Supply of Object Location Information for Autonomous Vehicle Manipulation

Abstract

Embodiments of the present disclosure provide a method, a computer program product, and an apparatus (200) for supplying object location information for the operation of an autonomous vehicle (100). The method includes: receiving (S21) a request for object location information from at least one autonomous vehicle (100). The method includes: obtaining (S23) VRU data from a plurality of vulnerable road user (VRU) data sources (104a-104n), wherein the VRU data includes corresponding VRU locations in a predetermined surrounding environment of the autonomous vehicle (100). Additionally, the method includes: determining (S25) object location information based on the obtained VRU data. Additionally, the method includes: periodically transmitting (S27) the determined object location information to the autonomous vehicle (100).

Description

Technical Field

[0001] The present disclosure generally relates to the field of implementing traffic environment perception in autonomous vehicles. More specifically, the present disclosure relates to computer-implemented methods and apparatuses for providing object location information to autonomous vehicles. Background Art

[0002] One of the most intensively studied and investigated areas in the automotive industry is the field of assisted and autonomous driving technologies. It is expected that vehicles with driving assistance functions and even autonomous vehicles for passenger and cargo transportation will account for an increasingly large share in daily traffic. An autonomous vehicle can sense its surrounding environment and perform the necessary functions to maneuver the vehicle with little or no human intervention.

[0003] An important basis for implementing autonomous vehicles is to reliably and robustly determine the position and trajectory of the vehicle. In addition to its own position, the behavior of all other traffic participants must be observed and predicted, including the recognition of the intentions and gestures of vulnerable road users (VRUs) (e.g., pedestrians and cyclists). Reliable technologies and methods for autonomous vehicles to detect VRUs and other relocatable objects are crucial for ensuring the safety of all relevant parties.

[0004] Multiple technologies can be used to detect objects, such as VRUs, using vehicle-implemented solutions. Such technologies include using image recognition (cameras), radar, and lidar sensors. Vehicle-implemented image processing resources and algorithms are used to classify the detected objects into lanes, traffic lights, vehicles, pedestrians, etc. In combination with a traffic environment model, it is required to identify relevant traffic participants / relocatable objects (e.g., VRUs) for modeling an accurate traffic situation.

[0005] "Sensor and object recognition technologies for self-driving cars, Mario Hirz et al., Computer-Aided Design and Applications, January 2018" discloses the use of sensor technologies for object detection in autonomous vehicles.

[0006] In addition to using the sensors of a specific autonomous vehicle to identify and model the traffic environment, additional inputs can also be collected through vehicle-to-vehicle and / or infrastructure-to-vehicle communication.

[0007] Sharing data streams from multiple input data sources with vehicles improves the safety and reliability of autonomous vehicle maneuvers, but poses challenges to the limited processor capabilities of each autonomous vehicle. Therefore, there is a need to achieve an increased multi-source information supply for autonomous vehicle maneuvers without increasing the corresponding data processing requirements within the autonomous vehicle. Summary of the Invention

[0008] Accordingly, an object of the present disclosure is to provide a method, a computer program product, and a device for object position information supply for autonomous vehicle maneuvers, which aim to alleviate, mitigate, or eliminate all or at least some of the drawbacks of the currently known solutions discussed above.

[0009] This object and other objects are achieved by the method, the computer program product, and the device as defined in the appended claims. The term 'exemplary' is understood in the current context as being used as an instance, an example, or an illustration.

[0010] According to a first aspect of the present disclosure, there is provided a computer-implemented method for object position information supply for autonomous vehicle maneuvers. The method includes: receiving a request for object position information from at least one autonomous vehicle. The method includes: obtaining VRU data from a plurality of vulnerable road user (VRU) data sources, wherein the VRU data includes corresponding VRU positions in a predetermined surrounding environment of the autonomous vehicle. Further, the method includes: determining the object position information based on the obtained VRU data. Further, the method includes: periodically sending the determined object position information to the autonomous vehicle.

[0011] Advantageously, the proposed method can be used to determine object position information, especially using VRU data from additional VRU data sources (such as mobile network operators, user devices, handheld devices, wireless devices, or wireless sensors). Other examples of VRU data sources include traffic cameras and connected wireless transportation units, such as scooters or rental bicycles. Therefore, using multiple VRU data sources to determine object position information and transmitting the determined object position information to an autonomous vehicle improves the reliability of the autonomous vehicle to make more informed decisions based on a more comprehensive understanding of its surrounding environment.

[0012] Embodiments of the proposed method and apparatus can be implemented using an object location provisioning application. The object location provisioning application implements various modules to triangulate and combine VRU data obtained from multiple VRU data sources to determine object location information. Additionally, the object location provisioning application verifies the object location information by allocating confidence levels based on overlapping information obtained from multiple VRU data sources. Thus, the object location provisioning application provides additional processing capabilities for performing these functions, rather than increasing the processing burden within the autonomous vehicle.

[0013] In some embodiments, prior to supplying object location information to the autonomous vehicle, the object location provisioning application provides additional processing capabilities for authentication, authorization, and security functions (such as data anonymization) to enhance the safety and trust of the autonomous vehicle.

[0014] Furthermore, embodiments of the proposed invention can be easily implemented for use on public roads and for autonomous vehicles in limited spaces such as industrial exits, logistics / distribution centers, etc.

[0015] In some exemplary embodiments, obtaining VRU data includes: authenticating the multiple VRU data sources for data ingestion of VRU data; and de-associating the VRU data from VRU identification information. For example, a VRU data source can be authenticated by, for example, using a password to verify the credentials associated with the VRU data source. A VRU data source can be authenticated using advanced authentication methods such as digital certificates, for example, authenticated using a specific authentication protocol (such as SSL / TLS). After authenticating the VRU data source, the VRU data is de-associated from the VRU identification information.

[0016] The proposed object location provisioning application provides additional processing capabilities for authentication, authorization, and security functions (such as data anonymization) to enhance the safety and trust of the autonomous vehicle ecosystem.

[0017] According to a second aspect of the present disclosure, there is provided a computer program product comprising a non-transitory computer-readable medium having stored thereon a computer program including program instructions, the computer program being loadable into a data processing unit and configured to cause the method described in the first aspect to be performed when the computer program is run by the data processing unit.

[0018] Further, according to a third aspect of the present disclosure, there is provided a device for supplying object position information for use in autonomous vehicle maneuvers. The device includes a control circuit configured to receive a request for object position information from at least one autonomous vehicle. The control circuit is configured to obtain VRU data from a plurality of vulnerable road user (VRU) data sources, wherein the VRU data includes corresponding VRU positions in a predetermined surrounding environment of the autonomous vehicle. Further, the control circuit is configured to determine the object position information based on the obtained VRU data. Further, the control circuit is configured to periodically transmit the determined object position information to the autonomous vehicle.

[0019] Other embodiments of the present disclosure are defined in the dependent claims. It should be emphasized that when used in this specification, the term "comprising / including" is used to specify the presence of the stated features, integers, steps or components. It does not preclude the presence or addition of one or more other features, integers, steps, components or combinations thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] As will be apparent from the following more particular description of exemplary embodiments, as illustrated in the accompanying drawings, wherein like reference numerals refer to like parts in the different views. The drawings are not necessarily to scale, but emphasis is placed upon illustrating exemplary embodiments.

[0021] Figure 1 An autonomous vehicle in a multi-source scenario is shown;

[0022] Figure 2 A flowchart showing example method steps implemented in an object position information supply application is disclosed;

[0023] Figure 3 is a signaling diagram showing signal exchange for an object position information supply application in a telecommunications network;

[0024] Figure 4A An object position information supply application in a 4G telecommunications network is disclosed;

[0025] Figure 4B An object position information supply application in a 5G telecommunications network is disclosed;

[0026] Figure 5 is a schematic block diagram showing an example configuration of an object position information supply application and its interfaces;

[0027] Figure 6 A computing environment for implementing an object position information supply application for autonomous vehicle maneuvers according to an embodiment is shown. DETAILED DESCRIPTION

[0028] Aspects of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. However, the devices and methods disclosed herein may be implemented in many different forms and should not be construed as limited to the aspects set forth herein. Like numbers refer to like elements throughout the drawings.

[0029] The terminology used herein is for the purpose of describing particular aspects of the present disclosure only and is not intended to limit the invention. It should be emphasized that when used in this specification, the terms "comprising / including" are used to specify the presence of stated features, integers, steps or components, but do not preclude the presence or addition of one or more other features, integers, steps, components or combinations thereof. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0030] Embodiments of the present disclosure will be described and illustrated more fully hereinafter with reference to the accompanying drawings. However, the solutions disclosed herein may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein.

[0031] It should be understood that when the present disclosure is described in terms of a method, it can also be embodied in one or more processors and one or more memories coupled to the one or more processors, wherein the one or more memories store one or more programs that execute the steps, services and functions disclosed herein when executed by the one or more processors.

[0032] In the following description of the exemplary embodiments, like reference numerals denote like or similar components.

[0033] Figure 1 An autonomous vehicle 100 in a multi-source scenario in a surrounding environment including infrastructure components and vulnerable road users (VRUs) (e.g., pedestrians and cyclists) is shown. In the context of the present disclosure, the term "autonomous vehicle" reflects a vehicle that is capable of sensing its surrounding environment and performing the necessary functions with minimal or no human intervention to maneuver the vehicle from a starting point to a destination. Different levels of autonomous driving have been defined, and as the level increases, the degree of independence of the vehicle in decision-making and vehicle control also increases. Vehicles with autonomous driving capabilities are expected to appear in limited spaces such as ports, logistics / distribution centers, etc. and on ordinary public roads.

[0034] An autonomous vehicle 100 can use different technologies to be able to detect objects in its surrounding environment: image recognition (cameras), radar sensors, and lidar sensors. For example, image processing algorithms are used to classify detected objects such as lanes, traffic lights, vehicles, pedestrians. Ensuring the safety of all relevant parties, especially vulnerable road users (VRUs) such as pedestrians and cyclists, is of utmost importance. Local processing resources within the autonomous vehicle 100 are used to build a 3D LDM (Local Dynamic Map) and to locate / track objects.

[0035] Such a self - contained system is important so that the autonomous vehicle 100 can act based only on its own input data when the vehicle does not have or has a poor / unreliable connection. On the other hand, this limits the potential to leverage the connection and use inputs from other data sources to identify objects and improve the vehicle's perception of its surroundings. Inputs from additional data sources would also enable the vehicle to make more informed decisions, especially considering the limitations of current camera and sensor technologies in adverse weather, physical damage to the equipment, or the presence of obstacles in the VRU's path. The proposed invention addresses the above - mentioned drawbacks by sharing, for example, anonymized object location information obtained from VRU data sources 104a, 104b via a telecommunications network 300 with the autonomous vehicle 100. Thus, the VRU data sources 104a, 104b serve as additional sources for the autonomous vehicle 100 to identify VRUs in a predefined surrounding environment.

[0036] A device implementing an object location supply application provides additional processing capabilities for performing various functions on the obtained VRU data and VRU data sources, such as authentication, ingestion, anonymization, data combination, and verification. Thus, the proposed device allows the use of VRU data obtained from additional VRU data sources 104a, 104b (e.g., user devices, handheld devices, wireless devices, or wireless sensors), which can be obtained using established communication means (e.g., via a telecommunications network 300), to determine object location information. Other examples of VRU data sources include traffic cameras and connected wireless transportation units such as scooters or rental bicycles. Thus, using multiple VRU data sources to determine object location information and transmitting the determined object location information to the autonomous vehicle improves the reliability of the autonomous vehicle 100 to make more informed decisions based on a better perception of the vehicle's surrounding environment.

[0037] Figure 2It is a flowchart showing example method steps implemented in an object location information supply application. In step S21, the method includes receiving a request for object location information from at least one autonomous vehicle 100. In one embodiment, the request includes an identifier of the autonomous vehicle 100. For example, the identifier of the autonomous vehicle 100 may be an International Mobile Subscriber Identity (IMSI) associated with the autonomous vehicle 100, which can be used to track or monitor the autonomous vehicle 100 and / or perform vehicle-to-everything (V2X) communication between the autonomous vehicle and a wireless communication network.

[0038] In step S23, the method includes obtaining VRU data from a plurality of VRU data sources 104a, 104b, where the VRU data includes respective VRU positions in a predetermined surrounding environment of the autonomous vehicle 100. For example, the VRU data corresponds to data obtained from various wireless devices (such as user equipment, wireless cameras, cameras on poles / traffic lights, etc.).

[0039] The plurality of VRU data sources 104a, 104b may include one or more wireless network operators. Additionally, the plurality of VRU data sources 104a, 104b may include various wireless devices, such as but not limited to user equipment (UE), wireless cameras, or wireless sensors.

[0040] In one embodiment, obtaining the VRU data may include: authenticating and / or authorizing the plurality of VRU data sources 104a, 104b for data ingestion of the VRU data in step S24a. For example, the VRU data sources 104a - 104n may be authenticated by, for example, using password verification of credentials associated with the VRU data sources 104a - 104n. The VRU data sources may be authenticated using advanced authentication methods (such as digital certificates), for example, authenticated using a specific authentication protocol (such as Secure Sockets Layer, Transport Layer Security (SSL / TLS)). After authenticating the VRU data sources 104a, 104b, in step S24b, the VRU data may be de-associated from the VRU identification information.

[0041] In step S25, the method further includes: determining object location information based on the obtained VRU data, and performing data fusion, etc., to determine object location information based on the obtained VRU data.

[0042] In one embodiment, the VRU location is identified in the VRU data obtained from multiple VRU data sources 104a, 104b. Additionally, the identified VRU locations for each VRU can be combined to determine object location information. For example, an object (such as a pedestrian) is identified from VRU data sources 104a and 104b. The VRU data obtained from VRU data sources 104a and 104b is used to identify the VRU location of the object. The VRU location obtained from VRU data source 104a is combined with the VRU location obtained from VRU data source 104b to determine the accurate location of the object. It should be noted that one or more location determination techniques or techniques yet to be known can be used to accurately determine object location information based on the obtained VRU data.

[0043] In step S27, the method includes: periodically sending the determined object location information, that is, the object location information of the detected object(s), to the autonomous vehicle 100. For example, the determined object location information is sent to the autonomous vehicle 100 every second. The sending of the object location information to the autonomous vehicle 100 can be periodic or configurable, depending on the requirements of the object location information at the autonomous vehicle 100.

[0044] In one embodiment, the determined object location information can be sent to the autonomous vehicle 100 by generating a report in a predefined format or standard format that includes the determined object location information.

[0045] Furthermore, the generated report with the determined object location information can be sent to the autonomous vehicle 100 periodically (e.g., every second) via a Cooperative Awareness Message (CAM).

[0046] The above steps can be implemented or executed using an object supply application, which can be configured to provide object location information to the autonomous vehicle 100. The object supply application can be located in a device 200 for edge computing (e.g., an edge node including one or more servers). The device 200 can include the necessary control circuits for performing the above method steps.

[0047] In some embodiments, the object supply application can be located in a cloud computing environment or a remote server, which is configured to execute the object supply application to periodically send object location information to the autonomous vehicle 100. The device 200 can include various modules, which can be implemented using hardware and / or software or a combination of hardware and software to perform the method steps. The functions of the various modules of the device 200 are explained in the later part of the specification in conjunction with Figure 5 are explained.

[0048] Figure 3is a signaling diagram showing the signal exchange for an object location information provision application in a telecommunications network 300. The object location provision application can be configured to interact with one or more network entities in the telecommunications network 300 to obtain VRU data. For example, the telecommunications network 300 includes multiple network units, such as base stations (i.e., EUTRAN 302a in a 4G network and NG-RAN 302b in a 5G network), a Mobility Management Entity (MME) 304a / Access and Mobility Management Function (AMF) 304b, a Gateway Mobile Location Center (GMLC) 306, and an Enhanced Serving Mobile Location Center (E-SMLC) 308a / Location Management Function (LMF) 310. It should be noted that the telecommunications network can include Figure 3 other network entities in addition to the entities shown.

[0049] As Figure 3 depicted in, the object location information provision application can be configured to send an S302 location service request to the GMLC 306 via a standard interface. The GMLC 306 sends the location service request S304 to the MME 304a / AMF 304b. After receiving the location service request, the MME 304a / AMF 304b sends the location service request S306 to the E-SMLC 308a / LMF 310 for processing the location service request. The E-SMLC 308a / LMF 310 collaborates with the EUTRAN 302a / NG-RAN 302b to process the S308 location service request.

[0050] The E-SMLC 308a / LMF 310 supports multiple positioning technologies that provide different levels of positioning accuracy. The E-SMLC 308a / LMF 310 calculates the location or positioning information of the object S310 based on the obtained VRU data. Among the available network-based positioning methods, the UE-assisted A-GNSS (assisted-GNSS) positioning method on the control plane provides the best accuracy (~10 meters (m) to 50 meters) and the lowest UE power consumption. It should be noted that more advanced positioning methods or positioning processes (e.g., GNSS-rtk, user plane positioning, etc.) that provide higher accuracy and better UE performance can be implemented at the E-SMLC 308a / LMF 310 for calculating the object's location information.

[0051] In addition, the E-SMLC 308a / LMF 310 then sends a location service response S312 back to the MME 304a / AMF 304b. The MME 304a / AMF 304b then sends the location service response S314 to the GMLC 306 and the GMLC 306 sends the location service response S316 to the object location provision application 200.

[0052] Figure 4a discloses an object location information provision application in a 4G telecommunications network. As shown in Figure 4a, various entities of the 4G telecommunications network include EUTRAN 302a, MME 302a, GMLC 306a, and E-SMLC 308a. The object location information provision application hosted in the device 200 (e.g., a server in the network domain) interacts with the 4G telecommunications network to obtain VRU data. For example, the device 200 communicates with the GMLC 306a through the Open Mobile Alliance Mobile Location Protocol (OMA MLP) interface. The device 200 can be configured to trigger a location service request to the GMLC 306a through the OMA MLP interface. The GMLC 306a and the E-SMLC communicate with the MME 304a through the SLg and SLs interfaces respectively. In addition, the MME 304a and the E-UTRAN 302a interact with each other through the S1 interface. The E-UTRAN 302a sends control signaling to the UE 104a through the LTE-Uu interface.

[0053] The MME 304a monitors the mobility of the UE 104a and sends the mobility information of the UE to the GMLC 306a and the E-SMLC 308a. The E-SMLC 308a implements various positioning technologies to determine the location of the UE 104a. In addition, the location information of the object can be determined based on the location of the UE 104a. The E-SMLC transmits the determined location of the object to the GMLC 306a, and the GMLC 306a then transmits the location information of the object to the device 200 through the OMA MLP interface, as shown in Figure 4a.

[0054] In some embodiments, as defined in 3GPP 36.305 and 38.305, the request for the target UE location can be triggered by the MME 304a or by another entity in the 4G telecommunications network.

[0055] In another embodiment, the location service request can be triggered by the location information provision application implemented in the device 200 through the OMA MLP interface via the GMLC.

[0056] Figure 4b discloses an object location information provision application in a 5G telecommunications network. As shown in Figure 4b, various entities of the 5G telecommunications network include NG-RAN 302B, AMF 304b, GMLC 306a, E-SMLC 308a, and LMF 310. The object location information provision application hosted in device 200 (e.g., a server in a network domain) interacts with the 5G telecommunications network to obtain VRU data. For example, device 200 communicates with GMLC 306a through the OMA MLP interface. Device 200 can be configured to trigger a location service request for LMF 310 through the OMA MLP interface. GMLC 306a and LMF 310 communicate with AMF 304b through the NLg and SLs interfaces, respectively. In addition, AMF 304b and E-UTRAN 302a interact with each other through the N2 interface. NG-RAN 302b sends control signaling to UE 104a through the NR-Uu interface.

[0057] AMF 304b monitors the mobility of UE 104a and sends the mobility information of UE 104a to GMLC 306a and LMF 310. LMF 310 implements various positioning technologies to determine the location of UE 104a. In addition, the location information of the object can be determined based on the location of UE 104a. As shown in Figure 4b, LMF 310 transmits the determined object location to device 200 through the OMA MLP interface.

[0058] In some embodiments, as defined in 3GPP 36.305 and 38.305, a request for the target UE location can be triggered by MME 304a or by another entity in the 5G telecommunications network.

[0059] Figure 5FIG. 0 is a schematic block diagram showing an example configuration of an object location information supply application and its interfaces. The object location supply application is implemented (e.g., in an edge server) as various modules within a device 200 (e.g., within an edge node) for supplying object location information for autonomous vehicle maneuvers. In the context of the present disclosure, "edge" indicates the location where the object location supply application is running, e.g., including the edge node of device 200. The location of the device depends on network characteristics, e.g., telecommunication network characteristics, and the various modules may also be partially distributed among different entities. The application will run at a selected location such that data sharing from the network and other sources to the edge node and from the edge node to the autonomous vehicle meets the latency requirements for the application to serve the use case, e.g., as useful "real-time" data. Thus, in some examples, the edge server runs as close as possible to the VRU data source and the location where the autonomous vehicle operates, e.g., in the mobile network operator (MNO) infrastructure near the road, to reduce latency and offload processing from the vehicle to the edge application. Additionally, introducing the edge application in the MNO infrastructure will enable secure supply of object location information via the MNO 4G or 5G network. Further, positioning the edge application in the MNO infrastructure enables the application to capture some of the required data from the telecommunication network using standardized APIs.

[0060] The device 200 for supplying object location information for autonomous vehicle maneuvers includes a control circuit, as shown, for example, as Figure 6 illustrated.

[0061] The control circuit is configured to receive a request for object location information from at least one vehicle. The control circuit is further configured to obtain VRU data from a plurality of vulnerable road user (VRU) data sources 104a - 104n, where the VRU data includes the respective VRU locations in a predetermined surrounding environment of the autonomous vehicle. The control circuit is further configured to determine object location information based on the obtained VRU data and to periodically transmit the determined object location information to the autonomous vehicle.

[0062] In one embodiment, the device 200 (e.g., the control circuit of the device) includes a data ingestor 202, an authenticator 204, a data anonymizer 206, a data combiner 208, a data verification engine 210, a report generator, a storage device 214, and an interface 216.

[0063] In some embodiments, the VRU data sources 104a - 104n may be authenticated by the authenticator 204 for data ingestion of VRU data by the data ingester 202. The authentication of the VRU data sources 104 - 104n may include verifying the credentials of the VRU data sources 104a - 104n. The most basic authentication method is to use a password. As previously mentioned, more advanced authentication methods (such as digital certificates) using a specific authentication protocol (such as SSL / TLS) are preferred.

[0064] Accordingly, after the authenticator 204 successfully authenticates the VRU data sources 104a - 104n, the control circuit (such as the data ingester 202) may be configured to obtain VRU data from the multiple VRU data sources 104a - 104n. That is, once authenticated, the VRU data sources may send data to the data ingestion layer provided by the data ingester. For some VRU data sources, requests need to be sent (either once or periodically) to trigger data collection. For example, the IMSI (unique UE identifier) of a phone whose location data the telecommunications network is going to collect is going to be sent to the GMLC system in the telecommunications network (4G and 5G) through the OMA MLP 3.2 interface (open and standardized), as explained with reference to FIGS. 4a and 4b. Such request clients are implemented in the data ingestion layer. Thus, the data input to the data ingester is from multiple sources and includes VRU locations (e.g., locations defined in a globally standardized format such as the 1984 World Geodetic System WGS84), timestamps, and other additional data (such as direction, speed, object type, etc.). VRU data includes each VRU location in the predetermined surrounding environment of the autonomous vehicle. For example, the predetermined surrounding environment of the autonomous vehicle 100 may include a distance ranging from 50 meters to 100 meters, etc. The data ingester 202 may be configured to de - associate the VRU data from the VRU identification information when obtaining the VRU data.

[0065] In some embodiments, the control circuit (such as the data ingester 202) may be configured to determine the VRU locations included in the VRU data obtained from the multiple VRU data sources. Additionally, the data ingester 202 may be configured to store the VRU data stored over time in a storage device. The VRU data stored in the storage device 214 may be used to understand and / or important characteristics of the VRU movement patterns along the path of the autonomous vehicle. The VRU data combined with other data such as road accident areas, school areas, etc. can be used to improve the understanding of the surrounding environment of the autonomous vehicle 100.

[0066] The control circuit can be configured, for example with the help of a data anonymizer 206, to anonymize user-specific information from VRU data obtained from a telecommunications network or a mobile network operator. Data anonymization is required for data from sources containing sensitive user information. This step can be performed either by the VRU data source itself (deleting / masking sensitive information, assigning a temporary identifier (ID) for sending to the edge application, etc.), or by the edge application, depending on the deployment model. For example, the data anonymizer 206 can be configured to anonymize user-specific information by deleting the International Mobile Subscriber Identity (IMSI) from the VRU data. Additionally, the data anonymizer 206 can maintain a mapping of the network identifier (i.e., the user ID) to the application-assigned user ID to distinguish data for different users.

[0067] The control circuit can also be configured, for example with the help of a data combiner 208, to combine the VRU locations of each respective VRU. The data can be sent to the data combiner (i.e., the data fusion component), which converts the location inputs in the data into a single standard format (such as WGS84), and for each collection period (every second), fuses the data from multiple sources. For example, the data combiner 208 can be configured to perform data fusion by combining the VRU locations obtained from multiple VRU data sources 104a - 104n (such as wireless devices like user equipment 104a, wireless cameras, and wireless sensors from which data can be obtained via a wireless network). Other examples of data sources include traffic cameras and connected wireless transportation units such as scooters or rental bicycles. For example, the data combiner 208 can be configured to combine the data from multiple VRU data sources for each second period. Additionally, the data combiner 208 can be configured to perform one or more actions on the VRU data, including converting the VRU data into a standard format, compressing the VRU data, extracting the VRU data, etc.

[0068] In some embodiments, the data combiner 208 can be configured to perform data fusion on the VRU data obtained from multiple VRU data sources 104a - 104n in a data verification engine 210 to detect VRUs with different accuracy levels. For example, the data combiner 208 can be configured to fuse data points corresponding to the same object detected by multiple VRU data sources 104a - 104n (when necessary and feasible) to improve the accuracy of the data.

[0069] The control circuit can be configured to verify the object position information by analyzing the VRU position in the VRU data, for example, with the help of the data verification engine 210. The data verification engine 210 can be configured to determine that multiple VRU data sources are detecting the same object. For example, the data verification engine 210 can be configured to perform data fusion (when necessary and feasible) on the data points corresponding to the same object detected by multiple VRU data sources 104a - 104n to improve the accuracy of the data. In addition, any duplicate data points that are observed and confirmed to belong to the same object during the data fusion process of the data can be filtered to improve the determination of the object position information.

[0070] The data verification engine 210 can also be configured to detect the data points belonging to the detected object identified by multiple VRU data sources. In addition, the data verification engine 210 can be configured to identify the redundant data points of the object detected by multiple VRU data sources. In addition, the data verification engine 210 can be configured to allocate confidence levels based on the overlapping information from multiple VRU data sources, and the data verification engine 210 can be configured to use the allocated confidence levels to verify the object position information.

[0071] The control circuit can also be configured to generate a report in a predetermined format or standard format with the determined object position information, for example, with the help of the report generator 212. The generated report with the determined object position information is periodically (e.g., every second) sent to the autonomous vehicle 100 via the interface 216 using cooperative awareness messages (CAM). The interface 216 can be a standard interface, such as an interface defined by standardized 3GPP or ETSI. The report generator is responsible for generating messages in a standard format, which have basic information (the position data points of the VRU) and possible additional information (such as the movement speed of the VRU, the VRU type (cyclist, pedestrian, etc.), the movement direction, the predicted direction, etc.). Then, the report generator will send standardized messages to the connected autonomous vehicles via the standardized interface. The reports currently being considered are general reports for the entire "area" of interest (e.g., the locations where the autonomous vehicle can operate). The same report can be sent to each vehicle. In the future, this solution can evolve to send more personalized messages to each connected vehicle based on the vehicle's speed, position, the currently interesting circular area around the vehicle, etc. This information will be collected via the standardized interface.

[0072] Figure 6FIG. 0 shows a computing environment 600 that implements an object location information supply application for the maneuvering of an autonomous vehicle 100 according to an embodiment. As shown, the computing environment 600 includes at least one data processing unit 604 equipped with a control unit 602 and an arithmetic logic unit (ALU) 603, a memory 605, a storage device 606, a plurality of networking devices 608, and a plurality of input / output (I / O) devices 607. The data processing unit 604 is responsible for processing the instructions of the algorithm. The data processing unit 604 receives commands from the control unit to perform its processing. In addition, any logical and arithmetic operations involved in the execution of the instructions are computed by the ALU 603.

[0073] The overall computing environment 600 may include multiple homogeneous and / or heterogeneous cores, multiple CPUs of different kinds, special media, and other accelerators. The data processing unit 604 is responsible for processing the instructions of the algorithm. In addition, multiple data processing units 604 may be located on a single chip or on multiple chips.

[0074] Algorithms including the instructions and code required for the implementation are stored in the memory 605 or the storage device 606 or both. When executed, the instructions may be retrieved from the corresponding memory 605 and / or storage device 606 and executed by the data processing unit 604.

[0075] In the case of any hardware implementation, various networking devices 608 or external I / O devices 607 may be connected to the computing environment to support the implementation through the networking devices 608 and I / O devices 607.

[0076] The embodiments disclosed herein may be implemented by at least one software program that runs on at least one hardware device and performs network management functions to control these units. Figure 6 The units shown in FIG. include blocks that may be at least one of a hardware device or a combination of a hardware device and software modules.

[0077] The foregoing description of specific embodiments will so fully reveal the general nature of the embodiments herein that those skilled in the art may, by applying current knowledge, readily modify and / or adapt various applications of such specific embodiments without departing from the general concept, and, accordingly, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Accordingly, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein may be practiced with modifications within the scope of the present disclosure.

Claims

1. A computer-implemented method of an object supply application performed by a device (200) for object location information supply for the operation of an autonomous vehicle (100), the method comprises: receiving (S21) a request for object location information from at least one autonomous vehicle (100); obtaining (S23) VRU data from a plurality of vulnerable road user (VRU) data sources (104a - 104n), at least some of the VRU data in the VRU data being associated with a user equipment (UE) in a telecommunication network (300), wherein the UE is associated with an international mobile subscriber identity (IMSI), wherein the VRU data includes corresponding VRU positions in a predetermined surrounding environment of the autonomous vehicle (100), and wherein obtaining VRU data comprises: authenticating (S24a) the plurality of VRU data sources (104a - 104n) for data ingestion of VRU data; and disassociating (S24b) the VRU data from VRU identification information by removing the IMSI from the VRU data and assigning a temporary identifier to the UE; determining (S25) the object location information based on the obtained VRU data, wherein determining object location information comprises: identifying (S26a) the VRU positions included in the VRU data from the plurality of VRU data sources (104a - 104n); combining (S26b) the VRU positions of each corresponding VRU; and periodically sending (S27) the determined object location information to the autonomous vehicle (100).

2. The computer-implemented method according to claim 1, wherein, the request includes an identifier of the at least one autonomous vehicle (100).

3. The computer-implemented method according to any one of the preceding claims, wherein, the plurality of VRU data sources (104a - 104n) includes one or more mobile network operators, and wherein the VRU data corresponds to wireless device data.

4. The computer-implemented method according to claim 1 or 2, wherein, the plurality of VRU data sources (104a - 104n) additionally includes wireless devices, wireless cameras or wireless sensors.

5. The computer-implemented method according to claim 1 or 2, wherein, the method includes: anonymizing user-specific information in the VRU data obtained from the one or more mobile network operators (300).

6. The computer-implemented method according to claim 1 or 2, wherein, the method includes verifying the object location information by: determining that the plurality of VRU data sources (104a - 104n) are detecting the same object; detecting data points belonging to the detected object identified by the plurality of VRU data sources (104a - 104n); identifying redundant data points of the object detected by the plurality of VRU data sources (104a - 104n); Assign a confidence level based on overlapping information from the multiple VRU data sources (104a - 104n); Use the assigned confidence level to verify the object location information; and The verification is performed before sending the determined object location information to the autonomous vehicle (100).

7. The computer - implemented method according to claim 1 or 2, wherein, The method includes: generating a report in a predefined format with the determined object location information.

8. The computer - implemented method according to claim 7, wherein, The generated report with the determined object location information is periodically sent to the autonomous vehicle via a Cooperative Awareness Message CAM.

9. A computer program product including a non - transitory computer - readable medium (800) having stored thereon a computer program including program instructions, the computer program being loadable into a data processing unit and configured to cause the method according to any one of claims 1 to 8 to be executed when the computer program is run by the data processing unit.

10. An apparatus (200) for an object supply application for supplying object location information for the maneuvering of an autonomous vehicle (100), the apparatus including a control circuit configured to perform the following operations: Receive (S21) a request for object location information from at least one autonomous vehicle (100); Obtain (S23) VRU data from multiple Vulnerable Road User (VRU) data sources (104a - 104n), at least some of the VRU data in the VRU data being associated with a User Equipment (UE) in a telecommunication network (300), wherein, The UE is associated with an International Mobile Subscriber Identity (IMSI), wherein the VRU data includes corresponding VRU positions in a predetermined surrounding environment of the autonomous vehicle (100), and obtaining VRU data includes: Authenticate (S24a) the multiple VRU data sources (104a - 104n) for data ingestion of VRU data; and Disassociate (S24a) the VRU data from the VRU identification information by removing the IMSI from the VRU data and assigning a temporary identifier to the UE; Based on the obtained VRU data, determine (S25) the object location information, wherein determining the object location information includes: Identify (S26a) the VRU positions included in the VRU data from the multiple VRU data sources (104a - 104n); Combine (S26b) the VRU positions of each corresponding VRU; and Periodically send (S27) the determined object location information to the autonomous vehicle (100).

11. The apparatus according to claim 10, wherein, The request includes an identifier of the at least one autonomous vehicle (100).

12. The apparatus according to claim 10 or 11, wherein, The plurality of VRU data sources (104a - 104n) includes one or more mobile network operators, and wherein, the VRU data corresponds to wireless device data.

13. The apparatus according to claim 10 or 11, wherein, the plurality of VRU data sources (104a - 104n) additionally includes wireless devices, wireless cameras, or wireless sensors.

14. The apparatus according to claim 10 or 11, wherein, the control circuit is configured to: anonymize user - specific information in the VRU data obtained from the one or more mobile network operators (300).

15. The apparatus according to claim 10 or 11, wherein, the control circuit is configured to verify the object location information by: determining that the plurality of VRU data sources (104a - 104n) are detecting the same object; detecting data points belonging to the detected object identified by the plurality of VRU data sources (104a - 104n); identifying redundant data points of the object detected by the plurality of VRU data sources (104a - 104n); allocating a confidence level based on overlapping information from the plurality of VRU data sources (104a - 104n); using the allocated confidence level to verify the object location information; and the verification is performed before sending the determined object location information to the autonomous vehicle (100).

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