Mobile target identification methods and nodes

Abstract

The present disclosure provides a method for identifying a mobile target performed at a first node, the method comprising: receiving a first target status report from a first perception unit and a second target status report from a second perception unit; matching first target identifiers of one or more mobile targets contained in the first target status report with second target identifiers of the one or more mobile targets contained in the second target status report one by one, wherein the matched first target identifier and the second target identifier jointly identify the same mobile target; and sending a matching report to a second node associated with the first node, the matching report comprising matching information between the first target identifier and the second target identifier and motion status information of the jointly identified same mobile target.

Description

Technical Field

[0001] The present invention relates to traffic monitoring, and in particular to the identification of moving objects in traffic. Background Art

[0002] At present, with the improvement of transportation infrastructure, intelligent nodes are usually set up on the road to realize the coordinated management of vehicles and roads, so as to monitor traffic flow and vehicle status in real time. Intelligent nodes usually include vehicle perception units (PNode) such as traffic cameras, radars, roadside units (RSUs), etc. Roadside units (RSUs) are important equipment widely installed to realize intelligent transportation, and are also one of the key equipment in vehicle-road collaborative application scenarios. In the application scenario of vehicle-road collaboration, RSU is generally installed on the roadside, such as roadbed equipment such as traffic lights, and is responsible for receiving the location and body status information of the vehicle, such as receiving the vehicle status report sent by the on-board unit (OBU) equipped on the vehicle, combining with information provided by other perception units such as cameras, and through the calculation of relevant data, identifying the vehicle and judging its movement trajectory, and sending warning information to relevant vehicles when necessary, so as to realize real-time information interaction with the vehicle.

[0003] However, in reality, intelligent nodes or roadside units (RSUs) in intelligent nodes and other sensing units (PNodes) are generally only deployed in key or interesting areas, and cannot be widely or continuously laid out. Therefore, vehicle status reports sent by vehicles cannot always be received by RSUs, and accordingly, vehicles cannot obtain useful information from RSUs in a timely manner. In addition, when a vehicle enters the coverage of another RSU from the coverage of one RSU, the computing center or node related to the other RSU usually needs to re-identify the vehicle and recalculate related data, which will also waste part of the computing power of this computing node. Summary of the invention

[0004] The present invention provides an improved vehicle identification technology, which can reduce the next node's computational requirements for re-matching a mobile target by sending an identified vehicle matching report to the next node; in addition, the next node can directly use the matching report from the previous node instead of receiving a mobility report from the mobile target, which is particularly useful when the mobility report of the mobile target cannot be received.

[0005] According to one aspect of the present invention, there is provided a method for identifying a mobile target performed at a first node, comprising: receiving a first target status report from a first perception unit and a second target status report from a second perception unit; matching first target identifiers of one or more mobile targets contained in the first target status report with second target identifiers of the one or more mobile targets contained in the second target status report one by one, wherein the matched first target identifier and second target identifier jointly identify the same mobile target; and sending a matching report to a second node associated with the first node, the matching report including matching information of the first target identifier and the second target identifier and motion status information of the jointly identified same mobile target.

[0006] According to one aspect of the present invention, a node is provided, comprising: at least one perception unit, configured to: receive a first target status report regarding one or more mobile targets and a second target status report regarding the one or more mobile targets; a computing node, configured to: match the first target identifiers of the one or more mobile targets contained in the first target status report with the second target identifiers of the one or more mobile targets contained in the second target status report one by one, wherein the matched first target identifiers and the second target identifiers jointly identify the same mobile target; and send a matching report to another node associated with the node, the matching report including matching information between the first target identifier and the second target identifier and motion status information of the jointly identified same mobile target.

[0007] According to one aspect of the present invention, there is provided a method for identifying a mobile target performed at a node, comprising: receiving a matching report from another node associated with the node, the matching report comprising matching information of a first target identifier and a second target identifier of the same mobile target and target status information, wherein the node and the other node are associated with each other through at least one associated feature; receiving a target status report for at least one mobile target from a target perception unit, the target status report comprising a third identifier of the at least one mobile target and the at least one associated feature; and matching the third identifier with the first target identifier or the second target identifier based on the at least one associated feature. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A diagram schematically showing a traffic scene to which an embodiment of the present invention is applied;

[0009] Figure 2 A schematic diagram showing a smart node according to one embodiment;

[0010] Figure 3 A flow chart showing identification of a mobile target at a first node according to one embodiment;

[0011] Figure 4 A flow chart is shown for identifying a mobile target at a second node according to one embodiment. DETAILED DESCRIPTION

[0012] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only for the purpose of illustrating the present invention and are not restrictive. In the following embodiments, the concept of the present invention is illustrated by taking a car traveling on the road as an example. However, after reading this disclosure, it is not difficult to understand that the disclosed solution is applicable to all vehicles (for example, four-wheeled vehicles, three-wheeled vehicles, two-wheeled vehicles, etc.) and pedestrians. In one embodiment, as long as the vehicle and the pedestrian have the ability to communicate with the external RSU, for example, the vehicle is equipped with an OBU or the driver inside it is connected to the RSU through a mobile phone, and the pedestrian can also communicate with the RSU through the mobile phone.

[0013] Figure 1 A schematic diagram of a traffic scene according to an embodiment of the present invention is shown. As shown in the figure, a plurality of target perception units are arranged on the traffic road, such as the road side unit RSU (Road Side Unit) and the traffic monitoring unit TMU (Traffic Monitor Unit) schematically shown in the figure. Here, only one RSU and one TMU are schematically shown in the figure. TMU can be a video capture device with one or more cameras, a radar such as a millimeter wave radar or an activation radar, etc. In the following example, TMU is taken as an example as a video capture device. RSU can be a transceiver implemented by any wireless technology, such as DSRC (Dedicated Short Range Communication) technology or cellular vehicle-to-everything (C-V2X). RSU is arranged on one side or two sides or above the road, and is used to receive signals from vehicles or people driving or walking on the road, or to send warning signals to vehicles or pedestrians when necessary. As shown in the figure, the video capture device TMU uses a camera to capture three cars Car1, Car2, and Car3 on the current road. These three vehicles can drive on the same lane or on different lanes. For the video capture device TMU, the vehicle Car1 passed earlier, while the vehicle Car3 came from behind and is therefore farther away from the camera of the TMU.

[0014] The video capture device TMU can calculate the status of each car based on the video or one or more image frames captured for each car, including the vehicle's position, motion characteristics (such as speed, acceleration), lane occupation information, driving direction information, classification information of the moving target (for example, in this embodiment, it is a car), etc.; at the same time, the video capture device TMU assigns an identification number to each car, for example, the first car Car1 seen is assigned an identification number of ID_1, the second car Car2 seen is assigned an identification number of ID_2, and the third car Car3 seen is assigned an identification number of ID_3. Therefore, the video capture device TMU can form a monitoring status report Monitor_Report of the captured vehicle and transmit it to the computing node, that is, CNode shown in the figure, where the transmitted vehicle status report can be expressed as follows:

[0015]

[0016] Among them, V represents the vehicle speed, A represents the acceleration of the vehicle, O represents the direction of the vehicle, P represents the current position of the vehicle, L represents the lane occupied by the vehicle, and C represents the classification of the vehicle, such as a car in this example. It should be noted that the above is only an exemplary example of the content of the vehicle status monitoring report Monitor_Report, but the present invention is not limited to this, and other motion information or parameters may also be included, for example, the direction of travel, time information such as a timestamp, and vehicle-specific features such as color, license plate, and other information.

[0017] In addition, the moving vehicles Car1, Car2, and Car3 will continuously report the vehicle status information detected by themselves to the roadside unit RSU during the movement. For example, the vehicle can use various sensors installed on itself to determine or related status parameters, or calculate other necessary parameters based on the determined status parameters, such as using the GPS sensor installed on the vehicle to determine the current vehicle's position coordinates, and using the speed sensor to detect the vehicle's driving speed. The roadside unit RSU receives the mobility status report Mobility_Report sent by the vehicles Car1, Car2, and Car3. The Mobility_Report sent by each vehicle also includes a specific identification number for each vehicle, such as a vehicle identification number, etc., which are referred to as ID below. A , ID B ,ID C To represent the vehicle identification number contained in the mobility report sent by the vehicle. Therefore, the mobility report Mobility_Report received by the roadside unit RSU can be represented as follows:

[0018]

[0019] The roadside unit RSU also transmits the received mobility report Mobility_Report to the computing node CNode. According to this embodiment of the present invention, the computing node CNode matches the monitoring report Monitor_Report with the information in the mobility report Mobility_Report, for example, determining the information in the Mobility_Report represented by ID A The identified vehicle should correspond to which vehicle in Monitor_Report. Here, the computing node CNode can use any known method in the prior art to achieve information matching, for example, by using a parameter comparison method, if the parameters (V1, A1, O1, P1, L1, C1) and (V1′, A1′, O1′, P1′, L1′, C1′) best match, then the computing node CNode determines that the identification ID_1 of the first vehicle Car1 assigned by the traffic monitoring unit TMU is the same as the identification ID in the mobility report Mobility_Report. A They represent the same vehicle, so the two are paired and associated, for example, with (ID_1, ID A ) indicates that, or the computing node CNode can use two identifiers ID_1 and ID A Connect to form a new ID A _ID_1 is the matching result. On the contrary, if the parameters (V1, A1, O1, P1, L1, C1) and (V2′, A2′, O2′, P2′, L2′, C2′) are the best matches, then the identifier ID_1 is matched with ID B For ease of explanation, in this example, it is assumed that the computing node CNode finally determines the ID A The indicated vehicle is Car1, ID B The indicated vehicle is Car2, ID C The indicated vehicle is Car3, so an associated pair is formed (ID_1, ID A )、(ID_2,ID B )、(ID_3,ID C After the matching relationship is established, the computing node can use the vehicle motion state information indicated by ID_1 in the monitoring report Monitor_Report and the vehicle motion state information indicated by ID_2 in the mobility report Mobility_Report to AThe indicated vehicle status information is used to comprehensively evaluate the motion state parameters of Car1. For example, the motion state parameters in the monitoring report Monitor_Report can be used to adjust the vehicle motion state parameters in the mobility report Mobility_Report. For another example, the motion state parameters in Monitor_Report and Mobility_Report are adjusted by calculating the adjustment function to adjust the vehicle motion state parameters, thereby obtaining a more accurate estimation of the position, speed, acceleration, driving direction, lane number and other parameters of the vehicle Car1. Here, (V1″, A1″, O1″, P1″, L1″, C1″) represents the update state determined by the computing node CNode for Car1 based on the matching monitoring report and mobility report. Similarly, for cars Car2 and Car3, the computing node CNode determines the update state based on their respective matching monitoring reports and mobility reports as follows:

[0020] Car2:(V2″,A2″,O2″,P2″,L2″,C2″)

[0021] Car3: (V3″,A3″,O3″,P3″,L3″,C3″).

[0022] In addition, according to another embodiment of the present invention, when determining the matching relationship between the vehicles in Monitor_Report and Mobility_Report, the computing node CNode may also provide a matching degree parameter of the two to characterize, for example, the degree of mutual matching between parameters (V1, A1, O1, P1, L1, C1) and (V1′, A1′, O1′, P1′, L1′, C1′), where the matching degree is represented by M. Any known technology in the prior art may be used to calculate the matching degree, for example, the matching degree may be calculated based on the Mahalanobis distance, and the matching degree may be represented in the form of a floating point number, such as 0.8, or in the form of an interval, for example, 0 represents a low matching degree and 1 represents a high matching degree.

[0023] It should be pointed out here that although in the above description, the computing node CNode and the traffic monitoring units TMU and RSU are described in a separate form, it is not difficult to understand that these devices can also be integrated together to achieve the above functions. Therefore, for the sake of simplicity, the computing node CNode and the traffic monitoring units TMU and RSU are collectively referred to as intelligent nodes INode, and the traffic monitoring unit TMU can be any sensor or detection device with information perception capabilities, such as a capture device with camera capabilities, laser radar and millimeter wave radar, etc. RSU is a communication device that complies with wireless communication protocols such as LTE, 5G, and the Internet of Things protocol IoT.

[0024] In addition, it should be noted that the intelligent node INode can use only a part of the parameters selected from all the motion parameters included in Monitor_Report and Mobility_Report when establishing a match, rather than all the motion parameters. In addition, in the above embodiment, the intelligent node INode uses RSU and video capture device as target perception units as examples to illustrate the establishment of vehicle matching based on the vehicle status reports Monitor_Report and Mobility_Report of the two, but the present invention is not limited to this. For example, the intelligent node INode can also match the target vehicle monitored by the video capture device and the laser radar based on the monitoring status reports from two TMUs such as the video capture device and the laser radar.

[0025] According to the present invention, in order to make full use of the more accurate vehicle status information calculated by the current intelligent node, such as INode_1, and avoid the subsequent intelligent node, such as INode_2, from re-matching when receiving the mobility report and the monitoring report, resulting in a waste of computing power, or the inability to receive the vehicle's mobility report Mobility_Report due to the limited wireless coverage of the RSU in INode_2 or a failure, according to an embodiment of the present invention, INode_1 and INode_2 are associated, and the associated INode_1 and INode_2 can be, for example, two nodes located on the same column but in different directions, that is, two nodes set back to back, and the two communicate through wired or wireless means. Of course, as needed, the nodes INode_1 and INode_2 can also be a certain distance apart and located at different positions. According to an embodiment of the present invention, INode_1 and INode_2 can be pre-associated and configured through one or more parameters, and these parameters include but are not limited to lane information (such as the number of lanes, lane markings, lane directions), area information (such as area markings, area segmentation information), driving direction information, and traffic restriction information, such as whether lane changes are allowed (such as double yellow lines), speed limit requirements, etc. For example, for nodes INode_1 and INode_2 that are set back to back on a column, the cameras in INode_1 and INode_2 monitor the traffic conditions on both sides of the column respectively. When INode_1 and INode_2 are associated with lane numbers, in order to ensure consistent recognition of lane numbers, for example, for lanes 1, 2, and 3 from left to right observed by the camera of INode_1, the camera in INode_2 should identify the corresponding lanes in order from right to left. When INode_1 and INode_2 are associated with driving direction, INode_2 should identify the vehicle entry direction observed by the camera of INode_1 as the vehicle exit direction. In addition, in another embodiment, this association can also be achieved by combining driving direction with speed limit. For example, the cameras in INode_1 and INode_2 are respectively set on adjacent columns, and the camera directions are consistent. The observation range of INode_1 is discontinuous with the observation range of INode_2. The far-end observation range of INode_1 can be set, for example, 5 meters more is the near-end observation range of INode_2. Then, by presetting "along the road direction, 5m", the observation ranges of the two cameras can be associated. When a vehicle leaves the observation range of INode_1, by inferring the vehicle's speed and the preset value of 5m, it can be determined how much time it will take for the vehicle to appear in the observation range of INode_2.

[0026] Figure 2Two associated intelligent nodes INode_1 and INode_2 are shown, where the intelligent node INode_1 has used the target perception unit (such as the traffic monitoring unit TMU, the roadside unit RSU) and the computing node CNode to match the mobility report of vehicles Car1, Car2, and Car3 with the monitoring report, that is, (ID_1, ID A )、(ID_2,ID B )、(ID_3,ID C ), the intelligent node INode_1 can generate a matching report Match_report1, which contains not only the identification of vehicles from different target sensing units (such as ID_1, ID A ) has been matched, and also includes the matched identifiers such as (ID_1, ID A ) are the vehicle status information of the same mobile vehicle, such as Car1, that is commonly identified by the intelligent node INode_1. It should be noted that the vehicle status information here can be the vehicle status report re-updated by the intelligent node INode_1 based on, for example, Monitor_Report1 and Mobility_Report1 as described above, or it can be a vehicle status report derived from Monitor_Report1 or Mobility_Report1, or other status reports about the vehicle obtained in other ways. The intelligent node INode_1 can transmit the matching report Match_report1 to the subsequently associated intelligent node INode_2, so that INode_2 can determine the status of the vehicle.

[0027] In the following description, the vehicle status report updated based on Monitor_Report1 and Mobility_Report1 is still taken as an example to illustrate Match_report in the embodiment of the present invention, wherein the matching report Match_report1 established by the node INode_1 is expressed as follows:

[0028]

[0029] Where, ID A _ID1 indicates that the ID matching has been completed for vehicle Car1. B _ID2 indicates that the ID matching has been completed for vehicle Car2. C_ID3 indicates that the identification match has been completed for vehicle Car3, and M1, M2, and M3 respectively indicate the matching degree between the mobility report and the monitoring report for vehicles Car1, Car2, and Car3. It can be seen that INode_1 can notify INode_2 using the matching report Match_Report1. The meaning of the notification information can be: for the same vehicle, the matching of the mobile vehicle has been completed, and the vehicle status of the vehicle at the previous time can be known.

[0030] It should be pointed out here that the matching report generated by the intelligent node can be a report that changes over time. For example, the matching report is updated at a certain period (such as 0.5s) and notified to other intelligent nodes associated with it at a certain period (such as 1s or 0.5s).

[0031] It should be noted here that INode_1 can include as much vehicle status information as possible in Match_Report1, so as to provide all necessary information for subsequent intelligent node INode_2 to perform data calculation. The following table shows an exemplary description of the vehicle parameter information transmitted in Match_Report1, in which the vehicle ID in the mobility report, i.e., ID A The vehicle ID1 assigned by the sensing unit is associated and stored in the same table to indicate that the two have established a pairing.

[0032] Transfer parameters in Match_Report

[0033]

[0034]

[0035] INode_2 can continue to perform other data calculation or monitoring tasks based on Match_Report1 transmitted by INode_1 without having to re-perform matching processing. For example, when node INode_2 cannot receive the vehicle's mobility report, it can directly use Match_Report1 from INode_1, or node INode_2 can ignore the vehicle's mobility report and directly use Match_Report1 from INode_1. As an example, for example, when node INode_2 is associated with node INode_1 through lane numbers, if the camera of node INode_2 captures pedestrians or foreign objects on the second lane, it can directly send an alarm message to the vehicle Car2 (whose lane parameter L2=2) occupying the second lane identified in Match_Report1. Therefore, the need to determine who occupies lane No. 2 and other matching and judgment processing is avoided, which not only saves the computing power of INode_2, but also improves efficiency.

[0036] As another application example, the node INode_2 can also use Match_Report1 and the monitoring report Monitor_Report transmitted by the target sensing unit TMU in the node INode_2 to recalculate the state parameters of the vehicle. As in the above example, since the node INode_2 is associated with the node INode_1 through the lane number, the node INode_2 can associate Match_Report1 with the vehicle with the same lane number in the monitoring report Monitor_Report transmitted by the sensing unit TMU, that is, (ID_2', ID B ), where ID_2' represents the identification number assigned by the TMU in Node_2 to the vehicle Car2 occupying the second lane. B is the identification number of the vehicle occupying the second lane in Match_Report1. Thus, INode_2 can use the vehicle status information indicated by ID_2' in the monitoring report and the vehicle status information indicated by ID in Match_Report1 to identify the vehicle occupying the second lane. B The indicated vehicle status information is used to recalculate the status of the vehicle Car2 (V2″′, A2″′, O2″′, P2″′, L2″′, C3″′). As a result, INode_2 can generate a matching report Match_report2 based on the updated vehicle status and transmit it to the next intelligent node associated with it, such as INode_3. As mentioned above, the information included in the matching report Match_report2 is not necessarily limited to the updated status information generated by the node INode_2, but can also be vehicle status information obtained in other ways.

[0037] It should be noted here that INode_2 can take into account the matching degree information M contained in Match_Report1 when calculating the updated vehicle state. The matching degree identifies the credibility of the vehicle state calculated by the intelligent node INode_1. The higher the matching degree, the higher the credibility of the vehicle state reported in the matching report. In an example, INode_2 will be higher than the predetermined matching threshold value M. T The matching result corresponding to the matching degree is used as the output of the node to judge the matching, and the value below the matching threshold M T The matching result corresponding to the matching degree is taken as the matching result that needs to be updated. For example, for a target with a matching degree lower than 0.8, INode_2 continues to obtain the mobility report and monitoring report of the target and matches the two reports of the target again. In another example, when receiving multiple matching reports from INode_1 continuously, INode_2 determines that the matching degree in the multiple matching reports is higher than the matching threshold value M. TThe proportion of matching results, when the proportion is greater than the proportion threshold R T When the ratio is less than the ratio threshold R, INode_2 takes the matching report as the matching report output by this node. T , INode_2 will obtain the mobility report and detection report and re-match all targets.

[0038] Figure 3 A flowchart for facilitating the recognition of mobile vehicles is shown according to an example of the present invention, which is executed at a node such as INode_1. As shown in the figure, in step 301: INode_1 receives a first target state report for one or more mobile vehicles from a first target perception unit and a second target state report for one or more mobile vehicles from a second target perception unit. For example, in this example, the first target perception unit may be a traffic monitoring unit TMU, such as a video capture device, which generates a monitoring report Monitor_Report by monitoring a plurality of mobile vehicles Car1, Car2, Car3, ..., and the detection report Monitor_Report includes not only the identification assigned by TMU to each vehicle Car1, Car2, Car3, ...; but also the state calculated by TMU for each vehicle using the captured video, including the vehicle's position, speed, acceleration, occupied lane, driving direction, etc. For example, at a certain time t, INode_1 receives a detection report Monitor_Report of three vehicles from the traffic monitoring unit TMU, and the detection report Monitor_Report contains the identification numbers ID_1, ID_3, ID_3 assigned by the traffic monitoring unit TMU to the observed vehicles Car1, Car2, Car3.

[0039] In this example, the second target sensing unit may be a roadside unit RSU, which is used to receive the mobility report Mobility_Report sent by each vehicle Car1, Car2, and Car3. The mobility report contains the vehicle's own identification number ID A ,ID B ,ID C .

[0040] In step 303, INode_1 matches the monitoring report from the traffic monitoring unit TMU with the mobility report from the vehicle, and establishes the IDs of Car1, Car2, and Car3 contained in the mobility report. A ,ID B ,ID C The one-to-one matching relationship between the identification ID_1, ID_3, ID_3 of the vehicles Car1, Car2, Car3 contained in the monitoring report is to determine the vehicle ID_1, ID_3, ID_3 in the mobility report. AThe identified vehicle should correspond to the vehicle in the monitoring report, thus forming an associated matching pair, for example (ID_1, ID A )、(ID_2,ID B )、(ID_3,ID C ).

[0041] In step 305, INode_1 generates a matching report Match_report1, which includes the matching information between the vehicle identification in the mobility report and the vehicle identification in the monitoring report and the status information of the same vehicle identified by the matched identifications. Here, the node INode_1 can use the vehicle status information indicated by ID_1 in the monitoring report and the vehicle status information indicated by ID_2 in the mobility report to identify the vehicle status information. A The indicated vehicle status information makes a more precise adjustment to the status parameters of the same vehicle Car, thereby obtaining a more accurate estimation of parameters such as the vehicle position, speed, acceleration, driving direction, position, and lane number, and generating an updated vehicle status. Thus, INode_1 can put the vehicle status information into the matching report Match_report1. Of course, INode_1 can also put the unprocessed vehicle status information from Monitor_Report and Mobility_Report or the vehicle status information obtained in other ways into the matching report Match_report1. In addition, when determining the matching relationship between the mobility report Mobility_Report sent by the vehicle and the monitoring report Monitor_Report from the RSU, the node INode_1 can also give a matching degree parameter of the two to characterize, for example, the degree M of mutual matching between the parameters (V1, A1, O1, P1, L1) and (V1′, A1′, O1′, P1′, L1′).

[0042] In step 307, INode_1 sends a matching report Match_Report1 to the pre-associated INode_2. Thus, by sending the matching report Match_Report1, INode_1 informs the node INode_2 of the matching result. The matching result may indicate that a match has been established, and the associated node INode_2 may perform other processing based on the matching report.

[0043] Figure 4The mobile vehicle identification process performed by the associated node INode_2 is shown. In step 401, the node INode_2 receives a matching report Match_Report1 from the associated node INode_1, in which the previous node INode_1 has established an identification match for the mobile vehicle traveling on the current road and updated the vehicle status. Here, the node INode_2 is associated with the node INode_1 through at least one associated feature Ass_Feature, such as lane information, driving direction information, traffic restriction information, etc.

[0044] In step 403, INode_2 receives a monitoring report Monitor_Report2 from a target sensing unit associated with INode_2, where the monitoring report Monitor_Report2 includes an identifier assigned by the target sensing unit to the mobile vehicle and at least one associated feature Ass_Feature, such as lane information.

[0045] In step 405, INode_2 associates the matching report Match_Report1 with the vehicles having the same value of the associated feature Ass_Feature in the monitoring report Monitor_Report2 based on the associated feature Ass_Feature, for example, matching the identifier contained in the mobility report Mobility_Report1 sent by the vehicle to INode_1 with the identifier assigned by the target sensing unit to the monitored vehicle contained in the monitoring report Monitor_Report2. For example, when the node INode_2 is associated with the node INode_1 through lane information, the node INode_2 can associate the vehicles having the same second lane information in Match_Report1 and Monitor_Report2, so as to give an example, INode_2 can generate Match_Report2 based on the matching report Match_Report1 and the monitoring report Monitor_Report2, and transmit it to the next intelligent node associated with it, such as INode_3.

[0046] It should be noted here that, when the matching report Match_report1 sent by the node INode_1 includes the matching degree M, INode_2 can further determine the matching degree of each vehicle in step 405, and determine the importance of using the matching report Match_Report1 sent by INode_1 in, for example, recalculating the status of the vehicle based on the matching degree. For example, when receiving multiple matching reports about vehicle Car1 from INode_1 in succession, it is determined that the matching degree of each matching report is higher than the matching threshold value M. T When the proportion is greater than the proportion threshold RT When the proportion is less than the ratio threshold value R, INode_2 will use the matching report with the highest matching degree as the matching report output by this node and transmit it to the next associated node INode_3; and when the proportion is less than the ratio threshold value R T , INode_2 can continue to obtain the mobility report and detection report of each vehicle and re-match them.

[0047] Although the embodiments of the present invention are shown and described in detail through the accompanying drawings and preferred embodiments, the present invention is not limited to these disclosed embodiments, and those skilled in the art may make any modifications based on the above detailed disclosure, including merging, replacing, adding and deleting features, etc. These solutions should be deemed to fall within the scope of protection defined by the attached claims.

Claims

1. A method for identifying a mobile target performed at a first node, comprising: receiving a first target status report from the first sensing unit and a second target status report from the second sensing unit; Matching the first target identifiers of the one or more mobile targets included in the first target status report with the second target identifiers of the one or more mobile targets included in the second target status report one by one, wherein the matched first target identifiers and second target identifiers jointly identify the same mobile target; A matching report is sent to a second node associated with the first node, wherein the matching report includes matching information between the first target identifier and the second target identifier and motion state information of the same commonly identified mobile target, wherein the second node is associated with the first node through at least one association feature, and the association feature is selected from the vehicle state information of the first target state report or the second target state report of the mobile target.

2. The method of claim 1, wherein the first sensing unit and the second sensing unit are respectively selected from the group consisting of: a video capture device for capturing video and / or images associated with a moving target to generate a target status report; a radar for monitoring the moving target to generate a target status report; A roadside unit is used to receive a target status report from the mobile target.

3. The method according to claim 1 or 2, wherein the matching information includes a first target identifier and a second target identifier of the matched mobile target.

4. The method of claim 3, wherein the matching information further includes a matching degree for use by the second node in determining the motion state of the mobile target.

5. The method as claimed in any one of claims 1-4, wherein the first node is associated with the second node through at least one of the following association features: lane information; area information; driving direction information; traffic restriction information.

6. The method of claim 5, wherein the first target status report and the second target status report further include one or more of the following parameters related to the mobile target: location information; motion feature information, driving direction information, time information, lane information, area information, mobile target-specific feature information, and target classification information; The analyzing of the first target status report and the second target status report comprises: A match between the first target identification and the second target identification of the mobile target is established by matching one or more parameters selected from the first target status report with one or more parameters selected from the second target status report.

7. A first node, comprising: at least one sensing unit configured to: receive a first target status report regarding one or more mobile targets and a second target status report regarding the one or more mobile targets; Compute nodes, configured as: Matching the first target identifiers of the one or more mobile targets included in the first target status report with the second target identifiers of the one or more mobile targets included in the second target status report one by one, wherein the matched first target identifiers and second target identifiers jointly identify the same mobile target; as well as Sending a matching report to a second node associated with the node, the matching report including matching information of the first target identifier and the second target identifier and motion state information of the same mobile target identified together; The second node is associated with the first node through at least one associated feature, and the associated feature is selected from the vehicle status information of the first target status report or the second target status report of the mobile target.

8. The node of claim 7, wherein the at least one sensing unit comprises a first sensing unit for receiving the first target status report and a second sensing unit for receiving the second target status report, wherein the first sensing unit and the second sensing unit are respectively selected from the group consisting of: Video capture equipment for capturing video and images of moving targets; A radar for monitoring the plurality of moving targets; A roadside unit is used to receive a target status report from the mobile target.

9. The node according to claim 7 or 8, wherein the matching information comprises a first target identifier and a second target identifier of the matched mobile target.

10. The node of claim 9, wherein the matching information further comprises a matching degree for use by the second node in calculating the updated state of the mobile target.

11. The node according to claim 7 or 8, wherein the first node is associated with the second node through at least one of the following information: lane information; area information; driving direction information; traffic restriction information.

12. A node as claimed in claim 7 or 8, wherein The first target status report and the second target status report further include one or more of the following parameters related to the mobile target: location information; motion feature information, driving direction information, time information, lane information, area information, mobile target-specific feature information, and target classification information; The analyzing of the first target status report and the second target status report comprises: A match between the first target identification and the second target identification of each mobile target is established by matching one or more parameters selected from the first target status report with one or more parameters selected from the second target status report.

13. A method for identifying a mobile target performed at a first node, comprising: receiving a matching report from a second node associated with the first node, the matching report including matching information of a first target identifier and a second target identifier of the same mobile target and target state information, wherein the first node and the second node are associated with each other through at least one association feature; receiving a target status report for at least one mobile target from a target sensing unit, the target status report including a third identifier of the at least one mobile target and the at least one associated feature; Based on the at least one associated feature, matching the third identifier with the first target identifier or the second target identifier; The first node sends a matching report to a third node associated therewith, wherein the matching report includes: matching information between the first target identifier of the mobile target and the third identifier and the calculated motion state.

14. The method of claim 13, wherein the matching information includes a first target identifier and a second target identifier of the matched mobile target.

15. The method of claim 13 or 14, wherein the at least one associated feature comprises: Lane information; area information; driving direction information; traffic restriction information.

16. The method of claim 15, wherein the matching report and the target status report further include one or more of the following parameters related to the mobile target: location information, motion feature information, driving direction information, time information, lane information, area information, mobile target-specific feature information, and target classification information; The node calculates the motion state of the mobile target based on the matching report and the target state report.

17. The method of claim 16, wherein the matching report further includes a matching degree, and calculating the updated state of the mobile target based on the matching report and the target state report further includes: Based on the matching degree, it is determined whether to accept the target motion state included in the matching report.

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