Patent Description:
The present specification generally relates to automotive applications where autonomous or semi-autonomous vehicles use positioning information to assist their decisions as of which direction and speed to adopt.

Traditionally, vehicles use assisted global navigation satellite system (GNSS) approaches assisted with correction data provided by terrestrial means, i.e., via the terrestrial network. The location estimation is typically performed at the vehicle itself, i.e., it corresponds to a UE-based positioning.

The assistance data is provided to the user equipment (UE) by the location management function (LMF), and is used for performing corrections on the estimated position. In particular, the assistance data contains.

An issue addressed with the present disclosure relates to the fact that vehicles must make decisions on their own movement (e.g., direction and speed) which depend on the relative locations at current and future time of nearby vehicles, pedestrians, cyclists, other moving objects, etc..

That is, in many cases, a vehicle makes a decision that depends not only on its own position, but also on (predicted) position of nearby vehicles. The (autonomous) vehicle's decision also depends on intelligence regarding nearby vehicles' intention to modify their velocity vector (e.g., turn, decelerate, accelerate). This is the typical assumption in traditional human-based vehicle steering, where the human driver makes an estimation of the future position of nearby vehicles and assesses whether a change of his/her own vehicle steady state would be safe, make sense, or would cause problems to the traffic.

In a more concrete view, autonomous vehicles should maintain a "steady state" condition with a constant velocity vector (for example, with respect to the highway lane they are using), unless they need to break such steady state. Breaking the steady state is the part that needs caution, since this is the part which is followed by a decision on which movement the vehicle should take with respect to other vehicles in the area.

As an example, when overpassing, a vehicle needs to predict whether there is enough time to merge into the left lane before another approaching vehicle reaches that point. Human drivers make such calculations instinctively.

Such prediction, and the determination of the vehicle's surroundings and the impact of the vehicle's actions (e.g. overpassing) on other vehicles or, more general, other traffic participants, and the impact of other vehicles or, more general, other traffic participants, on the vehicle's (decisions on) actions (e.g. overpassing) need to be done based on information on the vehicle's surroundings.

Although some information can be obtained locally (at the UE/vehicle) using data from cameras/sensors techniques (e.g. by means of sensing and machine vision techniques), sensor data can be insufficient, due to.

<FIG> shows a schematic diagram of an example of a vehicle environment. In particular, <FIG> illustrates a large truck significantly blocking surrounding vehicles' visions.

In such scenarios (where the sensor data is not reliable), information regarding the nearby vehicles is essential for not causing any traffic issues/collisions. Even for the case that vehicles can sense surroundings, obtaining predictive information only using sensors/cameras can be limited.

Hence, the problem arises that relevant information enabling the UE/vehicle to make safe decisions on its own behavior as a traffic participant considering the behavior (present/future) of other traffic participants may be unavailable and/or undeterminable for the UE/vehicle.

Hence, there is a need to provide for provision of vehicle's surrounding intelligence information.

<CIT> at paragraph [<NUM>] teaches the exchange of information between vehicles based on vehicle locations, speeds and moving directions (including real-time changes in moving directions) and, in paragraph [<NUM>] intended routes. <CIT> discloses updating routes dynamically based on traffic patterns.

<CIT> at paragraphs [<NUM>], [<NUM>] and [<NUM>] to [<NUM>] discloses the provision of a confidence level of predicted future location information. In the context of vehicles, the confidence issues stem from uncertainty due to road traffic conditions and congestion, and how extrapolation of current road conditions affects estimates of future location.

<CIT>, <CIT> and <CIT> disclose sharing intended maneuvers by adjacent autonomous vehicles.

In accordance with the present invention, there is provided apparatus of a first mobile entity, apparatus of a network entity and corresponding methods as claimed in the accompanying claims.

Any one of the above aspects enables an efficient obtaining, collecting, processing of information regarding surroundings of a terminal or a vehicle including said terminal and provision of the results of such processing to the terminal or vehicle including said terminal to thereby solve at least part of the problems and drawbacks identified in relation to the prior art.

In particular, any one of the above aspects integrates <NUM> positioning into vehicle-to-everything (V2X) use cases, which is in-line with industrial customer requirements where <NUM> positioning is an add-on to existing GNSS solutions.

Further, any one of the above aspects enables off-loading of computation power from vehicles for conducting analytics of nearby vehicles.

Still further, any one of the above aspects creates business value for <NUM> positioning by means of the surrounding intelligence information. That is, otherwise existing GNSS receivers can process only their own position but not others.

By way of example embodiments, there is provided provision of vehicle's surrounding intelligence information. More specifically, by way of example embodiments, there are provided measures and mechanisms for realizing provision of vehicle's surrounding intelligence information.

Thus, improvement is achieved by methods, apparatuses and computer program products enabling/realizing provision of vehicle's surrounding intelligence information.

In the following, the present disclosure will be described in greater detail by way of non-limiting examples with reference to the accompanying drawings, in which.

The present disclosure is described herein with reference to particular non-limiting examples and to what are presently considered to be conceivable embodiments. A person skilled in the art will appreciate that the disclosure is by no means limited to these examples, and may be more broadly applied.

It is to be noted that the following description of the present disclosure and its embodiments mainly refers to specifications being used as non-limiting examples for certain exemplary network configurations and deployments. Namely, the present disclosure and its embodiments are mainly described in relation to 3GPP specifications being used as non-limiting examples for certain exemplary network configurations and deployments. In particular, V2X communication is used as a non-limiting example for the applicability of thus described example embodiments. As such, the description of example embodiments given herein specifically refers to terminology which is directly related thereto. Such terminology is only used in the context of the presented non-limiting examples, and does naturally not limit the disclosure in any way. Rather, any other communication or communication related system deployment, etc. may also be utilized as long as compliant with the features described herein.

Hereinafter, various embodiments and implementations of the present disclosure and its aspects or embodiments are described using several variants and/or alternatives. It is generally noted that, according to certain needs and constraints, all of the described variants and/or alternatives may be provided alone or in any conceivable combination (also including combinations of individual features of the various variants and/or alternatives).

According to example embodiments, in general terms, there are provided measures and mechanisms for (enabling/realizing) provision of vehicle's surrounding intelligence information.

As discussed above, in scenarios where e.g. the sensor data is not reliable, information regarding the nearby vehicles is essential for not causing any traffic issues/collisions. Even for the case that vehicles can sense surroundings, obtaining predictive information only using sensors/cameras can be limited. For example, there is no consideration of previous location of nearby vehicles taken into account.

While known vehicles modify e.g. their speed based on instantaneous sensed (utilizing sensors) locations of other vehicles, according to example embodiments, the network (e.g. LMF) typically having much more information e.g. regarding trajectory of nearby vehicles is allowed to make more accurate predictions and is thus enabled to provide such surrounding intelligence information to UEs/vehicles. Hence, according to example embodiments, a centralized approach where the network/LMF provides such surrounding intelligence information is implemented.

In other words, turning, overpassing, accelerating, decelerating etc. of a vehicle (including a UE) must be done after the vehicle has collected surrounding intelligence information. According to example embodiments, surrounding intelligence information (e.g. on predicted future locations of nearby vehicles) is calculated at the network (e.g. LMF) and provided to the vehicle (UE) upon request.

As such, with such centralized solution according to example embodiments, a corresponding gap in known prior art can be closed.

In general, according to example embodiments, a UE request for surrounding intelligence information is implemented. According to example embodiments, this is related to the so-called mobile-originated location request (MO-LR) and can be implemented as a modified version of MO-LR. MO-LR is defined in <NUM>rd Generation Partnership Project (3GPP) TS38. <NUM>, an extract of which is given below.

<FIG> shows a schematic diagram of signaling sequences, and in particular illustrates UE positioning operations to support MO-LR. More specifically, <FIG> shows the sequence of operations for an MO-LR service, starting at the point where an location services (LCS) client in the UE or the user has requested some location service (e.g., retrieval of the UE's location or transfer of the UE's location to a third party).

The steps illustrated in <FIG> (MO-LR service support) are as explained below:.

However, while example embodiments are explained with reference to the MO-LR processing, these are not limited to an implementation oriented on the MO-LR processing (i.e. as a modified MO-LR processing), but several ways may be utilized as long as the information as discussed below is exchanged between vehicles/UEs and the network for assisting the vehicles'/UEs' decision finding.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus may be a terminal <NUM> such as a user equipment comprising a transmitting circuitry <NUM> and a receiving circuitry <NUM>. The transmitting circuitry <NUM> transmits, towards a network entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity. The receiving circuitry <NUM> receives, from said network entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity. Here, the combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to a claimed embodiment comprises an operation of transmitting (S51), towards a network entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity, and an operation of receiving (S52), from said network entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity, wherein said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. In particular, <FIG> illustrates a variation of the apparatus shown in <FIG>. The apparatus according to <FIG> may thus further comprise a determining circuitry <NUM>, a detecting circuitry <NUM>, and/or a sensing circuitry <NUM>.

In an embodiment at least some of the functionalities of the apparatus shown in <FIG> (or <FIG>) may be shared between two physically separate devices forming one operational entity. Therefore, the apparatus may be seen to depict the operational entity comprising one or more physically separate devices for executing at least some of the described processes.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, a claimed embodiment comprises an operation of determining an intention for a movement change of said first mobile entity, and an operation of transmitting, towards said network entity, information on said intention, wherein said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity is related to a section of said surroundings of said first mobile entity relevant for said movement change of said first mobile entity.

According to further example embodiments, said information on said intention comprises at least one of a type of said movement change, a direction of said movement change, and an intensity of said movement change.

According to the claimed embodiment, said information on said intention comprises time interval information indicative of an intention validity time interval of said intention.

According to further example embodiments, said location information of said at least one second mobile entity comprises a predicted future location of said at least one second mobile entity.

According to further example embodiments, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity comprises time information indicative of a predicted future time corresponding to said location information of said at least one second mobile entity.

According to further example embodiments, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity comprises trust information indicative of an accuracy of said location information of said at least one second mobile entity.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of receiving, from said network entity, a location request, an operation of detecting an own location of said first mobile entity, and an operation of transmitting, towards said network entity, information on said own location of said first mobile entity.

According to further example embodiments, said location request incudes a demand for sensor measurements with respect to nearby mobile entities.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of sensing a state of said at least one second mobile entity, and an operation of transmitting, towards said network entity, information on said state of said at least one second mobile entity.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of receiving, from said network entity, warning information indicative of that no predetermined minimum amount of combined spatiotemporal surroundings characteristics related to a specific area are available.

Here, it is noted that according to example embodiments, the predetermined minimum amount is defined by the network, e.g. by the network entity which may for example be an LMF. According to further example embodiments, the predetermined minimum amount is determined autonomously by the first network entity, e.g. by the terminal/UE which may for example be included in a vehicle in question.

According to further example embodiments, said method is operable at or by a terminal, user equipment, mobile station or modem.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus may be a network entity <NUM> such as a network side node of a cellular system such as a location management function entity comprising a receiving circuitry <NUM>, a generating circuitry <NUM>, and a transmitting circuitry <NUM>. The receiving circuitry <NUM> receives, from a first mobile entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity. The generating circuitry <NUM> generates said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity based on a position of said first mobile entity. The transmitting circuitry <NUM> transmits, towards said first mobile entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity. Here, the combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to a claimed embodiment comprises an operation of receiving (S61), from a first mobile entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity, an operation of generating (S62) said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity based on a position of said first mobile entity, and an operation of transmitting (S63), towards said first mobile entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity, wherein said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. In particular, <FIG> illustrates a variation of the apparatus shown in <FIG>. The apparatus according to <FIG> may thus further comprise an analyzing circuitry <NUM>, a creating circuitry <NUM>, a matching circuitry <NUM>, a refining circuitry <NUM>, an assigning circuitry <NUM>, a classifying circuitry <NUM>, an allocating circuitry <NUM>, and/or a detecting circuitry <NUM>.

According to a variation of the procedure shown in <FIG>, exemplary additional operations and exemplary details of the generating operation are given, which are inherently independent from each other as such. According to such variation, a claimed embodiment comprises an operation of receiving, from said first mobile entity, information on an intention for a movement change of said first mobile entity. Further, such generating operation according to a claimed embodiment comprises an operation of analyzing said position of said first mobile entity, a position of said at least one second mobile entity, and said intention for said movement change of said first mobile entity, and an operation of creating said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity based on a result of said analyzing, wherein said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity is related to a section of said surroundings of said first mobile entity relevant for said movement change of said first mobile entity.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of transmitting, towards said first mobile entity, a location request, and an operation of receiving, from said first mobile entity, information on an own location of said first mobile entity.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of receiving, from said first mobile entity, information on a state of said at least one second mobile entity, and an operation of matching said information on a state of said at least one second mobile entity with a position of said at least one second mobile entity, and an operation of refining said location information of said at least one second mobile entity based on said matching.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of assigning mobile entities of a plurality of mobile entities into respective capability groups based on respective mobile entity surroundings intelligence collaboration related capabilities of said mobile entities of said plurality of mobile entities.

According to further example embodiments, said mobile entity surroundings intelligence collaboration related capabilities include at least one of sensor capabilities and processing capabilities.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of classifying areas of a plurality of areas into a plurality of area groups respectively indicative of a different degree with which surroundings intelligence leading to a predetermined minimum amount of combined spatiotemporal surroundings characteristics is enabled.

According to a variation of the procedure shown in <FIG>, exemplary details of the classifying operation are given, which are inherently independent from each other as such. Such exemplary classifying operation according to example embodiments may comprise an operation of allocating said areas of said plurality of said areas to any of said plurality of area groups based on said capability groups and positions of respective mobile entities assigned to said capability groups.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of detecting, that said first mobile entity enters a specific area of any of said plurality of area groups indicative of that surroundings intelligence leading to said predetermined minimum amount of combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity is not enabled, and an operation of transmitting, towards said first mobile entity, warning information indicative of that no predetermined minimum amount of combined spatiotemporal surroundings characteristics related to said specific area are available.

According to further example embodiments, said method is operable at or by a network side node of a cellular system.

In other words, according to example embodiments, vehicles request and obtain a new set of position information, referred to as surrounding intelligence information, from a centralized network entity (e.g., the LMF).

Namely, on the UE-side (terminal, vehicle including terminal/UE), a surrounding intelligence request is transmitted to the network, i.e., a central entity of the network, i.e., a network side node, i.e., the LMF.

According to example embodiments, the vehicle (terminal, vehicle including terminal/UE) provides its intention (e.g., left lane merge, accelerate, decelerate) to the LMF and stamps it with a validity time interval for such intention.

According to further example embodiments, the vehicle (terminal, vehicle including terminal/UE) requests for LMF analytics (e.g. predicted future location of nearby vehicles).

According to further example embodiments, the vehicle (terminal, vehicle including terminal/UE) may provide to the LMF other sensor-based measurements of nearby UEs (e.g., 2D snapshots of yellow indicator, red brake sign, etc.).

On the LMF side (as an example of the central entity of the network ("the network")), i.e., the network side node), analytics are performed.

In particular, according to example embodiments, the network collects the vehicles' locations.

According to further example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) refines the mobility pattern of an UE using both cellular (<NUM> position estimates) and non-cellular measurement reports (i.e., sensor-collected information). The LMF matches UE's location with sensor-collected information about other UEs.

According to further example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) provides a list of {timestamp, predicted_location, UE_ID} of nearby vehicles to the UE.

According to further example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) groups devices based on their capabilities with regards to information collection. For example, devices with extended capabilities with respect to information collection (such as sensor-equipped devices) are distinguished from devices with low or zero such capabilities.

According to further example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) alerts UE heading into areas where insufficient information is available or the collected information is below a given level.

The above example embodiments are explained below in more specific terms.

As mentioned above, while example embodiments are explained below in more specific terms as a modification of the MO-LR processing, these are not limited to an implementation oriented on the MO-LR processing (i.e. as a modified MO-LR processing), but several ways may be utilized as long as the information as discussed below is exchanged between vehicles/UEs and the network for assisting the vehicles'/UEs' decision finding.

<FIG> shows a schematic diagram of signaling sequences according to example embodiments. In particular, <FIG> illustrates signaling associated with requesting and obtaining the surrounding intelligence information at the UE according to example embodiments.

According to example embodiments, the UE (terminal, vehicle including terminal/UE), potentially in addition to requesting its location information from the network, requests surrounding analytics information, which corresponds to the surrounding intelligence information that the UE needs to obtain before it breaks its velocity "steady state".

According to further example embodiments, the UE (terminal, vehicle including terminal/UE) provides its intention to the LMF, which is used by the LMF to process the surrounding intelligence information which is specific for this UE and for the particular intention of this UE. For example, if the UE/vehicle intends to overpass another vehicle by using the highway lane on its left, this intention to merge left is provided to the LMF which in turn processes it with the analytics on nearby vehicles locations (and future locations).

According to further example embodiments, the UE (terminal, vehicle including terminal/UE) stamps the message with a validity time for the intention.

According to further example embodiments, the UE (terminal, vehicle including terminal/UE) may provide to the LMF sensor information relevant to the provided intention. For instance, if the UE/vehicle intends to merge left, the sensor information involves camera information on potential turning indicators of nearby vehicles into the left lane where the UE/vehicle intends to merge into.

According to example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node), based on the provided information, conducts analytics processing based on received/obtained information.

According to further example embodiments, such processing by the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) comprises grouping the UEs/vehicles based on their capabilities of information collection. That is, the UEs/vehicles are assigned into groups that correspond to different capabilities as regards the amount of information that can be collected (e.g., groups of UEs with/without sensing (utilizing sensors) capabilities, groups of UEs with quick/slow processing capabilities of received surrounding intelligence information). This allows the LMF to categorize the UEs based on what amount of information it can collect from such UEs, and what level of processing the UE can apply to the intelligence information provided by the LMF.

According to further example embodiments, such processing by the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) may comprise, in case the LMF has received sensor-related information from the UE/vehicle, conducting analytics that lead to matching the sensor-related information by the UE (for example, the nearby car approaching the left lane as seen by the UE camera) with the information that the LMF has collected about the nearby UEs' location and their predicted location. This allows for further refinement of the nearby UEs' predicted location, as a means of double-checking that the predicted nearby UEs' location information matches the sensor-collected one. According to example embodiments, this can be achieved with existing big data processing tools. According to further example embodiments, machine learning with data fusion may be used to determine mobility models for the UEs of interest by corroborating inputs such as <NUM> NR uplink/downlink (UL/DL) reference signals (RS) and reports of the involved UEs, 2D camera images reported by the UEs, road camera images and RADARs, etc..

According to further example embodiments, such processing by the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) comprises processing the current location of nearby vehicles and conducting analytics that provide the predicted future location of the UEs that is relevant to the provided UE intention in the first step. In addition, according to further example embodiments, the LMF may provide a trust metric of its own prediction, by e.g. reporting a certainty level, the variance of the prediction, etc..

According to further example embodiments, if the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) requires additional information from specific UEs, the LMF creates a location request which involves requesting location and sensor measurements from nearby UE/vehicles, which are used for the analytics processing. According to still further example embodiments, the LMF may ask other devices that do not participate to the high speed traffic to provide surrounding information, in case the area lacks the necessary level of detail.

According to further example embodiments, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) provides to the UE requesting the surrounding intelligence information the list of nearby UEs along with their future locations associated with a corresponding timestamp and a trust metric of such location (e.g. the LMF may attach the variance of the location estimates). That is, the LMF provides to the requesting UE all the near-future predicted locations of the other UE/vehicles within an area of interest.

According to further example embodiments, based on the obtained information, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) determines the areas where sufficient information is collected that leads to sufficient surrounding intelligence information. That is, the LMF distinguishes between areas where it can provide such surrounding intelligence information (SII), and those where such collection and processing of information is not possible. According to example embodiments, such area classification may be generated by corroborating the different UE capability-groups (determined in a previous step as explained above) with the location of these groups. For example, classifiers like decision forests, decision trees, may be used to determine whether an area (e.g. with boundaries characterized by a set of Cartesian coordinates) is either SII-capable or not. According to still further example embodiments, such classifiers may receive as additional input the capabilities of the UEs transiting the area, capabilities of fixed infrastructure UEs in the area, etc..

According to further example embodiments, based on the request and intention of the UE of interest, in case the UE enters an area where the LMF does not have sufficient information and thus cannot support surrounding intelligence information, the LMF (as an example of the central entity of the network ("the network")), i.e., the network side node) provides a corresponding warning signal to that UE.

In the foregoing exemplary description of the network entity, only the units that are relevant for understanding the principles of the disclosure have been described using functional blocks. The network entity may comprise further units that are necessary for its respective operation. However, a description of these units is omitted in this specification. The arrangement of the functional blocks of the devices is not construed to limit the disclosure, and the functions may be performed by one block or further split into sub-blocks.

In <FIG>, an alternative illustration of apparatuses according to example embodiments is depicted. As indicated in <FIG>, according to example embodiments, the apparatus (terminal) <NUM>' (corresponding to the terminal <NUM>) comprises a processor <NUM>, a memory <NUM> and an interface <NUM>, which are connected by a bus <NUM> or the like. Further, according to example embodiments, the apparatus (network entity) <NUM>' (corresponding to the network entity <NUM>) comprises a processor <NUM>, a memory <NUM> and an interface <NUM>, which are connected by a bus <NUM> or the like, and the apparatuses may be connected via link <NUM>, respectively.

The processor <NUM>/<NUM> and/or the interface <NUM>/<NUM> may also include a modem or the like to facilitate communication over a (hardwire or wireless) link, respectively. The interface <NUM>/<NUM> may include a suitable transceiver coupled to one or more antennas or communication means for (hardwire or wireless) communications with the linked or connected device(s), respectively. The interface <NUM>/<NUM> is generally configured to communicate with at least one other apparatus, i.e. the interface thereof.

The memory <NUM>/<NUM> may store respective programs assumed to include program instructions or computer program code that, when executed by the respective processor, enables the respective electronic device or apparatus to operate in accordance with the example embodiments.

According to example embodiments, an apparatus representing the terminal <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform transmitting, towards a network entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity (thus the apparatus comprising corresponding means for transmitting), and to perform receiving, from said network entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity (thus the apparatus comprising corresponding means for receiving). Here, the combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity.

According to example embodiments, an apparatus representing the network entity <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform receiving, from a first mobile entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity (thus the apparatus comprising corresponding means for receiving), to perform generating said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity based on a position of said first mobile entity (thus the apparatus comprising corresponding means for generating), and to perform transmitting, towards said first mobile entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity (thus the apparatus comprising corresponding means for transmitting). Here, the combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity.

For the purpose of the present disclosure as described herein above, it should be noted that.

Generally, any method step is suitable to be implemented as software or by hardware without changing the idea of the present disclosure. Devices and means can be implemented as individual devices, but this does not exclude that they are implemented in a distributed fashion throughout the system, as long as the functionality of the device is preserved. Such and similar principles are to be considered as known to a skilled person.

The present disclosure also covers any conceivable combination of method steps and operations described above, and any conceivable combination of nodes, apparatuses, modules or elements described above, as long as the above-described concepts of methodology and structural arrangement are applicable.

In view of the above, there are provided measures for provision of vehicle's surrounding intelligence information. Such measures exemplarily comprise, at a network side node of a cellular system, receiving, from a first mobile entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity, generating said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity based on a position of said first mobile entity, and transmitting, towards said first mobile entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity, wherein said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity.

Claim 1:
An apparatus of a first mobile entity, the apparatus comprising
determining circuitry configured to determine an intention for a movement change of said first mobile entity,
transmitting circuitry configured to transmit, towards a network entity, a request for combined spatiotemporal surroundings characteristics related to surroundings of said first mobile entity and information on said intention, and
receiving circuitry configured to receive, from said network entity, said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity,
wherein:
said combined spatiotemporal surroundings characteristics related to said surroundings of said first mobile entity include location information of at least one second mobile entity in the surroundings of said first mobile entity,
said combined spatiotemporal surroundings characteristics related to
said surroundings of said first mobile entity are related to a section of said
surroundings of said first mobile entity relevant for said movement change of said first mobile entity, and
said information on said intention comprises time interval information indicative of an intention validity time interval of said intention.