Dynamic allocation of resources between communication and detection for intelligent traffic systems

Abstract

One aspect of the present disclosure relates to a method for allocating available resources in relation to communication and detection functions of objects, wherein each object out of two or more objects is a vehicle, a road infrastructure or another device designed to detect its respective surroundings. The method comprises receiving information relating to performance detection properties of the two or more objects, from the two or more objects, wherein each of the two or more objects has one or more performance detection properties and is designed to provide information about its one or more performance detection properties. The method furthermore comprises calculating an adjustment parameter for at least one object from the two or more objects based on the information received in relation to the performance detection properties of the two or more objects. The adjustment parameter is intended to be used to allocate an available resource for the at least one object between a communication function and a detection function of this object. Finally, the method comprises transmitting the calculated adjustment parameter to the at least one object in order to allocate an available resource for the at least one object between a communication function and a detection function of this object based on the transmitted adjustment parameter.

Description

[0001] Dynamic allocation of resources between communication and sensing for intelligent transport systems

[0002] Technical area

[0003] The present invention relates to techniques for allocating available resources related to the communication and sensing functions of objects. Related aspects relate to a computer program, a module, and two or more objects.

[0004] background

[0005] Recently, vehicles and / or road infrastructures (e.g. ‘intelligent transport systems’ such as intelligent vehicles, intelligent road infrastructures, etc.) have been increasingly used in road transport, the cooperation of which can make a significant contribution to making road transport more efficient, safer and more environmentally friendly, in particular by providing comprehensive, up-to-date and easily accessible information to road users (in some cases also to other road users which are not intelligent transport systems) and by enabling targeted and dynamic traffic management and control.

[0006] However, some state-of-the-art methods are not capable of dynamically and adaptively allocating the available resources of intelligent systems (e.g., available time and / or power) to achieve optimal cooperative performance of these intelligent systems and / or optimize their energy consumption, which may be necessary to achieve the aforementioned goals. Furthermore, some state-of-the-art methods are not capable of fully exploiting the performance-sensing characteristics of intelligent transportation systems to allocate these available resources. Therefore, there is a need to develop new techniques for allocating available resources of vehicles, road infrastructure, or other devices that can solve some or all of the aforementioned problems.

[0007] Summary of the invention

[0008] A first general aspect of the present disclosure relates to a method for allocating available resources related to the communication and sensing functions of objects, wherein each of two or more objects is a vehicle, road infrastructure, or other device configured to sense its respective environment. The method includes receiving information related to performance sensing characteristics of the two or more objects from the two or more objects, wherein each of the two or more objects has one or more performance sensing characteristics and is configured to provide information about its one or more performance sensing characteristics.Furthermore, the method comprises calculating an adaptation parameter for at least one of the two or more objects based on the information received regarding the performance acquisition properties of the two or more objects. The adaptation parameter is to be used to allocate an available resource for the at least one object between a communication function and an acquisition function of this object. Finally, the method comprises sending the calculated adaptation parameter to the at least one object in order to allocate an available resource for the at least one object between a communication function and an acquisition function of this object based on the sent adaptation parameter.

[0009] A second general aspect of the present disclosure relates to a computer program configured to carry out the method according to the first general aspect of the present disclosure. The present disclosure also relates to a computer-readable medium (e.g., a machine-readable storage medium such as an optical storage medium or read-only memory, e.g., FLASH memory) and signals storing or encoding the computer program of the present disclosure.

[0010] A third general aspect of the present disclosure relates to a module configured to execute the method for allocating available resources with respect to the communication and sensing functions of objects according to the first general aspect and / or to execute the computer program according to the second general aspect.

[0011] A fourth general aspect of the present disclosure relates to two or more objects, wherein each of two or more objects is a vehicle, road infrastructure, or other device. The two or more objects of the fourth aspect are configured to sense their respective environment and send information to a module according to the third aspect. Furthermore, at least one object of the two or more objects is configured to receive a calculated adaptation parameter for the at least one of the two or more objects from the module. Furthermore, the at least one object is configured to allocate an available resource for the at least one object between a communication function and a sensing function of that object based on the calculated adaptation parameter.

[0012] The techniques of the first to fourth general aspects may have one or more of the following advantages.

[0013] First, compared to some prior art techniques, the techniques of the present disclosure offer the possibility of optimally allocating the available resources of objects, e.g., intelligent transportation systems such as intelligent vehicles, intelligent road infrastructures, or other devices, between communication and sensing functions of these intelligent transportation systems based on their performance-sensing characteristics.

[0014] Second, the present techniques can make it possible to collect information about the performance sensing characteristics of intelligent transport systems (which can, for example, change in real time) and, based on this information, to dynamically adjust the allocation of available resources between communication and sensing functions for these intelligent transport systems. In particular, an intelligent transport system can use fewer available resources for communication with other road users (e.g., other intelligent transport systems) and more available resources for sensing (e.g., to improve its performance sensing characteristics) if, at a given time, its performance sensing characteristics have deteriorated (e.g., compared to the performance sensing characteristics of other intelligent transport systems at the same time).On the other hand, an intelligent transport system can spend fewer available resources on sensing and more available resources on communicating with other road users (e.g., to transmit sensed traffic-related information to them) if, at a certain point in time, its performance sensing characteristics have improved (e.g., compared to the performance sensing characteristics of the other intelligent transport systems at the same point in time).

[0015] Some terms are used in this disclosure as follows:

[0016] For the purposes of this disclosure, the term “intelligent transport systems” or, in other words, “intelligent transport systems” can refer to systems that use information and communication technologies in road transport, including its road infrastructure, vehicles and users, as well as in traffic and mobility management and for interfaces with other modes of transport (see Article 4 of DIRECTIVE 2010 / 40 / EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 7 July 2010 on the framework for the deployment of intelligent transport systems in road transport and for interfaces with other modes of transport).

[0017] Accordingly, the term "object" may include a "vehicle," "road infrastructure," or other "device" (e.g., a user's mobile phone) that may be equipped with one or more sensors (for the purpose of sensing its environment). Furthermore, the "objects" of the present techniques may communicate with systems external to the object, e.g., with a "module" defined below or with other systems (e.g., cloud systems). In some cases, the "objects" within the meaning of the present disclosure may also communicate with each other; more on this below. Furthermore, "objects" may be intelligent transportation systems as defined above or may be equipped with such systems. In the following, such "objects" are sometimes referred to as "smart vehicles," "smart road infrastructures," or "smart devices."Abbreviations such as "vehicles", "road infrastructures" and "devices" are also sometimes used.

[0018] In the present disclosure, such a vehicle (e.g., an intelligent “vehicle”) may be equipped with one or more sensors, such as cameras, radar, lidar, ultrasonic, GPS, or thermal sensors, to detect its surroundings (e.g., in real time while driving). A non-exhaustive list of further units of the sensor may include CPU cores, system-on-a-chip (SoC) hardware, RAM, hard drives, processing modules (e.g., image processing modules), and communication interfaces to other devices. A vehicle may communicate with systems external to the vehicle, such as a “module,” e.g., send information to and / or receive information from the module; more on this below (communication with other systems external to the vehicle, such as cloud systems, is also conceivable). Furthermore, an “intelligent vehicle” may receive information, e.g., in the form of recorded sensor data (e.g.,in the form of image data or data series), with other "intelligent vehicles", "intelligent road infrastructures", "intelligent devices", or any combination thereof. In some cases, the "objects" defined above can communicate with other road users that are not intelligent traffic systems within the meaning of the present disclosure (e.g., send messages to them and / or receive messages from them). In some examples, a sensor of the vehicle can be used in the context of semi-autonomous, autonomous, or assisted driving of a vehicle to provide functionality for the vehicle (e.g., with regard to safety functions, the implementation of driver information, and convenience functions, such as intelligent headlight control and traffic sign information). A "sensor" of a "vehicle" can be a camera, e.g., a front and / or rear camera of a vehicle.In addition, a sensor can include a system-on-chip (SoC), which contains, for example, a microprocessor for image processing algorithms. A camera-based sensor can use one or more technical paths for image processing. The first path of such a sensor can recognize the typical appearance of road object classes such as vehicles, cyclists, or lane markings using programmed algorithms. In the second path, the camera-based sensor can use optical flow and so-called Structure from Motion (SfM) to detect raised, lane-defining road objects such as curbs, curbs, or guardrails. The movement of related pixels can be tracked. From this, a three-dimensional structure can be estimated from the two-dimensional camera image. The third path can be based on artificial intelligence (e.g.based on a computer-based machine learning system) by classifying road objects such as vehicles parked at the roadside and distinguishing road surfaces from the roadside. Within the scope of the present disclosure, it is also conceivable that a mobile network itself may play the role of a sensor in some cases and is therefore referred to herein as a "sensor." For example, the 6G network may include such sensing functionalities by allowing information about the surroundings of an "object" to be transmitted in a suitable manner from the 6G network to the "object" (e.g., in the form of communication signals that are then decoded on the object side). This technology is known as "integrated communication and sensing."

[0019] In this disclosure, "road infrastructures" (e.g., "smart road infrastructures") are understood to mean any infrastructure as defined above that is equipped with one or more sensors capable of detecting the surroundings of the corresponding road infrastructure. These sensors may, for example, be infrastructure sensors that can be indestructible devices (e.g., road-specific reflectors) that are installed or embedded in the road or its surroundings (e.g., on buildings, posts, and signs) as needed. These sensors may, for example, have (at least some of) the same technical properties as the vehicle sensors described above. Similar to a "vehicle," a "road infrastructure" can communicate with systems external to the road infrastructure, such as the "module" defined below (communication with other systems external to the road infrastructure, such as cloud systems, is also conceivable).As mentioned above, a "smart road infrastructure" can, in some cases, communicate with "smart vehicles" and / or "smart devices." Vehicle-to-infrastructure communication can be bidirectional, meaning that the road infrastructure can transmit information (e.g., CAM and / or CPM messages) to the "smart vehicle," and vice versa, the vehicle can send the information to the "smart road infrastructure." (Similar communication between a "smart road infrastructure" and a "smart device" may also be possible.)

[0020] In the present disclosure, the term "device" may be understood to refer to a device (e.g., "a smart device") that is not a "vehicle" or "road infrastructure" as defined above, but which may be relevant in the context of traffic and is equipped with one or more sensors that can detect an environment of that device. Furthermore, the "device" may communicate with systems external to the device, such as the "module" defined below (communication with other systems external to the device, such as cloud systems, is also conceivable). In some cases, a "smart device" may communicate with other "smart devices," "smart vehicles," "smart road infrastructures," or any combination thereof. The "device" may, for example, be:It could be a user's mobile phone (or other device) that has the characteristics of a "device" as defined above. For example, a "device" could be a mobile phone belonging to a pedestrian, a passenger of a vehicle (moving or stationary), etc., participating in road traffic.

[0021] The term “performance sensing properties” within the meaning of the present disclosure may refer to performance sensing properties of “objects.” A performance sensing property of an “object” (e.g., a “smart vehicle,” a “smart road infrastructure,” or a “smart device”) may be given by a performance sensing property of a sensor of the object. A non-exhaustive list of such performance sensing properties of a sensor includes a resolution, an accuracy, a required sensing or processing time (e.g., image processing time), a required communication time between a sensor and a sensor data processing unit (e.g., a vehicle computer if a sensor is deployed in a vehicle), or other sensor-related properties. A performance sensing property may be quantified and, for example,be characterized by a corresponding numerical "acquisition parameter." In this way, a number of acquisition properties of an "object" can be described. In some cases, when the object comprises more than one sensor, a performance acquisition property of an object can be characterized by a global parameter (e.g., a global "acquisition parameter") that refers to this performance acquisition property (e.g., an average value of all performance acquisition properties of the same type that characterize different sensors).

[0022] The terms “CAM messages” (Cooperative Awareness Messages) and “CPM messages” (Collective Perception Messages) as used here can refer to standardized data packets used in vehicle communication within intelligent traffic systems. A “CAM message” from an intelligent traffic system can contain one or more of the following information relating to this and / or other intelligent traffic systems: vehicle position, driving speed, direction of travel, vehicle status, traffic flow (e.g., information about traffic light phases), or other information. A CPM message can contain information about the sensors of the intelligent traffic system, such as fields of view, and the traffic objects in the vicinity of the intelligent traffic system detected by these sensors (e.g., sensors of an “intelligent vehicle”).In one example, the CPM messages may contain aggregated and processed information collected from multiple CAM messages. Within the scope of the present disclosure, it is also conceivable that an exchange of information between "intelligent vehicles," "intelligent road infrastructures," and "intelligent devices" (any combination of the involved intelligent transportation systems is conceivable) may take place via "CAM" or CPM messages. The CAM or CPM messages may be transmitted (in other words: broadcast) by an "object" (e.g., an "intelligent vehicle" or an "intelligent road infrastructure"). The CAM or CPM messages may be broadcast by the intelligent transportation system once per a predetermined time interval (e.g., once every 100 milliseconds or more, once per second or more).In some cases, the intelligent transport systems must not exceed a certain distance from each other in order to maintain communication (e.g. through CAM and / or CPM messages).

[0023] Within the scope of the present disclosure, it is also conceivable that information about the above-introduced performance sensing characteristics of a sensor of an "object" may be present in the CAM and / or CPM message, or that the CAM and / or CPM message contains information from which such performance sensing characteristics can be determined. In some cases, "objects" (e.g., "smart vehicles," "smart road infrastructures," "smart devices," or any combination thereof) may send the CAM and / or CPM messages to the "module" (see below).

[0024] In some cases, CAM messages and / or CPM messages can be exchanged between vehicles using vehicle-to-vehicle communication (V2V). For example, vehicle-to-vehicle communication can use the wireless technology of WLANs according to 802.11p. Alternatively, the CAM and / or CPM messages can be sent from a vehicle to a road infrastructure using vehicle-to-infrastructure communication (V2I). In some cases, the CAM and / or CPM messages can be exchanged via roadside units (RSU) using vehicle-to-infrastructure communication and the traffic control center. The structure of the vehicle-to-infrastructure communication can be wireless and can be based, for example, on WLANs according to 802.11a, 802.11b, or 802.11g.In some cases, vehicle-to-infrastructure communication can be based on Bluetooth or, in the case of sensor networks, on ZigBee or even on Dedicated Short Range Communication (DSRC). In other cases, the CAM or CPM messages can be transmitted via a corresponding cellular connection using a corresponding base station, which can be configured, for example, as a 5G or 6G connection (e.g., from an intelligent traffic system to the "module" or from one intelligent traffic system to another "intelligent traffic system"). In some cases where no base station is available, vehicle-to-vehicle communication and / or vehicle-to-infrastructure communication can be carried out via a direct wireless communication link between the objects (e.g.,"smart vehicles" and / or "smart road infrastructures") (such a communication connection is hereinafter referred to as "sidelink communication"). Communication between the "smart devices" defined above, as well as between the "smart devices" and the "smart vehicles" / "smart road infrastructures" can take place in a similar manner as described above in the context of vehicle-to-vehicle communication and vehicle-to-infrastructure communication.

[0025] The term "resource" can refer to any resource that, alone or together with other resources, ensures the executability of a communication function and / or a sensing function of an "object." A "resource" can, for example, be a predetermined time interval that is divided between the communication function and the sensing function of the object (e.g., dynamically in real time when the object is in operation). In another example, a "resource" can be a predetermined power that is to be distributed between both the communication function and the sensing function of the object. As explained in more detail below, a "resource" of the object can be divided (or, in other words, distributed) between the communication function and the sensing function of the object using an "adaptation parameter."A "module" within the meaning of the present disclosure can serve the purpose of calculating the "adaptation parameters" introduced above. Furthermore, the module can transmit (or, in other words, send) the adaptation parameters to respective "objects"; more on this below.

[0026] The term "vehicle" encompasses any device designed to transport passengers and / or cargo. The vehicle may be a motor vehicle (e.g., an at least partially autonomously operating / assisted motor vehicle, in particular a car or a truck). However, the vehicle may also be a ship, a train, an aircraft, or a spacecraft.

[0027] Short description of the characters

[0028] Fig. 1a is a flowchart illustrating an example of a method for allocating available resources related to the communication and sensing functions of objects according to the first aspect. Figs. 1b and 1c are flowcharts illustrating further possible method steps according to the first aspect.

[0029] Fig. 2 shows a schematic illustration of an exemplary street scene with M intelligent traffic systems, in which four of these intelligent traffic systems are outlined: three vehicles A lt A2 and Ä M and road infrastructure M -i, which capture their respective environment and send messages L2, L M-1 , and L M to a module 10. In this example, module 10 communicates with vehicle i. The other road users that are not intelligent transport systems are also shown in this figure.

[0030] Fig. 3a and 3b schematically show possible exemplary resource allocations (time interval T in Fig. 3a and the power P in Fig. 3b) between a communication function and a detection function for two consecutive cycles k and / c+1 of one of the M intelligent transport systems shown in Fig. 2.

[0031] Detailed description

[0032] First, techniques for allocating available resources with respect to the communication and sensing functions of objects are described with reference to Figs. 1a to 1c. Next, an exemplary street scene with M intelligent transportation systems, illustrated in Fig. 2, and exemplary resource allocations shown in Fig. 3 are discussed in relation to the method of the present disclosure.

[0033] As outlined in Figs. 1a to 1c, a first general aspect relates to a method (e.g., a computer-implemented method) for allocating available resources related to the communication and sensing functions of objects. The method steps of the corresponding independent claim are illustrated in the solid-line boxes in Figs. 1a to 1c, while the method steps of some dependent claims are shown in the dashed-line boxes.

[0034] In the techniques of the present disclosure, each of two or more objects is a vehicle, road infrastructure, or other device (e.g., a mobile phone of a user such as a pedestrian, vehicle occupant, etc.) configured to sense their respective surroundings. The two or more objects may be intelligent transportation systems or be equipped with such systems. In other words, the “objects” may be any combination of “smart vehicles,” “smart road infrastructure,” and “smart devices,” as defined above. For example, two or more objects may comprise a total of M objects (e.g., M may be four or more, ten or more, twenty or more), as exemplified in Fig. 2. In this figure, three “smart vehicles” A lt Ä2and Ä M and a “smart road infrastructure” M-i. The environment that can be sensed by the respective "object" may include a non-exhaustive list of any combination of other "smart vehicles," "smart road infrastructures," "smart devices," as well as other road objects and road users that are not intelligent transportation systems within the meaning of the present disclosure, such as vehicles, pedestrians, and cyclists (as illustrated in Fig. 2).

[0035] The first step of the method comprises receiving 100 information relating to performance sensing properties of the two or more objects from the two or more objects. Furthermore, each of the two or more objects has one or more performance sensing properties (e.g., two or more, three or more, five or more, or ten or more performance sensing properties) and is configured to provide information about its one or more performance sensing properties (e.g., transmit this information). As described above, a performance sensing property of an "object" may be specified based on a performance sensing property of one or more sensors of the object. This information may, for example, be received by a module 10 (see the definitions above and also the discussions below).

[0036] For this purpose, the method step “receiving information” relating to the performance acquisition properties of the two or more objects may comprise receiving 120 acquisition parameters for each object of the two or more objects (e.g., for the “objects” i, A2,..., A M-1 and A M , which are shown in Fig. 2). In this case, a detection parameter can be assigned to each performance detection property of the one or more performance detection properties of the object, thereby forming a set of detection parameters for each of the two or more objects.

[0037] In the following, in some cases, a detection parameter of an object is referred to as r™, where the index j numbers a detection parameter of an m-th object. (In other words, r™ can represent a numerical value of the j-th detection parameter of the m-th object.) The index m can range from 1 to M if "two or more objects" comprise a total of M objects. That is, The m-th object can be represented by a total of R

[0038] detection parameters such that the index j runs from 1 to R. That is, [1, .. , R]. In one example, a set of detection parameters for the m-th object can be defined as {r m}. In some cases, a number of acquisition parameters R may be the same in each set of acquisition parameters. In other words, for each of the two or more objects, the same number of acquisition parameters can be selected for the method, quantifying the corresponding performance acquisition properties of these objects. The acquisition parameters may, for example, lie in a given interval, [a, b]. (Where a and b can be arbitrary real numbers, a, b , R.) For example, the acquisition parameters may be in the interval from zero to one: [0, 1].

[0039] In a non-limiting example, it can be achieved, for example, by appropriate processing of performance acquisition properties and normalization of acquisition parameters, that the larger a acquisition parameter is, the better the performance acquisition property associated with this acquisition parameter. For example, the inequality r™ > r3 m mean that the first performance capture property of the m-th object is better than the third performance capture property of the m-th object. The inequality r2 m > r For example, 2 can mean that the second performance detection property of the m-th object is better than the second performance detection property of the n-th object.

[0040] In some cases, a set of detection parameters for the m-th object can be written as a vector. For example, the vector can be written as the following expression: FI'" = (r™, ... , r™) 7', where the symbol T corresponds to the transpose operation.

[0041] In the present techniques, receiving information related to the performance sensing characteristics of the two or more objects may include receiving messages sent by the two or more objects. These messages may, for example, be sent to and received by a module 10. In the example of Fig. 2, three "smart vehicles" A lt A2 and Ä M and a “smart road infrastructure” Mi can send corresponding messages L2, L M-1 , and L M to module 10. In some cases, messages may be the CAM messages and / or CPM messages introduced above.

[0042] Furthermore, the method comprises calculating 200 an adaptation parameter for at least one of the two or more objects (e.g., for all of the two or more objects) based on the information received regarding the performance acquisition properties of the two or more objects (e.g., this information may include CAM and / or CPM messages, as already described above). The adaptation parameter of the present techniques is intended to be used to divide an available resource for the at least one object between a communication function and an acquisition function of this object. In one case, the available resource for the object may be a predetermined time interval 20 divided between the communication function and the acquisition function of the object. In a non-limiting example of Fig. 3a, the available resource is a predetermined time interval T; 20: where a time interval T k>erf21 for the acquisition function for a cycle k, while for the same cycle k a time interval T k komm ; 22 for the communication function. The exemplary division of the predetermined time interval T; 20 between the communication function and the acquisition function for the subsequent cycle k + 1 is also shown in Fig. 3a (the cycles are discussed further below). Alternatively or additionally, the available resource for the object can be a predetermined power 30, which is divided between the communication function and the acquisition function of the object. In a non-limiting example of Fig. 3b, the available resource is a predetermined power P; 30: where a power P k>er f 31 is assigned to the detection function for a cycle k, while for the same cycle k a power P kikomm> 32 is assigned to the communication function. The exemplary distribution of the specified power P; 30 between the communication function and the detection function for the subsequent cycle k + 1 is also shown in Fig. 3b.

[0043] In the techniques of the present disclosure, calculating the adaptation parameter for the at least one object from the two or more objects may include assigning 210 a weight factor for each acquisition parameter of the set of acquisition parameters. Hereinafter, in some cases, a weight factor of an acquisition parameter T™ is referred to as f, where, as defined above, the index j numbers a acquisition parameter. For example, a weight factor fj may reflect the importance of a performance acquisition property (which, as explained above, may be described by a corresponding acquisition parameter). Therefore, in some cases, a weight factor f may be independent of the object. In one example, it may be specified (e.g., by a user) whether one performance acquisition property is more or less important compared to another performance acquisition property.Based on this information, appropriate values ​​can be assigned to the weighting factors. The weighting factors can, for example, lie in a given interval, [c, d]. (Where c and d can be any real numbers, c, d , H .) For example, the weighting factors can be in the interval from zero to one: [0, 1]. In some cases, the weighting factors can be normalized. For example, the weighting factors can be normalized as follows: E;. =1 / = 1, where R is a total number of acquisition parameters. In one non-limiting example, a first acquisition parameter may be assigned a weight factor that has a larger value than a weight factor assigned to the second acquisition parameter if the first acquisition parameter is associated with a more important performance acquisition property than the second acquisition parameter. In other words, the more important a performance acquisition property is, the larger the value of the weight factor jj associated with that performance acquisition property may be. For example, the inequality > f2means that the first performance capture property is more important than the second performance capture property.

[0044] Furthermore, calculating the adjustment parameter for the at least one object from the two or more objects may include calculating 220 a weighted sum for each object from the two or more objects. The weighted sum for the object may be a sum of products, each product being a product of a function of a detection parameter and a weight factor assigned to that detection parameter. This may determine the two or more weighted sums for the corresponding two or more objects. In some cases, the function of the detection parameter may be a linear function. For example, the product of the function of the detection parameter and the weight factor assigned to that detection parameter may be a product of the detection parameter and the weight factor assigned to that detection parameter. In one case, the weighted sum may be ß mfor the m-th object can be given by the following expression: ß m = E; =1 / T / 71 , where the indices have the same meaning as previously defined.

[0045] In the techniques of the present disclosure, calculating the adaptation parameter for the at least one object from the two or more objects may further comprise determining 230 an object with a best collective performance detection property based on the two or more weighted sums. For example, the weighted sum corresponding to the object with the best collective performance detection property may take a maximum value of the two or more weighted sums. This weighted sum may, in some cases, be denoted as ß max = max me [ iM ^ ß m be referred to.

[0046] Furthermore, calculating the adjustment parameter for the at least one object from the two or more objects may include calculating 240 a new weighted sum for the at least one object from the two or more objects based on the weighted sum for the at least one object and the weighted sum corresponding to the object with the best collective performance sensing property. In one case, calculating the new weighted sum for the at least one object may be a ratio of the weighted sum for the at least one object to the weighted sum corresponding to the object with the best collective performance sensing property. For example, the new weighted sum 9 m for the m-th object as follows: 9 m = ß m / ß max, in this case the new weighted sums can be written as a vector. For example, the vector 0 can be written by the following expression: 0 =

[0047] (ß 1 , ...,ß M ') T , where the symbol T denotes the transpose operation. An alternative expression for this vector 0 can be as follows: 0 = (e 1 , ..., 9 M y.

[0048] It should be noted that in some cases, the relationship between a performance capture property and its associated capture parameter can be defined differently than described above: e.g., the smaller a capture parameter, the better the performance capture property associated with that capture parameter can be. Furthermore, in some cases, the relationship between a capture parameter and its associated weighting factor can be defined differently: e.g., the smaller a capture parameter's weighting factor, the more important the performance capture property associated with that capture parameter can be. In this case, the expressions for the new weighted sums can be adjusted accordingly (not explained in further detail below).

[0049] Furthermore, in the present techniques, calculating the adjustment parameter for the at least one object from the two or more objects (e.g., for all of the two or more objects) may further comprise calculating 250 the adjustment parameter for the at least one object based on the calculated new weighted sum for the at least one object. In some cases, an adjustment parameter may be in a predetermined interval, [e, f]. (Where, e and f may be any real numbers, e, f H ). For example, the adjustment parameters may be in the interval from zero to one: [0, 1]. In some cases, the adjustment parameters may have the following property: The larger the calculated new weighted sum for the at least one object, the smaller the adjustment parameter for the at least one object may be. For example, the adjustment parameter a mfor the m-th object can be given by the following expression: a m = 1 - y ■ 9 m , where y > 0 is a given factor and the expression introduced above for the new weighted sum 9 m used for the mth object. In some cases, the factor y can be equal to one, y = 1.

[0050] Finally, the method includes sending 300 the calculated adaptation parameter to the at least one object (e.g., to all of the two or more objects) to allocate an available resource for the at least one object between a communication function and a detection function of that object based on the sent adaptation parameter. The allocation of the available resource between a communication function and a detection function using the adaptation parameter can be performed, for example, on the at least one object (e.g., on all of the two or more objects).

[0051] In the present disclosure, a minimum proportion of the available resource allocated for the sensing function may be zero, corresponding to no allocated resource for the sensing function, in which case a proportion of the available resource allocated for the communication function takes a maximum value, which corresponds to the situation when the available resource is allocated only to the communication function. Alternatively or additionally, a minimum proportion of the available resource allocated for the communication function may be zero, corresponding to no allocated resource for the communication function, in which case a proportion of the available resource allocated for the sensing function takes a maximum value, which corresponds to the situation when the available resource is allocated only to the sensing function.In some cases, a capture share of the available resource for the at least one object may be proportional to the calculated adjustment parameter.

[0052] For example, if the available resource is a given time interval 20, the given time interval T between the communication function and the acquisition function of the m-th object can be determined using an adaptation parameter a m for this object can be written as follows: T = a m ■ T + (1 - a m ) ■ T. The time interval T corresponds to erf = a m - T a time interval allocated for the acquisition function (according to the division described above), while T kom = (1 - a m ) ■ T corresponds to a time interval allocated for the communication function. In this non-limiting example, the adjustment parameter lies in the interval from zero to one, a me [0,1], so that the two limits of the fitting parameter, a m = 0 and a m = 1, describe two of the situations mentioned in the previous paragraph. In the same way, another (e.g., any) available resource for the object can be divided between the communication function and the acquisition function of that object. For example, if the available resource is a given power 30, the given power ? can be divided between the communication function and the acquisition function of the m-th object using an adaptation parameter a m for this object can be written as follows: P = a m ■ P + (1 - a m ) ■ P. The power P corresponds to er y = a m ■ P of a power allocated for the acquisition function (according to the allocation described above), while P kom = (1 - a m ) ■ P corresponds to a power allocated for the acquisition function.

[0053] As an alternative to the allocation described above, in which a minimum portion of the available resource allocated for the sensing function or the communication function may be zero, another possible allocation of the available resource is conceivable within the scope of the present disclosure. For the at least one object, in some cases, a static communication portion of the available resource and a static sensing portion of the available resource may be allocated, which are independent of the calculated adaptation parameter. Furthermore, a dynamic portion of the available resource may be allocated for the at least one object, which is a difference between the available resource for the object and the allocated static communication and sensing portions of the available resource for the object.This dynamic portion of the available resource for the at least one object can be divided between the communication function and the acquisition function of that object based on the calculated adaptation parameter. The dynamic portion of the available resource for the at least one object can be 20% or more, 50% or more, or 90% or more of the (total) available resource. In some cases, a acquisition portion of the dynamic portion of the available resource for the at least one object can be proportional to the calculated adaptation parameter.

[0054] For example, if the available resource is a given time interval 20, the given time interval T between the communication function and the acquisition function of the m-th object can be determined using an adaptation parameter a m for this object can be written as follows: T = T er f St + T kom st + a m - T dyn+ l — a m ) - T dyn . The time interval T er f St a static capture portion of the available resource, T kom st a static communication portion of the available resource, and T dyn a dynamic portion of the available resource (according to the distribution described above). In addition, the time interval Terf,dyn = a ™ ' Tdyn a dynamic time interval allocated for the acquisition function, while T kom dyn = (1 - a m ) ■ T dyn corresponds to a dynamic time interval allocated for the communication function. (Both dynamic time intervals depend on the adaptation parameter a m(according to the definitions above.) In the same way, another (e.g., any) available resource for the object can be divided between a communication function and a detection function of this object (e.g., for the performance P). In both described cases, the following statement can hold for the distribution of the available resource: The smaller the adaptation parameter of the m-th object a m , the better the performance acquisition property of the mth object associated with this adaptation parameter. Consequently, such an object can allocate more available resources to the communication function compared to the acquisition function. This tendency is also evident from the above example expressions:

[0055] Within the scope of the present disclosure, it is conceivable that the method can be carried out for a number of cycles, starting with the first cycle of the number of cycles (e.g., for k = 1 in the notation of Figs. 3a and 3b). In this case, the method can comprise receiving 400 information relating to the performance detection properties of the two or more objects from the two or more objects for a current cycle. This information can be received, for example, by the "module" 10 (as already discussed above). The current cycle can be, for example, the / c cycle of Figs. 3a and 3b, where k is a natural number, ke N. Furthermore, the method can comprise calculating 500 the adaptation parameter for the at least one object from the two or more objects (e.g., for all of the two or more objects) for the current cycle.The method may then include sending 600 the adaptation parameter calculated for the current cycle for the at least one object to the at least one object (e.g., to all of the two or more objects). Furthermore, the adaptation parameter calculated for the current cycle may be used for a subsequent cycle, subsequent to the current cycle, to divide the available resource for the object between the communication function and the sensing function of that object for the subsequent cycle. The subsequent cycle may, for example, be the k+1 cycle illustrated in Figs. 3a and 3b. In some cases, the available resource for the object between the communication function and the sensing function of that object may remain unchanged for each of the number of cycles (e.g., the time interval in Fig. 3a and the power P in Fig.3b remain unchanged for cycles k and k + 1). The allocation of the available resource between a communication function and a detection function using the adaptation parameter can be done on the object side (as already described above).

[0056] The various aspects of the allocation of the available resource for the case explained above for the case where no cycles were discussed may be similarly applicable to the case with cycles described in the previous paragraph.

[0057] A second general aspect of the present disclosure relates to a computer program configured to carry out the method according to the first general aspect of the present disclosure. The present disclosure also relates to a computer-readable medium (e.g., a machine-readable storage medium such as an optical storage medium or read-only memory, e.g., FLASH memory) and signals storing or encoding the computer program of the present disclosure.

[0058] A third general aspect of the present disclosure relates to a module 10 configured to execute the method for allocating available resources with respect to the communication and sensing functions of objects according to the first general aspect and / or to execute the computer program according to the second general aspect.

[0059] The "module" of the present techniques can be a part of the computer system or comprise an entire computer system. The "module" can be a hardware unit that can have its own memory, preconfigured hardware logic, one or more registers, and an internal data connection. (A register can, for example, contain or describe memory areas.) In addition, a "module" can have one or more external interfaces, e.g., to a processing core and / or to one or more communication connections.

[0060] In some cases, the "module" may be designed as a standalone module located outside of "road infrastructure," "vehicles," or other "devices." In this case, the information exchange between the "module" and "objects" introduced above may occur, for example, via a base station (e.g., 5G or 6G connection). In another example, the "module" may be integrated into a "vehicle" (e.g., its in-vehicle computer) of the two or more objects. In one case, the information exchange between the "module" and "objects" introduced above may occur via a base station (e.g., 5G or 6G connection). Alternatively or additionally, the information exchange between the "module" and "objects" may be realized using "sidelink communication" (e.g., if the base station is not present or if it is outside the range of the "objects").Within the scope of the present disclosure, it is also conceivable that more than one "vehicle" comprises a "module" (e.g., the module of the same design), so that at one time, a module of a vehicle (e.g., an intelligent vehicle) provides the information exchange between the "module" and "objects," while at another time, a module of another vehicle provides the information exchange. In other words, a "vehicle" equipped with the respective "module" can, in some cases, be dynamically selected (e.g., in real time, when the "objects" are in operation), for example, to enable optimized information exchange via "sidelink communication" between the "module" and the "objects" (e.g., to ensure information exchange between the module and all of the two or more objects).

[0061] In some cases, the "module" may be integrated into a "road infrastructure" or other "device" of the two or more objects. In this case, the exchange of information between the "module" and the "objects" may take place in a similar manner as explained above in the context of the "module" integrated into a vehicle.

[0062] In addition, the module of the third aspect may be configured to transmit the calculated adaptation parameter for the at least one object from the two or more objects to the at least one object (e.g., to all of the two or more objects).

[0063] A fourth general aspect of the present disclosure relates to two or more objects, wherein each of two or more objects is a vehicle, road infrastructure, or other device. The two or more objects may be intelligent transportation systems or may be equipped with such systems. Furthermore, the two or more objects of the fourth aspect are configured to sense their respective environments and send information to a module according to the third aspect. Furthermore, at least one object of the two or more objects is configured to receive a calculated adaptation parameter for the at least one of the two or more objects from the module. Furthermore, the at least one object is configured to allocate an available resource for the at least one object between a communication function and a sensing function of that object based on the calculated adaptation parameter.

[0064] In the techniques of the present disclosure, each of the two or more objects may be configured to determine its one or more performance detection characteristics.

[0065] Furthermore, the two or more objects of the fourth aspect may be configured to assign a detection parameter for each performance detection property of the one or more performance detection properties, thereby forming a set of detection parameters for each object of the two or more objects. Furthermore, the two or more objects of the fourth aspect may be configured to: generate a message from each object of the two or more objects containing information about the one or more performance detection properties of the object; and send the messages from the two or more objects to the module. The message (e.g., each message) may be a CAM message or a CPM message, as discussed above.

Claims

PATENT CLAIMS 1 . A method for dividing available resources with respect to the communication and sensing functions of objects, each object being used by two or more objects ( i, 2, A M-lf A M ) is a vehicle, road infrastructure, or other device designed to sense its respective environment, the method comprising: Receiving (100) information relating to performance sensing properties of the two or more objects from the two or more objects, each of the two or more objects having one or more performance sensing properties and being configured to provide information about its one or more performance sensing properties; Calculating (200) an adaptation parameter for at least one object of the two or more objects based on the information received regarding the performance detection properties of the two or more objects, wherein the adaptation parameter is to be used to divide an available resource for the at least one object between a communication function and a detection function of that object, Sending (300) the calculated adaptation parameter to the at least one object to allocate an available resource for the at least one object between a communication function and a detection function of that object based on the sent adaptation parameter.

2. The method of claim 1, wherein receiving information related to the performance acquisition properties of the two or more objects comprises receiving (120) acquisition parameters for each object of the two or more objects, wherein a acquisition parameter is assigned to each performance acquisition property of the one or more performance acquisition properties of the object, thereby forming a set of acquisition parameters for each object of the two or more objects.

3. The method of claim 2, wherein receiving information related to the performance detection properties of the two or more objects comprises receiving messages L2, L M-1 , L M ) sent by the two or more objects.

4. The method according to any one of the preceding claims 1 to 3, wherein calculating the adaptation parameter for the at least one object from the two or more objects comprises the following steps: Assigning (210) a weighting factor for each acquisition parameter of the set of acquisition parameters; Calculating (220) a weighted sum for each object of the two or more objects, the weighted sum for the object being a sum of products, each product being a product of a function of a detection parameter and a weight factor assigned to that detection parameter, thereby determining the two or more weighted sums for the corresponding two or more objects.

5. The method of claim 4, wherein calculating the adaptation parameter for the at least one object from the two or more objects further comprises the following steps: Determining (230) an object with a best collective performance detection property based on the two or more weighted sums; Calculating (240) a new weighted sum for the at least one object from the two or more objects based on the weighted sum for the at least one object and the weighted sum corresponding to the object with the best collective performance detection property.

6. The method of claim 5, wherein calculating the adjustment parameter for the at least one object from the two or more objects further comprises calculating (250) the adjustment parameter for the at least one object based on the calculated new weighted sum for the at least one object.

7. Method according to one of the preceding claims 1 to 6, wherein the available resource for the object is a predetermined time interval (20) which is divided between the communication function and the detection function of the object, and / or the available resource for the object is a predetermined power (30) which is divided between the communication function and the detection function of the object.

8. The method according to any one of the preceding claims 1 to 7, wherein a minimum proportion of the available resource allocated for the acquisition function is zero, which corresponds to no resource allocated for the acquisition function, in which case a proportion of the available resource allocated for the communication function takes on a maximum value, which corresponds to the situation when the available resource is only allocated to the communication function, and / or wherein a minimum proportion of the available resource allocated for the communication function is zero, which corresponds to no resource allocated for the communication function, in which case a proportion of the available resource allocated for the acquisition function takes on a maximum value, which corresponds to the situation when the available resource is only allocated to the acquisition function.

9. The method according to any one of the preceding claims 1 to 7, wherein for the at least one object a static communication portion of the available resource and a static detection portion of the available resource are allocated, which are independent of the calculated adaptation parameter, wherein for the at least one object a dynamic portion of the available resource is allocated, which is a difference between the available resource for the object and the allocated static communication and detection portions of the available resource for the object, and wherein the dynamic portion of the available resource for the at least one object is divided between the communication function and the detection function of this object based on the calculated adaptation parameter.

10. The method according to any one of the preceding claims 1 to 9, wherein the method is carried out for a number of cycles, starting with the first cycle of the number of cycles, and further comprising: Receiving (400) information relating to the performance detection characteristics of the two or more objects from the two or more objects for a current cycle; Calculating (500) the adjustment parameter for the at least one object from the two or more objects for the current cycle; Sending (600) the adaptation parameter calculated for the current cycle for the at least one object to the at least one object, wherein the adaptation parameter calculated for the current cycle is to be used for a subsequent cycle subsequent to the current cycle in order to divide the available resource for the object between the communication function and the acquisition function of this object for the subsequent cycle.

11. A computer program designed to carry out the method according to any one of the preceding claims 1 to 10.

12. A module (10) designed to carry out the method for allocating available resources with respect to the communication and detection functions of objects according to one of the preceding claims 1 to 10 and / or to carry out the computer program according to claim 11.

13. The module (10) of claim 12, configured to transmit the calculated adaptation parameter for the at least one object of the two or more objects to the at least one object.

14. Two or more objects, each object being one of two or more objects i,2, A M ) is a vehicle, a road infrastructure, or another device, wherein the two or more objects are adapted to sense their respective surroundings and send information to a module (10) according to claim 12 or 13, wherein at least one object of the two or more objects is adapted to: receive a calculated (200) adaptation parameter for the at least one object of the two or more objects from the module; and to allocate an available resource for the at least one object between a communication function and a detection function of that object based on the calculated adaptation parameter.

15. The two or more objects of claim 14, wherein each of the two or more objects is configured to determine its one or more performance detection characteristics.

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