Patent Description:
The basic ITS communication architecture is described in ETSI (European Telecommunications Standards Institute) Standard EN <NUM><NUM> and related ETSI standards. A most recent development in ITS is the so-called Collective Perception Service (CPS) to share information on objects detected by one communication partner, such as a vehicle onboard unit (OBU) or a roadside unit (RSU) (generally called "ITS station", ITS-S), with another communication partner (ITS-S). The CPS in ITS is described in, e.g., ETSI Technical Report TR <NUM><NUM> and ETSI Technical Specification TS <NUM><NUM>.

<FIG> show the present concept of CPS to share "perceptions" (detections, analysis and/or trackings) of objects among participants according to the above-mentioned ETSI standards. In <FIG> a vehicle <NUM> on a road <NUM> perceives an object <NUM>, e.g., another vehicle, by means of an own sensor <NUM> such as a camera, a radar sensor, lidar sensor etc., with a field of view <NUM>. In <FIG> the vehicle <NUM> may be additionally aware of a third vehicle <NUM> around a bend <NUM> of the road <NUM> which obstructs the direct view to the vehicle <NUM>, by means of a wireless communication <NUM> between an ITS-S aboard the vehicle <NUM> and an own ITS-S aboard the vehicle <NUM>. In <FIG> the third vehicle <NUM> around the bend <NUM> perceives a fourth vehicle <NUM> by means of an own sensor <NUM> with a field of view <NUM> and shares information about this perception over the wireless communication <NUM> with the first vehicle <NUM>. Vehicle <NUM> thus enjoys the benefit of a "Collective Perception" (CP) from other ITS-S-equipped participants so that it becomes aware of objects beyond its own sensor range even when those objects are not equipped with an ITS-S on their own.

The messages exchanged in the CPS to share such perceptions (here: the message from the ITS-S of the vehicle <NUM> over the wireless communication <NUM> to the ITS-S of the vehicle <NUM>) to inform the communication partner (here: the vehicle <NUM>) about the existence, speed, distance, position, direction etc. of a perceived object (here: the vehicle <NUM>) are called Collective Perception Messages (CPMs). <FIG> shows the general structure of a CPM as defined in ETSI TR <NUM><NUM>. The CPM <NUM> contains - apart from an ITS PDU (Protocol Data Unit) header <NUM> designating the message as a "CPM package" - a set of CPM parameters <NUM> in the form of one or more data containers <NUM> - <NUM>, in particular:.

In particular, according to ETSI TR <NUM><NUM> a perceived object container <NUM> may contain sensor data such as distance, speed, acceleration, heading (angle) of a perceived object <NUM> as measured by the ITS-S's sensor <NUM>, and an indication of the time of measurement of the sensor data. For some data elements, e.g., for distance, speed, angle and object dimension values, ETSI TR <NUM><NUM> also provides for confidence measures of the respective data values. The receiving ITS-S can then assess the trustworthiness of the collectively shared perception information.

In general, it is up to the receiving ITS-S to make good use of the wealth of collectively shared sensor data to appropriately execute road safety applications, such as driver warnings or automatic braking and steering functions. However, the wealth of information can overload the processing capabilities of receiving ITS-S in heavy traffic situations, leading either to malfunctions or the need for higher processing powers with increased costs.

In <CIT>, redundant information in CPMs is reduced in that each ITS-S does no longer include its own sensor data describing another ITS-S into its CPMs once it has identified, in a CPM received from that other ITS-S, sensor data of that ITS-S descriptive thereof. However, this only reduces CPM size in an existing ITS.

In <NPL>, CPM data is combined with local sensor data to provide an information input to a local driver assistance system.

It is an object of the invention to overcome the shortcomings of the prior art and to provide novel devices for improving CPS in ITS.

To this end, the invention creates a novel ITS service station, comprising:.

The novel ITS service station of the invention aggregates CPMs from surrounding ITS-S into aggregated ("third") CPMs so that other ITS-S listening to these broadcasts are eased from the burden of following a multitude of ITS-S and processing a multitude of CPMs. The ITS service station of the invention therefore contributes to reduce the complexity of the CPS for listening ITS-S, in particular when the aggregated CPMs of the ITS service station are prioritized over "normal" CPMs during communication or receipt.

A preferred embodiment of the invention is characterized in that the first sensor data includes a first confidence measure of said first data value and the at least one second sensor data includes a second confidence measure of said second data value, wherein the aggregator is configured to calculate a third confidence measure from said first and at least one second confidence measures and to include said third confidence measure in the third sensor data.

An aggregated ("third") data value in the aggregated ("third") CPM will most likely have a better aggregated ("third") confidence measure since it had been aggregated from multiple data sources. Therefore, any ITS-S listening to both "normal" CPMs (here: the first and second CPMs) and "aggregated" CPMs (here: the "third" CPM of the inventive ITS service station) can choose to process and consider the CPM showing the best confidence measure for a specific data value needed, leading to an implicit prioritizing of the CPMs of the ITS service station at the receiving ITS-S. The receiving ITS-S may ignore sensor data regarding the same object from all other CPMs in favor of the sensor data on this object in the aggregated CPM. Processing load in the receiving ITS-S is thus significantly reduced, in particular in heavy traffic situations, e.g., at an intersection, and low-cost ITS-S with modest processing capabilities can be used without compromising safety.

In one embodiment, the aggregator is configured to determine objects in relation to sensor data to be the same when the objects match in one or more of object positions, speeds, headings, and accelerations, as indicated in the respective CPMs. This leads to accurate matching results, however, requires that the respective object positions are either geo-referenced to a common or global coordinate system, e.g., given as absolute geo-coordinates, or map-matched to streets, places, landmarks etc. in a map.

Alternatively - or additionally, for added accuracy and reliability - the aggregator is configured to determine objects in relation to sensor data to be the same when the objects match at least in object appearance characteristics indicated in the respective CPMs. Appearance characteristics can be any one or more of: an object dimension, colour, shape, orientation etc. The mapping as to object appearance can be useful when object positions are indicated relatively to a perceiving ITS-S or are not accurate enough to distinguish between objects close to each other. Appearance and position matching can also be combined to improve the accuracy of the match.

The third sensor data may additionally include the number of first and second sensor data from which the third sensor data has been aggregated, and/or may additionally include the number of first and second ITS-S from whose CPMs the third sensor data has been aggregated. An ITS-S receiving the aggregated CPM can use this information to further assess the confidence of a sensor data value indicated therein.

According to a further preferred feature of the invention each sensor data includes a local identifier of the object related to said sensor data and the aggregator is configured to assign a global identifier to all local identifiers relating to the same object and to include that global identifier in the third CPM. Receiving ITS-S can then use the global object identifiers in, e.g., own CPMs sent to other participants. If the assignment table between local and global identifiers is disseminated from the ITS service station to the receiving ITS-S, too, e.g., within the aggregated CPM or in a separate broadcast message, then receiving ITS-S may more easily match the sensor data on an object from the aggregated CPM with the sensor data on the same object from other "normal" CPMs without the need of own position and/or appearance matches to determine the identity of objects over different CPMs.

For keeping inventory and tracking of objects in its area of coverage over time, the aggregator of the ITS service station may have a memory for storing first and second CPMs including timestamps of the sensor data therein and may be configured to retrieve, for aggregating said third CPM, all sensor data from the memory having timestamps falling within a selected period of time.

Although the aggregated CPMs of the ITS service station of the invention may implicitly have priority over "normal" CPMs in that they will usually carry sensor data with higher confidence measures than the normal CPMs of other ITS-S, the aggregated CPMs of the ITS service station may additionally be flagged with a higher priority than normal CPMs. This may be done by, e.g., including a "high priority" flag in the header of the aggregated CPM. Receiving ITS-S then do not need to compare confidence measures to prioritize aggregated CPM over normal CPMs, but just will look for the high priority flag, to speed up processing.

The ITS service station of the invention can either be moveable, e.g., in the form of an onboard unit on a vehicle, or stationary, such as a roadside unit or infrastructure. In a particularly preferred embodiment the ITS service station is a roadside unit at an intersection. At intersections high vehicle traffic and hence communication traffic is to be expected so that receiving ITS-S benefit most from the load-reducing and safety-increasing CPM aggregation service of the inventive ITS service station.

The invention will now be described in further detail by means of exemplary embodiments thereof under reference to the enclosed drawings, in which show:.

The vehicles <NUM> - <NUM> each carry an ITS-S <NUM> - <NUM> in the form of an onboard unit (OBU). Vehicles <NUM>, <NUM> are exemplarily equipped with a sensor <NUM> with a respective field of view <NUM>, capable of perceiving an object (here: the fourth vehicle <NUM>). The sensors <NUM> may be of any kind, e.g., a camera, a radar or lidar sensor, an acoustic sensor, a vibration sensor, an infrared sensor etc. The ITS service station <NUM>, too, may have an own sensor <NUM> to perceive objects <NUM> in its vicinity, although this is not obligatory. Generally speaking, each of the ITS-S <NUM> - <NUM> and ITS service station <NUM> may have none, one or more sensors <NUM>, also of different sensor types.

Instead of being stationarily mounted as a roadside unit, the ITS service station <NUM> could also be mobile, e.g., aboard a vehicle as an OBU.

The object <NUM> perceived by the sensors <NUM> may be of any kind, e.g., a manned or unmanned land, sea or air vehicle, a pedestrian, an animal, a machine, a traffic sign, a radio, a light or infrared beacon broadcasting some kind of information which is useful to be collectively perceived, and the like.

In the traffic scenario depicted in <FIG>, the vehicle <NUM> cannot see the object (here: vehicle) <NUM> approaching the intersection S since its view is blocked by buildings <NUM> at the corner of the roads A and D. However, the vehicles <NUM>, <NUM> share their perception of the vehicle <NUM>, as detected by their sensors <NUM>, via CPMs <NUM>, <NUM> sent from their ITS-S <NUM>, <NUM> (the "perceiving" or "disseminating" ITS-S) to the ITS-S <NUM> (the "receiving" or "listening" ITS-S) of the vehicle <NUM>. These "normal" CPMs <NUM>, <NUM> are also received by the ITS service station <NUM>, which creates an "aggregated" CPM <NUM> therefrom, as follows.

With reference to <FIG> and <FIG>, the ITS service station <NUM> has a receiver <NUM> with an area of radio coverage <NUM> to receive the CPMs <NUM>, <NUM> from the ITS-S <NUM>, <NUM> in its neighborhood. It goes without saying that the radio coverage area <NUM> will be dependent both on the transmitting power of the disseminating ITS-S <NUM>, <NUM> and the receiving sensitivity of the receiver <NUM>. For ease of description, the various normal CPMs <NUM>, <NUM>. are designated as CP<NUM>, CP<NUM>,. , generally CPm, in the following.

An aggregator <NUM> connected to the receiver <NUM> processes the set {CPm} of the received CPMs CPm and calculates the aggregated CPM <NUM>, called CPΣ in the following, therefrom. The aggregated CPM CPΣ is then broadcast by a transmitter <NUM> connected to the output of the aggregator <NUM> so that it can be received by listening ITS-S in the vicinity, such as (here) the ITS-S <NUM> on the vehicle <NUM>. The transmitter <NUM> and the receiver <NUM> of the ITS service station <NUM> can be implemented by a combined transceiver, too.

To calculate the aggregated CPM CPΣ from the received normal CPMs CPm the aggregator <NUM> has a memory <NUM> in which - among other programs and data as needed - two tables are stored: a table <NUM> storing the CPMs CPm, shown in <FIG>, and an (optional) table <NUM> storing assignments between global and local object identifiers, shown in <FIG> and described further down below.

With reference to <FIG> and <FIG> a CPM <NUM>, <NUM>, <NUM> or CPm, respectively, contains - apart from the other data depicted in <FIG> - in any perceived object container <NUM>, here called oc<NUM>, oc<NUM>,. , generally ocn, a local object identifier id, usually assigned by the perceiving ITS-S <NUM>, <NUM>, and one or more sensor data sdi (i = <NUM>, <NUM>,. ) on the object <NUM> with the local object identifier idn.

The sensor data sdi on an object <NUM> perceived by a disseminating ITS-S <NUM>, <NUM> contains data value di derived from an output of one or more sensor/s <NUM>, e.g., a distance of the object <NUM> to the sensor <NUM>, a speed of the object <NUM>, a geo-referenced or map-matched position PD of the object <NUM>, a heading, angle or path of travel of the object <NUM>, one or more dimensions of the object <NUM>, a shape, color or class of the object <NUM> as determined by the sensor <NUM>, e.g., as taken by a camera and determined by image processing, etc. For example, any of the data items in the perceived object container <NUM> of a CPM according to ETSI TR <NUM><NUM> can be the data value d, such as the data elements xDistance, yDistance, zDistance, xSpeed, ySpeed, zSpeed, xAcceleration, yAcceleration, zAcceleration, yawAngle, planarObjectDimension1, planarObjectDimension2, verticalObjectDimension, objectRefPoint, dynamicStatus, Classification, MatchedPosition according to ETSI TR <NUM><NUM>.

Some of the data values di which are provided by the respective sensor <NUM> or a suitable processor connected to the sensor/s <NUM> in the ITS-S <NUM>, <NUM> or the ITS service station <NUM> may be provided with a confidence measure cfi, in particular the distance, speed, angle and dimension data values di. For such data values di, the respective sensor data sdi is a pair (d, cf)i comprised of the data value di and the associated confidence measure cfi.

The confidence measure cfi of a data value di may be any statistical measure of the confidence, reliability, trustworthiness, non-error rate etc. of this data value di. For example, the confidence measure cfi can be the <NUM>%-confidence interval of the respective data value di, i.e., that with a probability of <NUM>% the data value falls within this interval. Of course, other measures of confidence could be used as explained later on.

If the ITS service station <NUM> has one or more own sensors <NUM> which generate their own sensor data sdk (k = <NUM>, <NUM>,. ), the output of these sensors <NUM> can, e.g., be stored - in the same format as the received CPMs CPm - in data records SD<NUM>, SD<NUM>,. , generally SDk, for example in the same table <NUM>, as shown in <FIG>.

From at least two received CPMs CPm, or at least one received CPM CPm and at least one sensor data record SDk, the aggregator <NUM> calculates the aggregated CPM CPΣ as follows.

As shown in <FIG>, an aggregated sensor data sdΣ can be any aggregation of sensor data sdi, sdk of the same sensor type (i.e., the same type of sensor <NUM> or combination of sensors <NUM>) from at least two different CPMs CPm or at least one CPM CPm and at least one record SDk. By means of "aggregation" any mathematical operation or representation which can be performed on two or more data values di and dk which yields one data value dΣ shall be understood. For example, the aggregated data value dΣ can be an average or weighted average of the individual data values di, dk of which it has been aggregated. Alternatively, the aggregated data value dΣ, could be that one of the originating data values di, dk which has the best confidence measure cfi, cfk, respectively. Or, the aggregated data value dΣ is an average or weighted average of just the two or three data values di, dk with the best confidence measures. Furthermore, for a weighted average the individual data values di, dk could be weighted by their respective confidence measures cfi, cfk. As an example, one embodiment of an aggregation function F for a data value dΣ formed from at least two different CPMs CPi (i = <NUM>. N) could be: <MAT>.

Of course, other aggregation functions F could be used such as, for example, using only the data value di with the "best" confidence measure cfi as the aggregated data value dΣ, using step functions or a binary decision tree to select one or more data value/s di with "good" confidence measure/s surpassing a specified threshold while discarding other ones below the threshold, including other information such as the number of data sources (stations <NUM>, <NUM>, <NUM> and/or sensors <NUM>), their positions, speeds and/or headings with respect to the object <NUM>, the field of view <NUM> of the respective sensors <NUM>, etc..

Furthermore, also sensor data sdi, sdk from more than one sensor <NUM> of an ITS-S <NUM>, <NUM> and/or the ITS service station <NUM> which relate to the same object <NUM> can be aggregated into one aggregated sensor data sdΣ for that object <NUM>, e.g., by combining different dimension values into a shape value, or combining speed and heading values into a movement vector, etc..

If data values di, dk with respective confidence measures cfi, cfk are used in the aggregation function F the data values di, dk could be weighted with their respective confidence measures cfi, cfk, for example as follows: <MAT>.

The aggregated sensor data sdΣ can have an aggregated confidence measure cfΣ attributed to the aggregated data value dΣ. Any statistical operation or measure can be applied to calculate this aggregated confidence measure cfΣ. The type of calculation also depends on which type of confidence measure is used for the confidence measure cfi, cfk in the CPMs CPm and data records SDk.

For example, the confidence measure cfi, cfk could be indicated in the form of a confidence interval into which a given percentage of all readings of a sensor <NUM> falls, e.g., a confidence interval for a percentage (confidence level) of <NUM>%. Or, the other way round, the confidence measure cfi, cfk could be indicated as the percentage (confidence level) of all measurements of a sensor <NUM> which fall into a given confidence interval (error range). All sorts of known statistical measures for indicating such confidence measures can be used.

For example, if the confidence measure cfi, cfk is indicated as that confidence interval into which <NUM>% of all measurements fall (the "<NUM>%-confidence level"-confidence interval) as used in ETSI TR <NUM><NUM> for indicating the confidence of the data values distance, speed, angle and dimension, and if we assume the sensors <NUM> to have a Gaussian error distribution, for the exemplary aggregation function F given in equation (<NUM>) the aggregated confidence measure cfΣ can be calculated using <MAT> as <MAT> wherein Φ designates the Cumulative Distribution Function and Φ-<NUM> designates the inverse thereof.

When other aggregation functions F than that of equation (<NUM>) are used, the aggregated confidence measure cfΣ - be it indicated as a confidence level for a given confidence interval or as a confidence interval for a given confidence level - can be calculated accordingly, as known in the art of statistics and error propagation calculus.

The aggregated confidence measure cfΣ can also be a composite field or concatenation of the confidence measure cfΣ as calculated above and other information such as the number of data sources responsible for that confidence measure cfΣ, their positions, speeds and/or headings with respect to the object <NUM>, the fields of view <NUM> of the respective sensors <NUM>, etc. For example, the more different the positions of the data sources, i.e. the positions of the sensors <NUM> and/or the positions of the stations <NUM>, <NUM>, <NUM>, with respect to a specific object <NUM> are, the better the data quality of the data value dΣ aggregated therefrom is.

To be able to aggregate the originating data values di, dk or originating sensor data sdi, sdk (including the respective confidence measures cfi, cfk, if applicable) into the aggregated data value dΣ or sensor data sdΣ, respectively, the aggregator <NUM> has to determine that the originating data values or sensor data all relate to the same perceived object <NUM>. This is easy if the object identifiers id in the respective CPM CPm and/or data records SDk are the same. This may happen when the disseminating ITS-S <NUM>, <NUM> (and the ITS service station <NUM>, if provided with an own sensor <NUM>) already use "global" object identifiers instead of "local" object identifiers. The term "local" object identifier refers to an object identifier id which had been assigned by the respective station <NUM>, <NUM>, <NUM> itself (locally), whereas the term "global" object identifier, here designated g-id in the following, refers to an object identifier which had been assigned by the ITS service station <NUM> at least "area wide" in its area of coverage <NUM>.

To this end, the ITS service station <NUM>, and in particular the aggregator <NUM>, optionally hosts and manages the assignment table <NUM> in the memory <NUM>, storing an association (assignment) between a global object identifier g-idn and all local object identifiers idm,n regarding the same perceived object <NUM>, as described in a perceived object container ocn, from a multitude of different CPMs CPm received (plus, if applicable, of different data records SDk stored).

<FIG> shows an example of the assignment table <NUM>. The aggregator <NUM> may disseminate the table <NUM> in its aggregated CPMs CPΣ or in separate broadcasts such as CAMs (Common Awareness Messages) or BST (Beacon Service Table) messages to all listening ITS-S <NUM> - <NUM> in its area of coverage <NUM>. Listening ITS-S <NUM> - <NUM> may then use the disseminated global object identifiers g-idn in their CPMs CPm to the ITS service station <NUM>, so that matching the data values di, dk or sensor data sdi, sdk, respectively, as to the "same" object <NUM> can be done by looking for the same global object identifier g-idn.

On the other hand, the aggregator <NUM> can determine itself whether objects <NUM> about which sensor data sdi is communicated in the CPMs CPm (or sensor data sdk is recorded in the records SDk) relate to the same object <NUM>. To this end, sensor data sdi, sdk indicative of object positions - be they given "absolutely" in terms of a global or at least geo-referenced coordinate system or "relatively" to the position of the respective disseminating ITS-S <NUM>, <NUM> and then converted into global or geo-referenced coordinates or map-matched position data - may be matched to each other so that congruency (within a certain error margin) in position indicates the same object. Alternatively or additionally, further movement characteristics of the object <NUM> can be taken into account in the match, such object speed, heading, and/or acceleration.

Alternatively, object identity could be determined by the aggregator <NUM> by means of analyzing sensor data sdi, sdk indicative of one or more characteristics of an object's appearance, such as one or more dimension/s of an object <NUM>, its color, shape, orientation, heading etc..

Both methods of matching, as to object positions and as to object appearance characteristics, can be combined to increase the accuracy of the match.

Based on the matches found, the aggregator <NUM> can then assign the global object identifier g-idn to the local object identifiers idi, idk of the matching objects <NUM>.

The aggregation performed by the aggregator <NUM> may take into account timing aspects. Each CPM CPm and record SDk, and in particular each perceived object container ocn or even each individual sensor data sdi, sdk, may contain a timestamp t indicative of the time of measurement of the respective sensor data sdi, sdk. The timestamp t may be indicated in any suitable format, be it relatively to a time of sending the respective CPm or the time of storing the respective record SDk, or absolutely in terms of a systemwide reference clock.

The timestamps t can also take into account the track or estimated movement of a perceived object <NUM>, and any calculation, processing or transmission delays. In this way, the "age" of a sensor data sdi, skk can be accounted for by the aggregator <NUM> when matching objects <NUM> for identity and/or calculating the aggregated CPM CPΣ. For example, the aggregator <NUM> may, when aggregating the CPM CPΣ, only use sensor data sdi, sdk from its memory <NUM> whose timestamps t fall within a selected period of time, for example into a past cycle interval, when the ITS service station <NUM> cyclically sends CPMs CPΣ.

An aggregated sensor data sdΣ may even relate to a "global" object confidence of the perceived object <NUM> taking into account sensor data sdi, sdk of different (sensor) types.

In the aggregated CPM CPΣ the aggregator <NUM> may optionally include the number (count) of originating sensor data sdi, sdk from which a specific aggregated sensor data sdΣ had been aggregated, and/or the number (count) of disseminating ITS-S <NUM>, <NUM> from whose CPMs CPm that specific aggregated sensor data sdΣ had been aggregated. The numbers (counts) can be, e.g., attached as data field/s to the respective aggregated confidence value/s cfΣ in the aggregated CPM CPΣ. These numbers (counts) can then be used by a receiving ITS-S <NUM> to select if or which one of several received aggregated CPM CPΣ is to trust most regarding a specific sensor data.

Usually, the receiving ITS station <NUM> will select and use that aggregated sensor data sdΣ on an object <NUM> which has the best confidence measure cfΣ attributed to it, e.g., the smallest confidence interval or the highest confidence level, when the confidence measure is expressed in such terms. However, with the additional knowledge of the numbers (counts) of originating sensor data or ITS-S, from which the sensor data sdΣ had been aggregated, the receiving ITS-S <NUM> can improve the selection, e.g., by weighting the confidence measures cfΣ by their respective numbers (counts) of underlying data. On the other hand, said numbers (counts) may be particularly useful for sensor data sdΣ which does not comprise a confidence measure cfΣ at all. For example, it the data value dΣ in the sensor data sdΣ is an average of the originating data values di, dk, the number (count) of averaged values is a measure of the quality of the process of averaging.

The aggregated CPMs CPΣ broadcast by the ITS service station <NUM> as CPMs <NUM> may optionally contain a "high priority" flag or a flag indicating a priority which is higher than those of the "normal" CPMs CPm (CPMs <NUM>, <NUM>). ITS-S <NUM> listening to the CPMs <NUM>, <NUM> may prefer CPMs <NUM> with higher priority (or disregard CPMs <NUM>, <NUM> with lower priority) so that the aggregated CPMs <NUM> of the ITS service station <NUM> are favored.

Claim 1:
Intelligent Transportation System, ITS, service station (<NUM>), comprising:
a receiver (<NUM>) having an area of radio coverage (<NUM>) and being configured to receive a first Collective Perception Message, CPM, (<NUM>) from a first ITS station (<NUM>) at a first position (PB) within the coverage area (<NUM>), the first CPM (<NUM>) including first sensor data (sdi) on an object (<NUM>) perceived by the first ITS station (<NUM>);
an aggregator (<NUM>) connected to the receiver (<NUM>) and configured to aggregate said first sensor data (sdi) with at least one second sensor data (sdi) on the same object (<NUM>) into a third sensor data (sdΣ), which second sensor data (sdi) is either received via the receiver (<NUM>) in a second CPM (<NUM>) from a second ITS station (<NUM>) at a second position (PC) within the coverage area (<NUM>) perceiving the same object (<NUM>) or is determined by a sensor (<NUM>) of the ITS service station (<NUM>) perceiving the same object (<NUM>);
wherein the first sensor data (sdi) includes a first data value (di), the at least one second sensor data (sdk) includes a second data value (dk) and the aggregator (<NUM>) is configured to calculate a third data value (dΣ) from said first and at least one second data values (di, dk) and to include said third data value (dΣ) in the third sensor data (sdΣ), wherein each of the first, second and third data values (di, dk, dΣ) is a distance of the object (<NUM>) with respect to a respective sensor (<NUM>), a speed of the object (<NUM>), an acceleration of the object (<NUM>), a geo-referenced or map-matched position (PD) of the object (<NUM>), a heading, angle or path of travel of the object (<NUM>), one or more dimensions of the object (<NUM>), a shape, color or class of the object (<NUM>), and
in that the ITS service station further comprises a transmitter (<NUM>) connected to the aggregator (<NUM>) and configured to broadcast said third sensor data (sdΣ) in a third CPM (<NUM>).