. An accident probability parameter is a parameter characterizing the risk that an accident happens on the corresponding road segment. Analogously, a hazard probability parameter characterizes the risk that a hazard happens, e.g. an animal on the road or an object on the road. Thus, using the road segment attribute and the amended road segment attribute, the corresponding road segment can be described in the relevant aspects. This leads to an accurate and useful representation of the road segment. 
     The travel path attribute and the amended travel path attribute each may comprise at least one of an identification parameter, a neighbor description parameter, a travel probability parameter or an intersection angle parameter. In the present context, an identification parameter is an alphanumeric value being suitable for identifying the travel path. A neighbor description parameter is a parameter describing the nodes, i.e. the road segments, limiting the edge, i.e. the travel path. The neighbor description parameter may for example comprise the identification parameters of the adjacent road segments. The nature of a travel probability parameter can best be understood if a travel path is not the only travel path starting from an adjacent road segment. In such a case the travel probability parameter defines the probability by which the participant will take a specific one of the travel paths. Obviously, the probabilities of all travel paths starting from one road segment add up to one. Moreover, if only one travel path starts from a road segment, the corresponding travel probability is one. An intersection angle parameter characterizes an angle between 0° and 360° by which the road segments being connected by the travel path meet each other. Using at least one of the above parameters leads to an accurate and useful representation of the travel paths within a roadway infrastructure. 
     The travel experience parameter may comprise at least one of a position parameter or a travel speed parameter. In other words, the travel experience parameter may comprise a record of the positions that the corresponding participant had while traveling within the roadway infrastructure. Alternatively or additionally, the travel experience parameter may comprise a record of speed at which the corresponding participant was traveling within the roadway infrastructure. By providing the travel experience parameters of a plurality of participants, an overview of the positions and speed of the participants within the roadway infrastructure can be generated. Analyzing the travel experience parameters offers insights into the mobility system. For example a traffic jam can be detected in a portion of the roadway infrastructure where a lot of participants are located which do not travel at all or only travel at very low speed. 
     In an example, the road segment attribute and the travel path attribute are jointly analyzed for providing the travel objective parameter. This means that both information regarding the structure and information regarding the movements within the roadway infrastructure are considered for providing the travel objective parameter. For example, a road closure is a static information about the roadway infrastructure. In such a case, additional information about the movements of the participants can be considered, i.e. it can be considered which alternative route is taken by the majority of the participants. The travel objective parameter can thus, for example, point to a different alternative route. In a case where there is no further alternative, the travel objective parameter may recommend a speed which is suitable for providing fluent traffic on the alternative route, even though it is overloaded with participants. 
     In another example, at least one of the road segments is described by at least one historic road segment attribute. Alternatively or additionally, at least one of the travel paths is described by at least one historic travel path attribute. In this context, the historic road segment attribute relates to the same type of information as the road segment attribute which can be designated a current road segment attribute. The same applies to the historic travel path attribute. Consequently, the road segments and/or the travel paths are described by both historic and current attributes. Consequently, the current attributes can be compared to the corresponding historic attributes. In doing so, amendments in the attributes are comparatively easy to detect. 
     In an example, the method comprises receiving an external mobility parameter. Such a parameter is received from an external unit and describes at least one aspect of the mobility system. An exemplary external mobility parameter relates to a road closure information or a construction work information which can be provided by a city government. Another exemplary external mobility parameter relates to weather information being provided by an external weather data provider. A further exemplary external mobility parameter can relate to a calendar event being associated to a certain location within the mobility system, e.g. a football match in a football stadium. 
     An influence of the external mobility parameter on at least one of the road segment attribute or the travel path attribute may be determined and the at least one of the road segment attribute or the travel path attribute may be amended such that the influence of the external mobility parameter is reflected in the corresponding amended road segment attribute or the corresponding amended travel path attribute. Additionally, the at least one of the amended road segment attribute or the amended travel path attribute may be provided to the external entity. It is also possible to derive at least one travel objective parameter from the at least one of the amended road segment attribute or the amended travel path attribute and provide the travel objective parameter to the external entity. Consequently, the external mobility parameter is used to update the representation of the mobility system. In other words, the representation can be extended and enhanced by information resulting from the external mobility parameter. Such information can be helpful to increase the operational efficiency of the mobility system. In an illustrative situation, the external mobility parameter relates to a date and time when a football match ends in a football stadium. Thus, at that time the travel objective parameter will direct participants that are just passing the football stadium along a circumvention road which is not prone to traffic jam. 
     In a further example, the travel experience parameter received from a specific participant, the resulting influence of the travel experience parameter on at least one of the road segment attribute or the travel path attribute and the amended road segment attribute and the amended travel path attribute may be collected in a participant profile being attributed to the specific participant. Thus, a participant profile is created which comprises information relating to one specific participant. This profile can for example be used in order provide participant-specific or personalized routes through the roadway infrastructure. For example, from the data collected in the participant profile, it may be recognizable that highways are preferred over city roads. Such a preferences can subsequently be utilized when calculating routes. 
     The method may be at least partly computer-implemented, and may be implemented in software or in hardware, or in software and hardware. Further, the method may be carried out by computer program instructions running on means that provide data processing functions. The data processing means may be a suitable computing means, such as an electronic control module etc., which may also be a distributed computer system. The data processing means or the computer, respectively, may comprise one or more of a processor, a memory, a data interface, or the like. 
     According to a second aspect, there is provided a data processing device comprising means for carrying out the method of the present disclosure. For example, the data processing device is a centralized data processing device which is superordinate to the participants of the mobility system. Using such a data processing device leads to enhanced operational safety and operational efficiency at system level. This means that the mobility system can be operated at or close to a global optimal as compared to local optimal arms in a scenario where the operation of each single participant is optimized. 
     The data processing device can be a computer. 
     According to a third aspect, there is provided a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of the present disclosure. Such a computer-readable medium can be used to improve operational safety and operational efficiency of the mobility system. 
     According to a fourth aspect, there is provided a system for controlling a mobility system, comprising the data processing device of the present disclosure. Moreover, the system comprises a plurality of participant-level data acquisition devices, each of the participant-level data acquisition devices being attributable to a participant and being configured to generate and provide at least one travel experience parameter to the data processing device of the present disclosure. Alternatively or additionally, the system comprises a plurality of infrastructure-level data acquisition devices being attributable to an element of the roadway infrastructure and being configured to generate and provide at least one infrastructure experience parameter to the data processing device of the present disclosure. Consequently, the mobility system is controlled in a way that operational safety and operational efficiency are enhanced. Furthermore, such a system can control the mobility system in a computationally efficient manner. 
     If the participant is a vehicle, the participant-level data acquisition device may be locatable inside the vehicle. For example, the participant-level data acquisition device is mountable inside the vehicle. 
     In case the participant is a pedestrian or a cyclist, the participant-level data acquisition device may just be carried by the participant. 
     The participant-level data acquisition device is for example a mobile phone or a tablet. It can also be a computer device or a control unit. 
     It should be noted that the above examples may be combined with each other irrespective of the aspect involved. Accordingly, the method may be combined with structural features and, likewise, the apparatus and the system may be combined with features described above with regard to the method. 
     These and other aspects of the present disclosure will become apparent from and elucidated with reference to the examples described hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Examples of the disclosure will be described in the following with reference to the following drawings. 
         FIG.  1    shows a mobility system and a system for controlling the mobility system in accordance with the present disclosure, the system for controlling the mobility system comprising a data processing device according to the present disclosure having a computer-readable medium according to the present disclosure, wherein the computer-readable medium comprises instructions which cause the data processing device to carry out a method according to the present disclosure, 
         FIG.  2    shows steps of the method according to the present disclosure, 
         FIG.  3    shows a representation of the roadway infrastructure in the form of a graph which is stored on the computer-readable medium of  FIG.  1   , 
         FIG.  4    illustrates the creation of a user profile for a specific participant of the mobility system of  FIG.  1   . 
     
    
    
     The figures are merely schematic representations and serve only to illustrate examples of the disclosure. Identical or equivalent elements are in principle provided with the same reference signs. 
     DETAILED DESCRIPTION 
       FIG.  1    shows a mobility system  10  comprising a roadway infrastructure  12  and a plurality of participants  14   a ,  14   b ,  14   c  being able to travel within the roadway infrastructure  12 . 
     In the examples illustrated in the Figures, the participants  14   a ,  14   b ,  14   c  are vehicle. It is understood that this is purely illustrative and does not mean that other kinds of participants are excluded. For better understanding, the reference signs  14   a ,  14   b ,  14   c  will also be attributed to these vehicles. As has been explained before, the participants  14   a ,  14   b ,  14   c  are examples of external entities. 
     For better visibility, the size of the vehicles  14   a ,  14   b ,  14   c  has been artificially increased with respect to the roadway infrastructure  12 . 
     It is understood that the number of three vehicles  14   a ,  14   b ,  14   c  is purely illustrative. It is, of course, possible to have a lot more vehicles traveling within the roadway infrastructure  12 . 
     The roadway infrastructure  12  comprises a network  16  of roads  18   a  to  18   f . These roads  18   a  to  18   f  are connected via intersections  20   a ,  20   b ,  20   c.    
     in the exemplary roadway infrastructure  12  shown in  FIG.  1   , road  18   a  is a highway and the remaining roads  18   b  to  18   f  are city roads. 
     Furthermore,  FIG.  1    shows a system  22  for controlling the mobility system  10 . The system  22  may therefore also be called a control system. 
     The system  22  comprises a data processing device  24  having a data processing unit  26  and a data storage unit  28 . The data processing unit  26  and the data storage unit  28  are communicatively connected such that data being stored in the data storage unit  28  can be processed by the data processing unit  26 . Additionally, data processing results computed in the data processing unit  26  may be stored in the data storage unit  28 . 
     In the present example, the data processing device  24  is a central data processing unit being located on system-level. The data processing device  24  is also an example of a traffic monitoring system. It can be implemented as a cloud service. 
     The system  22  additionally comprises three participant-level data acquisition devices  30   a ,  30   b ,  30   c . The participant-level data acquisition device  30   a  is mounted in vehicle  14   a . The participant-level data acquisition device  30   b  is mounted in vehicle  14   b . The participant-level data acquisition device  30   c  is mounted in vehicle  14   c.    
     Since in the present example, the participants  14   a ,  14   b ,  14   c  are vehicles  14   a ,  14   b ,  14   c , the participant-level data acquisition devices  30   a ,  30   b ,  30   c  can also be called vehicle-level data acquisition devices  30   a ,  30   b ,  30   c.    
     As has already been explained with respect to the vehicles  14   a  to  14   c , also the number of participant-level data acquisition devices  30   a  to  30   c  is purely illustrative. 
     Each of the participant-level data acquisition devices  30   a  to  30   c  is communicatively connected to the data processing device  24  via a respective wireless data connection  32   a ,  32   b ,  32   c . The wireless data connections  32   a ,  32   b ,  32   c  are bidirectional. 
     The system  22  further comprises an exemplary infrastructure-level data acquisition device  31  being attributable to an element of the roadway infrastructure  12  as will be explained later. 
     The infrastructure-level data acquisition device  31  is communicatively connected to the data processing device  24  via a respective wireless data connection  33 . The wireless data connection  33  is bidirectional. 
     The infrastructure-level data acquisition device  31  is configured to generate and provide at least one infrastructure experience parameter  55  to the data processing device  24 . 
     The data processing device  24  also comprises an interface  34  to an external unit  36  which will be explained in detail further below. Since the external unit  36  does not form part of the system  22 , it is represented in dashed lines. 
     The data processing device  24  comprises means for carrying out a method for controlling the mobility system  10 . 
     In more detail, the storage unit  28  of the data processing device  24  comprises a computer-readable medium  38  comprising instructions which cause the data processing device  24  to carry out the methods for controlling the mobility system  10  when being executed. 
     Generally speaking the data processing device  24  can be designated a computer. 
     The steps of the method for controlling the mobility system  10  are represented in  FIG.  2   . 
     In a first step S 1 , a representation of the roadway infrastructure  12  in the form of a graph  40  is provided. 
     Such a graph  40  is represented in  FIG.  3   . To facilitate the understanding of the representation performed by the graph  40 , the graph  40  is shown as an overlay over the roadway infrastructure  12 . For better visibility, the details of the roadway infrastructure  12  are only equipped with reference signs in  FIG.  1   . 
     The graph  40  has a plurality of nodes  42   a  to  42   m . Each of the nodes  42   a  to  42   m  represents a road segment of the roadway infrastructure  12 . 
     Moreover, each of the road segments is described by an exemplary road segment attribute  44   a  to  44   m  and an exemplary historic road segment attribute  46   a  to  46   m  being represented as attributes of the corresponding node  42   a  to  42   m.    
     The graph  40  further has a plurality of edges  48   a  to  48   l , each of the edges  48   a  to  48   l  connecting two out of the plurality of nodes  42   a  to  42   m , wherein each of the edges  48   a  to  48   l  represents a travel path within the roadway infrastructure  12 . 
     Each of the travel paths is further described by an exemplary travel path attribute  50   a  to  50   l  and an exemplary historic travel path attribute  52   a  to  52   l  being represented as attributes of the corresponding edges  48   a  to  48   l.    
     In a second step S 2  of the method, exemplary travel experience parameters  54   a ,  54   b ,  54   c  are received from each of the vehicles  14   a ,  14   b ,  14   c  (cf.  FIGS.  1  and  2   ). Alternatively or additionally, the exemplary infrastructure experience parameter  55  is received from the element of the roadway infrastructure  12 . 
     This is done via the corresponding wireless data connection  32   a ,  32   b ,  32   c ,  33 . 
     Each of the travel experience parameters  54   a ,  54   b ,  54   c  is generated by the corresponding participant-level data acquisition device  30   a ,  30   b ,  30   c . In other words, each of the participant-level data acquisition devices  30   a ,  30   b ,  30   c  is configured to generate and provide at least one travel experience parameters  54   a ,  54   b ,  54   c  to the data processing device  24 . 
     The travel experience parameters  54   a ,  54   b ,  54   c  directly or indirectly describe a travel route within the roadway infrastructure  12  having been traveled by the corresponding vehicle  14   a ,  14   b ,  14   c  or currently being travelled by the vehicle  14   a ,  14   b ,  14   c.    
     In a third step S 3  of the method for controlling the mobility system  10 , an influence of the travel experience parameters  54   a ,  54   b ,  54   c  and/or the infrastructure experience parameter  55  on the road segment attributes  44   a  to  44   m  and the travel path attributes  50   a  to  50   l  is determined (cf.  FIG.  2   ). 
     If an influence has been detected, the relevant road segment attributes  44   a  to  44   m  and the relevant travel path attributes  50   a  to  50   l  are amended such that the influence of the travel experience parameters  54   a ,  54   b ,  54   c  and/or the infrastructure experience parameter  55  is reflected therein. 
     In other words, the representation in the form of the graph  40  is updated. 
     Subsequently, in a fourth step S 4  (cf.  FIG.  2   ), the amended road segment attributes  44   a  to  44   m  and the amended travel path attributes  50   a  to  50   l  are provided to the vehicles  14   a ,  14   b ,  14   c  such that the vehicles  14   a ,  14   b ,  14   c  dispose of an updated representation of the roadway infrastructure  12  and can use this representation for making travelling decisions. 
     Alternatively or additionally, the a travel objective parameter  56   a ,  56   b ,  56   c  for each vehicle  14   a ,  14   b ,  14   c  is derived from the amended road segment attributes  44   a  to  44   m  and the amended travel path attributes  50   a  to  50   l . These travel objective parameters  56   a ,  56   b ,  56   c  are provided to the corresponding vehicle  14   a ,  14   b ,  14   c  via the corresponding wireless data connection  32   a ,  32   b ,  32   c  (cf.  FIGS.  1  and  2   ). 
     It is also possible that an infrastructure objective parameter  57  is derived and provided to the element of the roadway infrastructure  12  being equipped with the infrastructure-level data acquisition device  31 . 
     In order to derive the travel objective parameters  56   a ,  56   b ,  56   c , and/or the infrastructure objective parameter  57 , the road segment attributes  44   a  to  44   m  and the travel path attributes  50   a  to  50   l  are analyzed jointly. 
     Optionally, also the historic road segment attributes  46   a  to  46   m  and the historic travel path attributes  52   a  to  52   l  are considered in the analysis, e.g. as further input parameters. 
     Analyzing these attributes may comprise the application of a pattern recognition technique, a statistical analysis or a machine learning technique. 
     Also, in a step S 5 , the method comprises receiving an external mobility parameter  58  (cf.  FIGS.  1  and  2   ). 
     The external motility parameter  58  is provided by the external unit  36 . 
     Steps S 2  and S 5  may be performed in parallel or jointly. 
     Subsequently, in a step S 6 , an influence of the external mobility parameter  58  on the road segment attributes  44   a  to  44   m  and the travel path attributes  50   a  to  50   l  is determined and the road segment attributes  44   a  to  44   m  and the travel path attributes  50   a  to  50   l  are amended such that the influence of the external mobility parameter  58  is reflected. 
     Steps S 3  and S 6  may be performed in parallel or jointly. 
     Moreover, in a step S 7 , the amended road segment attributes  44   a  to  44   m  and the amended travel path attribute  50   a  to  50   l  are provided to the vehicles  14   a ,  14   b ,  14   c.    
     This can be done in parallel or jointly to the performance of step S 4 . 
     Alternatively or additionally, travel objective parameters  56   a ,  56   b ,  56   c  may be derived from the amended road segment attributes  44   a  to  44   m  and the amended travel path attributes  50   a  to  50   l . The travel objective parameters  56   a ,  56   b ,  56   c  are then provided to the respective vehicle  14   a ,  14   b ,  14   c.    
     This can also be done jointly with the provision of the travel objective parameters  56   a ,  56   b ,  56   c  being derived from the amended road segment attributes  44   a  to  44   m  and the amended travel path attributes  50   a  to  50   l  which have been amended due to the experience parameters  54   a ,  54   b ,  54   c.    
     Moreover, a travel objective parameter  60  may be provided to the external unit  36 . The travel objective parameter  60  may be equal to one of the travel objective parameters  56   a ,  56   b ,  56   c , however this is not necessarily the case. 
     In the following, several simplified application scenarios will be explained. 
     The first application scenario relates to route planning. In more detail, the representation of the roadway infrastructure  12  shall contain information which route the vehicles  14   a ,  14   b ,  14   c  will most probably take. This so-called most probable route can be used for navigating an autonomous vehicle through the roadway infrastructure  12 . It is also possible to use the most probable route in a navigation unit of a vehicle being driven by a human driver. 
     In this application scenario, all travel path attributes  50   a  to  50   l  comprise a travel probability parameter. In cases, where more than one edge  48   a  to  48   l  originates from a node  42   a  to  42   m , the corresponding travel probability parameter of the travel paths are each inferior to one. This is for example the case for edges  48   b ,  48   f  and  48   g.    
     In an initial situation, the travel probability parameter of edge  48   b  may be 0.6, the travel probability parameter of edge  48   f  may be 0.2 and the travel probability parameter of edge  48   g  may be 0.1. 
     When traveling through the roadway infrastructure  12 , each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b  and  54   c . In the present application scenario, the travel experience parameters  54   a ,  54   b  and  54   c  comprises a position parameter. In other words, the travel experience parameters  54   a ,  54   b  and  54   c  comprise an information on which route the respective vehicle  14   a ,  14   b ,  14   c  has taken. 
     Using again the example of node  42   b , the travel experience parameters  54   a ,  54   b ,  54   c  may show that the probability parameters of the edges  48   b ,  48   f  and  48   g  do not correctly reflect the behavior of the vehicles  14   a ,  14   b ,  14   c.    
     Consequently, the probability parameters of the edges  48   b ,  48   f  and  48   g  may be updated such that for example, the travel probability parameter of edge  48   b  may be 0.5, the travel probability parameter of edge  48   f  may be 0.1 and the travel probability parameter of edge  48   g  may be 0.4. 
     A second application scenario relates to a so-called connected cruise control. 
     In this context, the road segment attributes  44   e ,  44   f  and  44   h  of nodes  42   e ,  42   f  and  42   h  may comprise a traffic flow parameter having the form of a standard speed for traveling these road segments. 
     Again, each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b ,  54   c . In the present application scenario, the travel experience parameter  54   a ,  54   b ,  54   c  comprises a position parameter and a corresponding travel speed parameter. 
     Consequently, it is noticeable that the vehicles  14   a ,  14   b ,  14   c  traveling on travel paths being represented by edges  48   i  and  48   k  travel at a speed lower than the standard speeds. 
     Consequently, knowing the number of vehicles  14   a ,  14   b ,  14   c  traveling the road segments being represented by nodes  42   e ,  42   f ,  42   h , optimal travel speeds can be determined if a security distance in function of the travel speed and the length of the road segments is known. 
     This optimal travel speed can be provided to each of the vehicles  14   a ,  14   b ,  14   c  in the form of a travel objective parameter  56   a ,  56   b ,  56   c  being a travelling behavior recommendation. 
     In this application scenario, the flow of vehicles  14   a ,  14   b ,  14   c  is optimized on a system-level. 
     A third application scenario is about accident risk. 
     In this application scenario, the road segment attributes  44   a  to  44   m  comprise an accident probability parameter. 
     As before, each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b ,  54   c . In the present application scenario, the travel experience parameter  54   a ,  54   b ,  54   c  comprise a position parameter and a corresponding travel speed parameter. 
     When analyzing the travel experience parameters  54   a ,  54   b ,  54   c , e.g. by pattern recognition, it can be found that at least for some of the road segments being represented by nodes  42   a  to  42   m , the accident probability parameter is too low. 
     Consequently, these road segment attributes  44   a  to  44   m  can be updated, wherein the accident probability parameter is increased. 
     As a further consequence, a travel objective parameter  56   a ,  56   b ,  56   c  comprising a warning message of increased accident risk is provided to each of the vehicles  14   a ,  14   b ,  14   c . Moreover, a travelling behavior limitation in the form of a speed limit may be provided as a travel objective parameter  56   a ,  56   b ,  56   c.    
     A fourth application scenario is about hazard warning. 
     In this context, the road segment attributes  44   a  to  44   m  may comprise a traffic flow parameter having the form of a standard speed for traveling the corresponding road segments. 
     Again, each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b ,  54   c . In the presence application scenario, the travel experience parameter comprises a position parameter and a corresponding travel speed parameter. 
     Consequently, it is noticeable that the vehicles  14   a ,  14   b ,  14   c  traveling on a specific road segment, e.g. the one being represented by node  42   k , stop travelling. 
     It can be concluded that a hazard, e.g. an animal or obstacle, is present on the road segment corresponding to this node, e.g.  42   k.    
     As a consequence thereof, a travel objective parameter  56   a ,  56   b ,  56   c  comprising a warning message may be provided to each of the vehicles  14   a ,  14   b ,  14   c.    
     Furthermore, vehicles  14   a ,  14   b ,  14   c  traveling on the road segment being represented by node  42   j  may be instructed to reduce speed. 
     As a further consequence, the travel path attribute  50   f  concerning the travel probability of the travel path being represented by edge  48   f  may be significantly reduced such that vehicles  14   a ,  14   b ,  14   c  circumvent the location of the hazard. 
     A fifth application scenario is about a participant profile being a vehicle profile in the present example. This is illustrated in  FIG.  4   . 
     In this application scenario, the travel experience parameter, e.g.  54   a , received from a specific vehicle, e.g. vehicle  14   a , is collected in a participant profile being attributed to the specific vehicle. 
     Moreover, the resulting influence of the travel experience parameter  54   a  on at least one of the road segment attributes  44   a  to  44   m  or the travel path attributes  50   a  to  50   l  is stored in a participant profile being attributed to the specific vehicle. 
     Consequently, graph  40  can be amended using the above-mentioned influences. In doing so, a participant-specific graph  40   a  is created which is a specific to vehicle  14   a.    
     In  FIG.  4   , the creation of the participant-specific graph  40   a  based on graph  40  is schematically illustrated. 
     A sixth application scenario relates to dynamic mobility regulations. This application scenario is a variant of the second application scenario. 
     However, in contrast to the second application scenario, now the road segment attribute  44   h  of node  42   h  may comprise a traffic regulation parameter having the form of a switching frequency of a traffic light. 
     Again, each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b ,  54   c . In the present application scenario, the travel experience parameter  54   a ,  54   b ,  54   c  comprises a position parameter and a corresponding travel speed parameter. Thus, it is noticeable that the vehicles  14   a ,  14   b ,  14   c  traveling on travel paths being represented by edges  48   i  and  48   k  risk to build up a traffic jam. This is for example noticed by a travel speed having a low average but a high variance, i.e. the vehicles  14   a ,  14   b ,  14   c  brake and accelerate a lot. 
     Consequently, the switching frequency of the traffic light can be adapted such that smooth traffic flow is guaranteed on the travel paths being represented by edges  48   i  and  48   k.    
     In a variant of the sixth application scenario also the infrastructure experience parameter  55  is used. In the present application scenario the corresponding element of the roadway infrastructure is a ticketing system of a football stadium, more precisely the ticketing system of a parking lot of the football stadium. When analyzing the infrastructure experience parameter  55  it is noticeable that a lot of vehicles are leaving the parking lot. Thus, the switching frequency of the traffic light can also take into account this circumstance and guarantee smooth traffic. 
     A seventh application scenario relates to dynamic speed control. 
     Now the road segment attribute  44   f  of node  42   f  may comprise a traffic regulation parameter having the form of a deployment height of a deployable speed bump. 
     Again, each of the vehicles  14   a ,  14   b ,  14   c  generates and provides a travel experience parameter  54   a ,  54   b ,  54   c . In the present application scenario, the travel experience parameter  54   a ,  54   b ,  54   c  comprises a position parameter and a corresponding travel speed parameter. Thus, it is noticeable that the vehicles  14   a ,  14   b ,  14   c  exceed the speed limit by far when using the travel paths being represented by edges  48   i  and  48   k.    
     Consequently, the deployment height of the deployable speed bump can be increased such that the vehicles  14   a ,  14   b ,  14   c  need to reduce the corresponding travel speed. 
     Other variations to the disclosed examples can be understood and effected by those skilled in the art in practicing the claimed disclosure, from the study of the drawings, the disclosure, and the appended claims. In the claims the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items or steps recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope of the claims. 
     LIST OF REFERENCE SIGNS 
     
         
           10  mobility system 
           12  roadway infrastructure 
           14   a - 14   c  participant, vehicle 
           16  network 
           18   a - 18   f  road 
           20   a ,  20   b ,  20   c  intersection 
           22  system for controlling a mobility system 
           24  data processing device 
           26  data processing unit 
           28  data storage unit 
           30   a ,  30   b ,  30   c  participant-level data acquisition device 
           31  infrastructure-level data acquisition device 
           32   a ,  32   b ,  32   c  wireless data connection 
           33  wireless data connection 
           34  interface 
           36  external unit 
           38  computer-readable medium 
           40  graph 
           40   a  participant-specific graph 
           42 - 42   m  node 
           44   a - 44   m  road segment attribute 
           46   a - 46   m  historic road segment attribute 
           48   a - 48   l  edge 
           50   a - 50   l  travel path attribute 
           52   a - 52   l  historic travel path attribute 
           54   a ,  54   b ,  54   c  travel experience parameter 
           55  infrastructure experience parameter 
           56   a ,  56   b ,  56   c  travel objective parameter 
           57  infrastructure objective parameter 
           58  external mobility parameter 
           60  travel objective parameter 
         S 1  method step 
         S 2  method step 
         S 3  method step 
         S 4  method step 
         S 5  method step 
         S 6  method step 
         S 7  method step