Object identification device, object identification system

Abstract

To provide a technology that reduces the risk of misidentifying a moving object corresponding to the data source from among the objects detected by a sensor. [Solution] The object identification device comprises a communication unit 3 configured to wirelessly communicate with a communication device attached to a moving object, an object detection sensor 2 that detects objects within a predetermined range, and a control unit 1 that acquires the detection speed, which is the moving speed of the detected object. The control unit is configured to use the communication unit to receive a dataset from the communication device containing information related to at least one of the position or speed of a target moving object, which is a moving object linked to the communication device, acquire the target speed, which is the speed of the target moving object, based on the received dataset, and identify the detected object having the detection speed most similar to the target speed as the target moving object.

Description

Technical Field

[0001] The disclosure in this specification relates to a technique for identifying an object whose presence is perceived by wireless communication from among the objects detected by an object detection sensor.

Background Art

[0002] The technique disclosed in Patent Document 1 compares the positioning result by a GPS (Global Positioning System) receiver with the position of the host vehicle identified by roadside equipment through image recognition, and the in-vehicle device identifies the relative position of the GPS receiver on the vehicle body of the host vehicle. Then, the in-vehicle device identifies the position of the host vehicle using the identified relative position of the GPS receiver.

[0003] Also, in a vehicle, there is a technique for detecting an object around the host vehicle using an object detection sensor such as an in-vehicle camera. The detection result of the object can be used for vehicle control or warning the driver.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In object detection sensors such as cameras, multiple objects may be detected. Furthermore, as another technology, efforts are being made to implement technologies that receive data indicating the location of moving objects through direct communication, such as vehicle-to-vehicle and vehicle-to-infrastructure communication. Considering these circumstances, there is a need for technology to identify the object corresponding to the source of the received data (the so-called communication partner) from among multiple objects detected by an object detection sensor. In this case, as disclosed in Patent Document 1, it is conceivable to identify the communication partner from among the objects detected by the camera using the location information of the data source. However, when location information is determined by GPS, the GPS positioning error can exceed 10 meters depending on the surrounding environment. In this case, due to the influence of the GPS positioning error, there is a risk of misidentifying another object near the communication partner as the communication partner.

[0006] One of the purposes of the disclosure is to provide a technology that reduces the risk of misidentifying a moving object corresponding to the data source from among the objects detected by the sensor. [Means for solving the problem]

[0007] The object identification device disclosed herein is A communication unit (3) is configured to be able to communicate wirelessly with a communication device attached to a mobile unit, An object detection sensor (2) that detects objects within a predetermined range, The system includes a control unit (1) that acquires the detected speed, which is the movement speed of the detected object, which is an object detected by the object detection sensor, The control unit is Using the communication unit, a dataset containing information related to at least one of the position or velocity of a target mobile object, which is a mobile object associated with the communication device, is received from the communication device. Based on the received dataset, the target velocity, which is the speed of the target moving object, is obtained. The system is configured to identify the target moving object as the detected object having a detection speed that is most similar to the target speed.

[0008] The object identification system disclosed herein is An object identification system comprising a moving body (500) and an object identification device (10) for identifying an object, The mobile unit has a communication device (50), The communication device transmits a dataset containing information relating to at least one of the position or velocity of a moving object. The object identification device is A communication unit (3) configured to enable wireless communication with a communication device, An object detection sensor (2) that detects objects within a predetermined range, The system includes a control unit (1) that acquires the detected speed, which is the velocity of the detected object, which is an object detected by the object detection sensor, The control unit is Using the communication unit, a dataset containing information related to at least one of the position or velocity of a target mobile object, which is a mobile object associated with the communication device, is received from the communication device. Based on the received dataset, the target velocity, which is the speed of the target moving object, is obtained. The detected object with the detection speed most similar to the target speed is identified as the target moving object.

[0009] The velocity of an object is one of its characteristics and can vary from object to object. According to a configuration in which the control unit identifies the detected object having the detection velocity most similar to the target velocity as the target moving object, it becomes possible to appropriately identify the target moving object that is the communication partner from among the detected objects. Therefore, the disclosed object identification device or object identification system can reduce the risk of misidentifying the moving object corresponding to the data source from among the objects detected by the object detection sensor. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram shows the overall structure of the object identification system. [Figure 2] This flowchart shows an example of processing performed by the control unit. [Figure 3] This diagram shows the overall structure of the object identification system related to the modified example. [Figure 4]It is a flowchart showing an example of processing by a control unit. [Figure 5] It is a flowchart showing an example of processing by a control unit. [Figure 6] It is a flowchart showing an example of processing by a control unit. [Figure 7] It is a flowchart showing an example of processing by a control unit. [Figure 8] It is a flowchart showing an example of processing by a control unit. [Figure 9] It is a flowchart showing an example of processing by a control unit. [Figure 10] It is a flowchart showing an example of processing by a control unit. [Figure 11] It is a flowchart showing an example of processing by a control unit. [Figure 12] It is a flowchart showing an example of processing by a GNSS receiver. [Figure 13] It is a flowchart showing an example of processing by a control unit.

Mode for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. The configurations disclosed below may be variously modified and implemented without departing from the gist. Various modifications may be appropriately combined and implemented within a range where no technical contradiction occurs. In the following description, members having the same function may be given the same reference numerals, and the specific description thereof may be omitted. Also, members having the same function may be given the same or similar names, and the specific description thereof may be omitted. When only a part of the configuration is mentioned, the description given elsewhere may be applied to the other parts.

[0012] Furthermore, if a part of the configuration is described in each embodiment, the other parts of the configuration can be the same as those described in the previously described embodiment. Not only are combinations of the parts specifically described in each embodiment possible, but it is also possible to partially combine embodiments, to the extent that no technical inconsistencies arise.

[0013] <First Embodiment> Embodiments of this disclosure will be described below with reference to the figures. Figure 1 is a diagram showing an example of a schematic configuration of an object identification system Sy including an object identification device 10. The object identification system Sy comprises a roadside unit 100 and a mobile body 500. The object identification device 10 is provided in the roadside unit 100.

[0014] The object identification device 10 is a device that performs a process to identify objects that are located within a predetermined range determined according to the position and orientation of the object identification device 10. In the following, the predetermined range will also be referred to as the detection range.

[0015] The roadside unit 100 is a wireless device used for vehicle-to-infrastructure (V2I) communication. The roadside unit 100 may be placed on the road, near the road, or near an intersection. The roadside unit 100 may be attached to a traffic light or a structure such as a utility pole or streetlamp.

[0016] The roadside unit 100, which includes the object identification device 10, recognizes road users such as vehicles and pedestrians within its detection range and transmits the recognition results to surrounding vehicles. For example, the roadside unit 100 checks for the presence of pedestrians within its detection range and, if necessary, transmits a signal to surrounding vehicles (e.g., buses) to alert drivers.

[0017] The surrounding vehicles referred to here may be vehicles located in a position where roadside unit 100 can communicate with the roadside unit. Furthermore, the vehicles to which messages are notified by roadside unit 100 may be limited to vehicles that are wirelessly connected to roadside unit 100 and are relevant to the warning content (for example, vehicles that meet specific conditions). Vehicles relevant to the warning content may be referred to as warning target vehicles. The description of roadside unit 100 as the entity executing the object detection process may be appropriately replaced with object identification device 10.

[0018] <Mobile> In this embodiment, the mobile body 500 is a vehicle. However, the mobile body 500 may also be a vulnerable road user (VRU), such as a pedestrian, cyclist, or kick scooter user. As shown in Figure 1, the mobile body 500 has a communication device 50 and a GNSS receiver 51. "GNSS" is an abbreviation for Global Navigation Satellite System. The communication device 50 is connected to the GNSS receiver 51. One or more ECUs (Electronic Control Units) may be interposed between the communication device 50 and the GNSS receiver 51.

[0019] The communication device 50 is a wireless communication device attached to the mobile unit 500. The communication device 50 attached to the mobile unit 500 can be understood as a communication device 50 mounted on a vehicle that is the mobile unit 500, a communication device 50 carried by a pedestrian or the like that is the mobile unit 500, or a communication device 50 used in the mobile unit 500.

[0020] In this embodiment, the communication device 50 is an in-vehicle device mounted on a vehicle, which is the mobile body 500. The communication device 50 is linked to the mobile body 500. If the mobile body 500 is a pedestrian or cyclist, the person's portable device (such as a smartphone) corresponds to the communication device 50.

[0021] The communication device 50 has a wireless communication module configured to enable short-range communication, which is direct wireless communication, with the communication unit 3. The communication method between the communication device 50 and the communication unit 3 (i.e., the short-range communication method) may be DSRC (Dedicated Short Range Communications) or Wi-Fi (registered trademark). DSRC may be wireless communication compliant with standards such as IEEE802.11p, ARIB STD-T75, or CEN EN12253. The short-range communication method may also be cellular V2X, for example, communication using a PC5 interface. The communication device 50 may include circuitry compatible with the communication method with the communication unit 3.

[0022] The GNSS receiver 51 is a device that sequentially calculates its current position by receiving navigation signals transmitted from positioning satellites that constitute the GNSS. Navigation signals may be read as positioning signals. GNSS may be GPS, GLONASS, Galileo, or BeiDou, etc. Navigation signals include satellite identification numbers. Satellite identification numbers are information used to distinguish between multiple positioning satellites. Satellite identification numbers are, for example, PRN (Pseudo Random Noise) IDs. The position calculated by the GNSS receiver 51 corresponds to the position of the mobile object 500. The GNSS receiver 51 periodically performs a positioning calculation process, which is the process of calculating the position, and inputs the calculation result (i.e., position data) to the communication device 50. Hereafter, the position calculated by the GNSS receiver 51 will also be referred to as the GNSS position.

[0023] The GNSS receiver 51 includes a RAM (Random Access Memory) which is not shown. The GNSS receiver 51 stores the GNSS position in the RAM. In this disclosure, the term "position" may be interpreted as "position coordinates" as appropriate.

[0024] The GNSS receiver 51 may be configured to calculate the travel speed (hereinafter simply referred to as speed) of the mobile object 500. In this embodiment, the GNSS receiver 51 calculates the speed value and direction of movement (i.e., the speed vector of the mobile object 500) of the mobile object 500 based on the history of position coordinates determined based on the navigation signal and the time interval of positioning.

[0025] The GNSS receiver 51 periodically performs a velocity calculation process, which is the process of calculating the velocity of the mobile object 500, and inputs the calculation result (i.e., velocity data) to the communication device 50. The GNSS receiver 51 corresponds to a first velocity calculation unit that calculates the velocity of the mobile object 500 based on the position of the mobile object 500 that it has positioned. The communication device 50 may also have a function to calculate velocity based on the history of position coordinates calculated by the GNSS receiver 51.

[0026] In the following, the speed calculated based on the position coordinate history calculated by the GNSS receiver 51 will also be referred to as the GNSS-based speed. Alternatively, the GNSS receiver 51 may be configured to determine the speed of the mobile object 500 from the Doppler frequency shift occurring in the carrier frequency of the navigation signal, rather than from the position coordinate history.

[0027] Furthermore, the GNSS receiver 51 may be configured to calculate a precision degradation coefficient, which is an indicator of the accuracy of positioning based on navigation signals. The GNSS receiver 51 sequentially identifies the DOP (Dilution of Precision), which is a positioning precision degradation coefficient influenced by the geometric arrangement of positioning satellites as seen from the navigation signal reception point. A smaller DOP value tends to indicate higher positioning accuracy, so a higher DOP value can be treated as a greater degree of positioning accuracy degradation. The DOP can be identified based on the orbital information contained in the navigation signal received by the GNSS receiver 51. The DOP may be configured to use PDOP (Position DOP), HDOP (Horizontal DOP), or VDOP (Vertical DOP). The GNSS receiver 51 inputs GNSS precision data, which is data indicating the positioning precision degradation coefficient, to the communication device 50.

[0028] In addition, the GNSS receiver 51 may input the satellite usage data, which is a dataset indicating the combination of positioning satellites used in the positioning calculation process, to the communication device 50. The GNSS receiver 51 may also store the satellite usage data and the GNSS position in association. The positioning satellites used in the positioning calculation process will be referred to as "used satellites" below. Used satellites may be represented by satellite identification numbers. In other words, the satellite usage data may be a dataset containing multiple satellite identification numbers corresponding to multiple used satellites.

[0029] The GNSS receiver 51 may be configured to input some or all of the position data, velocity data, GNSS accuracy data, and satellite data used into a single frame to the communication device 50. Alternatively, the position data, velocity data, GNSS accuracy data, and satellite data used may be input to the communication device 50 separately.

[0030] The communication device 50 receives position data, velocity data, GNSS accuracy data, and satellite usage data from the GNSS receiver 51. The communication device 50 periodically wirelessly transmits a state dataset, which is a dataset containing information related to at least one of the position or velocity of the mobile body 500, to the roadside unit 100. In this embodiment, the communication device 50 transmits a dataset as a state dataset that includes the velocity value and direction of movement information of the mobile body 500. The velocity value and direction of movement information can form a velocity vector. The transmission method by the communication device 50 may be any method, such as broadcasting. The periodic transmission interval may be 100 milliseconds or 200 milliseconds, etc.

[0031] The mobile unit 500 may be equipped with various in-vehicle devices in addition to the communication device 50 and GNSS receiver 51, such as an ECU (Electronic Control Unit). One or more ECUs may be connected to the communication device 50.

[0032] <Object identification device> As shown in Figure 1, the object identification device 10 comprises a control unit 1, an object detection sensor 2, a communication unit 3, and a positioning sensor 4. Each of these will be described in turn below.

[0033] The object detection sensor 2 is a sensor for detecting objects within the detection range. In this embodiment, the object detection sensor 2 is a LiDAR. LiDAR is an abbreviation for Light Detection and Ranging or Laser Imaging Detection and Ranging.

[0034] Object detection sensor 2 may also be a millimeter-wave radar. Object detection sensor 2 may also be a UWB radar using impulse waves used in UWB (Ultra Wide Band) communication. Object detection sensor 2 may also be a ToF (Time of Flight) camera or a stereo camera, etc.

[0035] The LiDAR object detection sensor 2 generates three-dimensional observation point cloud data indicating the positions of reflection points for each irradiation direction by irradiating with laser light. Hereinafter, reflection points are also referred to as observation points. The irradiation range of the laser light defines the detection range. The LiDAR object detection sensor 2 is installed on the roadside unit 100, for example, so that the detection range is a predetermined area in front of the roadside unit 100. The LiDAR object detection sensor 2 may be installed at any position on the roadside unit 100.

[0036] The object detection sensor 2, LiDAR, performs clustering on the detected observation point cloud. Clustering is the process of grouping observation points whose distance from each other is less than a predetermined value, treating them as observation points corresponding to the same single object. In the following, objects detected by object detection sensor 2 will be referred to as detected objects. One cluster corresponds to one detected object.

[0037] The object detection sensor 2, a LiDAR, transmits data indicating the detected object to the control unit 1 as detection data. The detection data represents the three-dimensional position of each detected object. In other words, the detection data indicates the position information of each detected object. The position information of a detected object indirectly indicates the distance from the LiDAR to that detected object. The object detection sensor 2, a LiDAR, generates detection data by irradiating laser light at predetermined observation intervals. The observation interval may be 100 milliseconds, 200 milliseconds, or the like.

[0038] The communication unit 3 is a wireless communication module configured to perform narrow-area communication, which is direct wireless communication, with the communication device 50 attached to the mobile unit 500. The communication unit 3 may include a circuit that is compatible with the communication method with the communication device 50. The communication unit 3 acquires the dataset transmitted from the communication device 50. The communication unit 3 transmits the acquired dataset to the control unit 1.

[0039] The positioning sensor 4 is a GNSS receiver that determines its own position, which is the position of the device, based on navigation signals transmitted from multiple positioning satellites. The position of the device corresponds to the location where the roadside unit 100 is installed. The positioning sensor 4 transmits the determined position of the device to the control unit 1. Note that the positioning sensor 4 is an optional element. The object identification device 10 does not necessarily need to be equipped with the positioning sensor 4.

[0040] The control unit 1 is hardware that identifies an object based on data acquired from the object detection sensor 2 and the communication unit 3. The control unit 1 may also be referred to as an edge computer. The control unit 1 has a processor 11, RAM 12, storage 13, and I / O 14. The processor 11 is a processing core that performs calculations based on data received from the object detection sensor 2 or other sensors. The processor 11 may be a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).

[0041] Storage 13 is a rewritable non-volatile memory. Storage 13 may be implemented using at least one type of non-transitory tangible storage medium, such as semiconductor memory, magnetic media, and optical media. Storage 13 may include multiple types of storage media, such as ROM (Read Only Memory) and flash memory. I / O 14 is an input / output circuit.

[0042] <Functions of the control unit> The processor 11 acquires detection data from the object detection sensor 2. The processor 11 may perform a process to identify the type of object corresponding to a cluster based on the cluster features. A feature is a state quantity that represents the characteristics of the cluster point cloud that constitutes a single object. A variety of features can be used to identify the type of detected object.

[0043] The processor 11 performs tracking for each detected object based on the detection data. Tracking involves identifying the correspondence between the current position of a detected object, estimated from past detection data, and the latest detection data, thereby determining the current position of previously detected objects. By performing the tracking process each time detection data is acquired from the object detection sensor 2, it becomes possible to identify the changes in the position of detected objects over time.

[0044] Such tracking includes determining the identity of the most recently detected object with previously detected objects (so-called identification). If the processor 11 determines that the detected object in the current detection data and the detected object in the past detection data are the same object, it links the two. This linking makes each detected object traceable. Hereafter, the tracking result data for each detected object will also be referred to as tracking data. A common detection number or similar may be assigned to identical detected objects, and their location data may be stored.

[0045] The processor 11 obtains the detection velocity, which is the movement speed of the detected object. Based on the tracking data, the processor 11 calculates the detection velocity for each detected object. In this embodiment, the processor 11 calculates the value of the detection velocity and the direction of movement (i.e., the detection velocity vector) based on the amount of change in the position of the detected object and the observation interval. Hereafter, the detection velocity vector will also be referred to as the detection velocity vector. The detection velocity may be rephrased as the detection movement speed, etc. The processor 11 may calculate the detection velocity for each detected object by any other method.

[0046] In the following, the mobile body 500 corresponding to the communication device 50 will also be referred to as the target mobile body. In other words, the target mobile body is the mobile body 50 on which the communication device 50 that communicated using the communication unit 3 is mounted. The mobile body 500 corresponding to the communication device 50 may also be referred to as the mobile body 500 associated with the communication device 50.

[0047] The processor 11 receives a state dataset from the communication device 50, which includes the velocity value and direction of movement, using the communication unit 3. Based on the received state dataset, the processor 11 obtains the target velocity, which is the velocity of the target moving object. As described above, the state dataset in this embodiment includes information on the value and direction of the target velocity, which is the velocity of the target moving object, and the processor 11 obtains the value and direction of the target velocity (i.e., the target velocity vector) based on the received dataset. In the following, "target velocity" refers to the velocity of the target moving object identified based on the received data. The target velocity may also be referred to as the velocity in the received database, the received movement velocity, etc. In the following, the target velocity vector will also be referred to as the target velocity vector.

[0048] The processor 11 identifies the detected object with the detection velocity that is most similar to the target velocity as the target moving object. In this embodiment, the processor 11 uses cosine similarity to calculate the detection velocity vector with the highest similarity to the target velocity vector. Then, it identifies the detected object with the detection velocity vector with the highest similarity as the target moving object. The processor 11 may calculate the similarity between the target velocity and the detection velocity using a method other than cosine similarity.

[0049] The processor 11 may evaluate the degree of similarity between the target speed and the detection speed based only on the value of the target speed and the value of the detection speed. The processor 11 may also evaluate the degree of similarity between the target speed and the detection speed based only on the direction of the target speed and the direction of the detection speed.

[0050] <Processing Flow> Next, the object identification process performed by the processor 11 will be explained using the flowchart shown in Figure 2. The object identification process is the process of identifying the detected object corresponding to the target moving object from among one or more detected objects.

[0051] The object identification process is performed repeatedly from the time the ignition switch is turned ON until it is turned OFF. The object identification process should be executed sequentially at a predetermined cycle (e.g., 100 milliseconds or 200 milliseconds) during which the object detection sensor 2 generates detection data. In the flow description, "S" means step. For example, the notation S11 may be replaced with step 11 or step S11.

[0052] In S11, the processor 11 acquires detection data from the object detection sensor 2. Based on the latest detection data, the processor 11 may perform tracking processing for objects that have already been detected. S11 may include the execution of tracking processing.

[0053] In S12, the processor 11 determines whether it has acquired a dataset from the moving object 500 since the last time it performed object identification processing. If the result in S12 is No, the object identification processing is terminated. If the result in S12 is Yes, the process proceeds to S13.

[0054] In S13, the processor 11 obtains the target velocity based on the received state dataset. For example, the processor 11 obtains the value and direction of the target velocity included in the received state dataset.

[0055] In S14, the processor 11 calculates a detected velocity vector based on the tracking data obtained in S11. In S15, the processor 11 identifies the detected object with the most similar detected velocity vector to the target velocity vector as the object corresponding to the target moving object. Specifically, the processor 11 uses cosine similarity to identify the detected velocity vector with the highest similarity to the target velocity vector. The processor 11 identifies the detected object with the highest similar detected velocity vector as the target moving object.

[0056] <Summary of the First Embodiment> The velocity of an object is one of its characteristics and can vary from object to object. Therefore, according to the configuration in this disclosure, in which the processor 11 identifies the detected object having the detection velocity most similar to the target velocity as the target moving object, the accuracy of identifying the detected object corresponding to the target moving object can be improved. Accordingly, according to this disclosure, the risk of misidentifying the moving object corresponding to the data source can be reduced.

[0057] Furthermore, in general, positioning based on navigation signals can be affected by multipath, which can cause positioning errors. However, the developers of this disclosure have discovered that within a certain range on the ground (for example, within and near a single intersection), positioning errors due to multipath can be considered approximately constant. Moreover, satellite configurations do not change significantly over a few seconds or tens of seconds and can be considered approximately constant. Therefore, within a certain short period (for example, tens of seconds), the satellite configuration is approximately constant, and thus errors due to satellite configurations can also be considered approximately constant.

[0058] Based on these findings, for positioning performed within a short time at a single intersection, the offset error that may occur in the positioning result by the GNSS receiver 51 can be constant. The offset error is the difference between the positioning result and the actual position. If the offset error is constant, the speed of the moving object 500 (i.e., the target speed), calculated by dividing the change in position of the positioning result by the GNSS receiver 51 by the positioning interval, will not be affected by the offset error. Therefore, in localized locations and for positioning performed within a short time, the target speed calculated by the GNSS receiver 51 can cancel out the effect of the offset error, resulting in a more accurate target speed.

[0059] In this disclosure, the GNSS receiver 51 calculates the target velocity based on the history of position determined based on the navigation signal and the time interval of positioning. The processor 11 then identifies the detected object with the detection velocity most similar to the received target velocity as the target moving object. When the object identification device 10 of this disclosure is used in a localized location and within a short time period, it is expected that the target moving object can be identified more appropriately.

[0060] <Example 1> The communication device 50 transmitted a state dataset containing the velocity value and direction of movement information of the mobile object 500, but is not necessarily limited to this configuration. Alternatively or additionally, the communication device 50 may transmit a state dataset containing location information of the mobile object 500. The location information may be expressed in GPS coordinates. For example, the location information may be expressed in latitude and longitude.

[0061] If the state dataset includes the source location information, the processor 11 may calculate the target speed based on the location history of the mobile object 500 included in the received state dataset. Specifically, the processor 11 may calculate the target speed based on the location coordinate history and positioning interval of the target mobile object. Hereafter, the location of the target mobile object will also be referred to as the target position. The target position may be rephrased as the received position coordinates, etc.

[0062] <Modification 2> The mobile unit 500 may also be configured to include a vehicle speed sensor 52 for calculating the speed of the mobile unit 500, in addition to the GNSS receiver 51 (see Figure 3). Hereinafter, the GNSS-based speed will also be referred to as the first speed. The vehicle speed sensor 52 corresponds to a second calculation unit that calculates the speed of the mobile unit 500 in a different way from the first calculation unit. Hereinafter, the speed calculated by the vehicle speed sensor 52 will also be referred to as the second speed. In this embodiment, the vehicle speed sensor 52 may be a so-called wheel speed sensor that detects the speed at which the vehicle's wheels rotate. The vehicle speed sensor 52 periodically inputs second speed data, which is data indicating the second speed, to the communication device 50.

[0063] The vehicle speed sensor 52 may be of a different type. For example, the vehicle speed sensor 52 may be a device that calculates speed by integrating the output of an acceleration sensor. Alternatively, the vehicle speed sensor 52 may include a front camera and calculate the speed of the moving object 500 from the speed of a predetermined stationary object in the image captured by the front camera. The vehicle speed sensor 52 may calculate its own accuracy. The vehicle speed sensor 52 inputs its calculated accuracy as vehicle speed sensor accuracy data to the communication device 50.

[0064] The communication device 50 may transmit a state dataset containing information on the first speed and the second speed. The state dataset may include GNSS accuracy data and vehicle speed sensor accuracy data. The processor 11 may use at least one of the first speed and the second speed to identify the detected object corresponding to the target moving object. For example, the processor 11 may use the average value of the first speed and the second speed to identify the detected object corresponding to the target moving object.

[0065] The processor 11 may select the higher accuracy of the first speed and the second speed based on the GNSS accuracy data and the vehicle speed sensor accuracy data, and identify the detected object corresponding to the target moving object. By using the higher accuracy of the first speed and the second speed to identify the detected object corresponding to the target moving object, the risk of incorrectly identifying the target moving object can be reduced.

[0066] <Variation 3> The processor 11 may perform a warning process if the target moving object is a vehicle subject to warning. The warning process performed by the processor 11 will be explained using the flowchart shown in Figure 4. A warning process is the process of issuing a warning to the target moving object when predetermined warning conditions are met.

[0067] After identifying the target moving object in S15, processor 11 proceeds to S16. In S16, processor 11 determines whether the target moving object is a vehicle subject to warning. Here, as an example, the vehicle subject to warning is a bus. Processor 11 determines whether the target moving object is a bus. In S16, if the answer is No, the process ends. In S16, if the answer is Yes, the process proceeds to S17.

[0068] In S17, the processor 11 predicts the behavior of each detected object, including the target moving object. Based on past detection data and current detection data, the processor 11 predicts the future behavior of each detected object.

[0069] In S18, the processor 11 determines whether a warning is necessary based on the predicted behavior of each detected object. A warning is necessary in situations where a warning to the driver of the target moving object is deemed necessary, such as when there are pedestrians around a bus stop. If the answer in S18 is No, the process ends. If the answer in S18 is Yes, the process proceeds to S19.

[0070] In S19, the processor 11 issues a warning to the target mobile object. The processor 11 uses the communication unit 3 to send a warning signal to the communication device 50. Steps S16 to S19 are also referred to as service-related processing. Upon receiving the warning signal, the communication device 50 issues a warning to the driver of the mobile object 500. According to the configuration of Modified Example 3, the risk of the driver of the vehicle being warned causing a traffic accident can be reduced.

[0071] <Second Embodiment> The processor 11 may use information other than speed to extract candidate objects from the detected objects that are considered to be highly likely to be the target moving object, and then identify the target moving object from among the extracted candidate objects.

[0072] In this embodiment, the state dataset includes information on the target location, the target velocity, and the accuracy degradation coefficient. The processor 11 obtains the target velocity, target location, and accuracy degradation coefficient based on the received state dataset.

[0073] The processor 11 extracts detected objects that are within a candidate distance from the target position as candidate objects, and identifies the candidate object with the detection velocity most similar to the target velocity as the target moving object. The candidate distance is the distance value used to extract the candidate objects.

[0074] The candidate distance may be a fixed value such as 20m or 40m, but the processor 11 in this embodiment dynamically sets the candidate distance. Specifically, the processor 11 obtains the accuracy degradation coefficient included in the received dataset and sets the candidate distance according to the accuracy degradation coefficient. The higher the accuracy degradation coefficient, the larger the candidate distance is set to by the processor 11.

[0075] Next, the object identification process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 5. Step S21 is the same as step S11. Step S22 is the same as step S12.

[0076] In S23, the processor 11 obtains the target position, target velocity, and accuracy degradation coefficient based on the received dataset. For example, the processor 11 obtains the values ​​and direction of the target position and target velocity included in the received dataset. S24 is a similar step to S14.

[0077] In S25, the processor 11 sets candidate distances. The processor 11 sets candidate distances according to the accuracy degradation coefficient. In S26, the processor 11 extracts detected objects that are within the candidate distance from the target position as candidate objects. In S27, the processor 11 identifies the candidate object having the detection velocity vector most similar to the target velocity vector as the target moving object.

[0078] <Summary of the second embodiment> In this embodiment, detected objects located within a candidate distance from the target position are extracted as candidate objects, and the candidate object with the most similar detection speed is identified as the target moving object. Here, it is conceivable that the lower the positioning accuracy, the larger the positional error between the actual target position and the measured target position will be. Therefore, when extracting candidate objects, depending on the setting of the candidate distance, it is conceivable that the target moving object may not be extracted as a candidate object.

[0079] In this embodiment, the higher the accuracy degradation coefficient, the larger the candidate distance is set. This reduces the risk that the target moving object will not be extracted as a candidate object. This improves the likelihood of identifying the correct target moving object from among the detected objects.

[0080] <Variation> The processor 11 sets the candidate distance to a larger value as the accuracy degradation coefficient increases, but this configuration is not necessarily limited to this. As mentioned above, the candidate distance may be a constant value. The processor 11 may also change the candidate distance according to the target speed. The candidate distance may be increased as the target speed increases, because a wider range of possible locations for the moving target increases with higher target speed.

[0081] Furthermore, the accuracy degradation coefficient may be calculated by the processor 11. If the state dataset includes satellite usage data, the processor 11 may calculate an accuracy degradation coefficient such as DOP based on the satellite usage data and use it for the control described above.

[0082] <Third Embodiment> The processor 11 may be configured not to perform specific processing in situations where the reliability of the acquired target speed is considered low. In a configuration where GNSS-based speed is used as the target speed, the GNSS positioning accuracy of the target moving object affects the accuracy of the target speed. Therefore, in a configuration where GNSS-based speed is used as the target speed, the processor 11 may refrain from performing the process to identify the target moving object if the newly acquired combination of positioning satellites does not match the latest combination of positioning satellites stored in RAM 12. This is because a change in the combination of satellites used causes a change in positioning error, which affects the target speed.

[0083] In this embodiment, the state dataset includes information on the target location, information on the combination of satellite identification numbers corresponding to the navigation signals used to determine the target location, and information on the target speed.

[0084] The processor 11 obtains a combination of target location, target speed, and satellite identification number based on the received state dataset. The processor 11 stores the obtained satellite identification number combination in RAM 12. RAM 12 corresponds to the storage unit.

[0085] The processor 11 determines whether the newly acquired combination of positioning satellites matches the latest combination of positioning satellites stored in the RAM 12. Specifically, the processor 11 determines whether the newly acquired combination of satellite identification numbers from the communication device 50 matches the latest combination of satellite identification numbers stored in the RAM 12. If the newly acquired combination of satellite identification numbers matches the latest combination of satellite identification numbers stored in the RAM 12, the processor 11 identifies the detected object corresponding to the target moving object. On the other hand, if the newly acquired combination of satellite identification numbers does not match the latest combination of satellite identification numbers stored in the RAM 12, the processor 11 does not identify the detected object corresponding to the target moving object.

[0086] Next, the object identification process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 6. Step S31 is the same as step S11. Step S32 is the same as step S12.

[0087] In S33, the processor 11 determines whether the combination of satellite identification numbers newly acquired from the communication device 50 matches the latest combination of satellite identification numbers stored in RAM 12. If yes, proceed to S34; otherwise, proceed to S39.

[0088] S34 is the same step as S23. S35 is the same step as S14. S36 is the same step as S25. S37 is the same step as S26. S38 is the same step as S27. In S39, processor 11 stores the acquired satellite identification number combination in RAM 12.

[0089] <Summary of the third embodiment> The value of the positioning error in the positioning result (target position) obtained by the GNSS receiver 51 changes depending on the combination of positioning satellites that transmit the navigation signal used for positioning. In this case, if the GNSS receiver 51 performs positioning multiple times and the combination of transmitting positioning satellites is the same, the positioning error included in the multiple positioning results will be approximately constant. On the other hand, if the combination of transmitting positioning satellites is different, the positioning errors included in the multiple positioning results will be different values.

[0090] Therefore, in a configuration where the target speed is calculated based on the history of the target location, the following concerns arise. That is, if the positioning errors included in the multiple target locations used to calculate the target speed are constant, the target speed calculated by the GNSS receiver 51 will not be affected by the positioning errors. However, if the positioning errors included in the multiple target locations used to calculate the target speed are different, the target speed calculated by the GNSS receiver 51 may be an incorrect value due to the influence of the positioning errors. In other words, if the combination of transmitting positioning satellites is different, the target speed calculated by the GNSS receiver 51 based on the history of the target location may have low reliability.

[0091] This embodiment was created based on the above concept. In this embodiment, assuming that GNSS-based speed is used as the target speed, the processor 11 does not perform identification processing if the newly acquired combination of satellite identification numbers (in other words, the satellites used) does not match the latest combination of satellite identification numbers stored in RAM 12. In other words, the processor 11 does not perform identification processing in situations where the reliability of the acquired target speed is considered low. This reduces the risk of incorrectly identifying the target moving object.

[0092] Furthermore, a decrease in the reliability of the target speed is not limited to cases where the combination of satellites used is changed. Even if the combination of satellites used remains constant, if the positioning results of the roadside unit described later are unstable, the reliability of the positioning results will decrease due to weather conditions and other factors, and consequently, the reliability of the target speed will also decrease. Therefore, if the positioning results of the roadside unit described later are unstable, the identification process may be stopped. In addition, if there is a large fluctuation (variation) in the target speed, there is a possibility that incorrect information is being transmitted due to a malfunction or unauthorized access, so the identification process may also be stopped.

[0093] <Fourth Embodiment> In the second embodiment, the processor 11 extracted candidate objects using the target position and candidate distance. The processor 11 may further narrow down the candidate objects from a different perspective. Hereinafter, the candidate objects extracted by the processor 11 using the target position and candidate distance will also be referred to as first candidate objects.

[0094] In this embodiment, the state dataset includes information on the target position, information on the value and direction of the target velocity, and information on the accuracy degradation coefficient. The processor 11 obtains the target position, the value and direction of the target velocity based on the received dataset.

[0095] After extracting first candidate objects, processor 11 further narrows down the first candidate objects. Processor 11 extracts first candidate objects as second candidate objects whose detected velocity direction falls within a candidate angle relative to the velocity direction of the target moving object. The candidate angle is the angle value used to extract the second candidate object. The candidate angle may also be interpreted as the acceptable angle range.

[0096] The processor 11 sets candidate angles according to the acquired target velocity. The slower the target velocity, the larger the candidate angle value set by the processor 11. From among the second candidate objects, the processor 11 identifies the candidate object with the detection velocity value that is most similar to the target velocity value as the target moving object.

[0097] Next, the object identification process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 7.

[0098] S41 is the same step as S11. S42 is the same step as S12. S43 is the same step as S23. S44 is the same step as S24. S45 is the same step as S25. In S46, the processor 11 extracts a detected object that is within a candidate distance from the target position as the first candidate object.

[0099] In S47, the processor 11 sets candidate angles. The processor 11 sets candidate angles according to the target velocity. In S48, the processor 11 extracts from the first candidate objects a detection object whose detected velocity direction falls within the candidate angle relative to the velocity direction of the target moving object, and identifies it as a second candidate object. In S49, the processor 11 identifies from the second candidate objects the candidate object whose detected velocity value is most similar to the target velocity value, and identifies it as the target moving object.

[0100] <Summary of the fourth embodiment> The developers of this disclosure have discovered that the lower the speed, the greater the error in detecting the direction of movement; in other words, the lower the speed, the easier it is to misdetect the direction of the velocity vector. In this embodiment, the processor 11 increases the candidate angle as the target speed decreases. This reduces the risk of excluding the detected object corresponding to the target moving object from the candidates due to errors in detecting the direction of movement.

[0101] In the fourth embodiment, a first candidate object was extracted, and then a second candidate object was extracted from the first candidate object; however, the configuration is not necessarily limited to this. The process of extracting the first candidate object may be omitted. In other words, steps S45 and S46 may be omitted in the object identification process.

[0102] The processor 11 sets the candidate angle to a larger value as the target speed decreases, but this configuration is not necessarily limited to this. The candidate angle may be a constant value. Also, if the state dataset includes the steering angle of the target moving object, the processor 11 may dynamically determine the candidate angle according to the received steering angle. The larger the received steering angle, the larger the candidate angle may be set to.

[0103] <Fifth Embodiment> The processor 11 may be configured to correct the target position by map matching. In this embodiment, the RAM 12 stores map information of a predetermined range. The predetermined range includes the predetermined area. The state dataset in this embodiment includes information on the target position, information on the target speed, and information on the accuracy degradation coefficient.

[0104] Processor 11 performs map matching. Map matching is the process of correcting the target location to be located on a road in the vicinity of the target location, based on map information. Processor 11 acquires an electronic map based on the map information.

[0105] In the map matching process, the driving trajectory identified from the target locations arranged in chronological order is matched to the roads on the electronic map. Then, the target locations are corrected so that they are located on the roads on the electronic map, and candidate locations for the target locations on the roads are determined. The amount of correction made to the target locations is referred to below as the map matching correction amount. The processor 11 stores the map matching correction amount in the RAM 12.

[0106] The processor 11 determines whether the map matching correction amount is stable. Whether the map matching correction amount is stable is determined from the history of map matching correction amounts stored in RAM 12. For example, if the variation values ​​of the past five map matching correction amounts fall within a predetermined range, the processor determines that the map matching correction amount is stable.

[0107] If the processor 11 determines that the map matching correction amount is stable, it generates correction information for correcting the target position. The processor 11 stores the map matching correction amount as correction information in the RAM 12. In the object identification process, the processor 11 uses the correction information to correct the target position. The processor 11 uses the corrected target position to extract candidate objects.

[0108] Next, the correction information generation process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 8. The correction information generation process is the process of generating correction information. The correction information generation process is performed repeatedly from when the ignition switch is turned ON until it is turned OFF. The correction information generation process should be executed sequentially when the target position is received. The target correction information generation process is performed before the object identification process. Note that the target correction information generation process may be performed in parallel with the object identification process.

[0109] In S51, the processor 11 obtains the target position based on the received state dataset. In S52, the processor 11 performs map matching processing. In S53, the processor 11 stores the map matching correction amount in RAM 12.

[0110] In S54, the processor 11 determines whether the map matching correction amount is stable. If the result in S54 is No, the correction information generation process is terminated. If the result in S54 is Yes, the process proceeds to S55. In S55, the processor 11 causes the map matching correction amount to be saved as correction information in RAM 12.

[0111] Next, the object identification process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 9.

[0112] S61 is the same step as S11. S62 is the same step as S12. S63 is the same step as S23. In S64, the processor 11 corrects the target position based on the correction information stored in RAM 12.

[0113] S65 is the same step as S24. S66 is the same step as S25. In S67, the processor 11 extracts candidate objects using the corrected target position. Specifically, the processor 11 extracts detected objects that are within a candidate distance from the corrected target position as candidate objects. S68 is the same step as S27.

[0114] The process of extracting candidate objects may be omitted. In other words, steps S66 to S67 may be omitted. Also, step S54 may be omitted.

[0115] <Summary of the Fifth Embodiment> The target position determined based on navigation signals may contain positioning errors. According to the configuration of this embodiment, by correcting the determined target position so that it is located on a road in the vicinity of the target position, it is expected that the positioning errors contained in the target position can be reduced. As a result, the target position can become a more accurate location. Therefore, by extracting candidate objects using the corrected target position, more appropriate candidate objects can be extracted. This improves the possibility of identifying the correct moving target object.

[0116] <Sixth Embodiment> In the fifth embodiment, correction information was calculated by performing map matching using the history of the target location, but the configuration is not necessarily limited to this. In this embodiment, the processor 11 calculates correction information based on the position of the device determined by the positioning sensor 4. The state data set of this embodiment includes information on the target location, information on the target speed, and information on the accuracy degradation coefficient.

[0117] In this embodiment, RAM 12 stores the device's position on the map. Since the roadside unit 100 is fixed to the ground, the device's position stored in RAM 12 is considered to represent the correct location.

[0118] The processor 11 calculates the device position error based on the device position determined by the positioning sensor 4 and the device position obtained from the RAM 12. The device position error is the difference between the device position determined by the positioning sensor 4 and the device position obtained from the RAM 12. The device position error may be, for example, the difference between the device position determined by the positioning sensor 4 and the device position obtained from the RAM 12 for each direction.

[0119] After calculating the device position error, the processor 11 determines whether the device position error is stable. Whether the device position error is stable is determined from the history of the device position error stored in RAM 12. For example, the processor 11 determines that the device position error is stable if the variation in the values ​​of the past five device position errors falls within a predetermined range.

[0120] If the processor 11 determines that the position error of its own device is stable, it generates correction information to correct the target position. The processor 11 stores the position error of its own device as correction information in the RAM 12. In the object identification process, the processor 11 corrects the target position using the correction information. The processor 11 extracts candidate objects using the corrected target position.

[0121] Next, the correction information generation process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 10.

[0122] In S71, the processor 11 obtains the position of the device from the distance measuring sensor. In S72, the processor 11 calculates the position error of the device. In S63, the processor 11 stores the map matching correction amount in the RAM 12.

[0123] In S74, the processor 11 determines whether the device's position error is stable. If the result in S74 is No, the correction information generation process is terminated. If the result in S74 is Yes, the process proceeds to S55. In S75, the processor 11 saves the device's position error as correction information to the RAM 12. Note that step S74 may be omitted.

[0124] The object identification process performed by the processor 11 in this embodiment is the same as the object identification process in the fifth embodiment (Figure 9). The process of extracting candidate objects may be omitted. In other words, steps S66 to S67 may be omitted.

[0125] <Summary of the 6th Embodiment> In this embodiment, the processor 11 calculates the device position error based on the device position determined by the positioning sensor 4 and the device position obtained from the RAM 12. Then, the processor 11 corrects the target position based on the calculated device position error.

[0126] For positioning performed within a short time while the distance between the object identification device 10 and the target moving object is within a certain range, the positioning error when the positioning sensor 4 performs positioning based on the navigation signal and the positioning error when the communication device 50 performs positioning based on the navigation signal are approximately the same value. Therefore, the position error of the device itself and the positioning error when the communication device 50 performs positioning based on the navigation signal are approximately the same value. Consequently, it can be expected that the processor 11 can reduce the positioning error included in the target position by correcting the target position based on the calculated position error of the device itself. As a result, the corrected target position becomes a more accurate position. Therefore, by extracting candidate objects using the corrected target position, an appropriate candidate object can be extracted. This improves the possibility of identifying the correct communication partner.

[0127] <Seventh Embodiment> In the sixth embodiment, the processor 11 calculated correction information and corrected the target position, but the configuration is not necessarily limited to this. In this embodiment, the processor 11 calculates the navigation positioning error, which is an error that occurs in positioning based on navigation signals, and transmits the navigation positioning error to the communication device 50. The GNSS receiver 51 corrects the GNSS position based on the navigation positioning error. The RAM 12 in this embodiment stores the position of the device on the map.

[0128] The processor 11 calculates the navigation positioning error based on the device's position determined by the positioning sensor 4 and the device's position obtained from the RAM 12. The navigation positioning error is, for example, the value of the ionospheric delay. Ionospheric delay is the delay in propagation time that occurs when radio waves transmitted from a positioning satellite pass through the ionosphere. The navigation positioning error may also be the roadside unit (RSU) positioning error.

[0129] The processor 11 transmits the calculated navigation positioning error data to the communication device 50 using the communication unit 3. The communication device 50 transmits the received error data, which is the received navigation positioning error data, to the GNSS receiver 51. The received error data is data indicating the navigation positioning error observed by the roadside unit 100.

[0130] The GNSS receiver 51 stores the received error data in RAM. The GNSS receiver 51 corrects the GNSS position based on the navigation positioning error. The GNSS receiver 51 stores the corrected GNSS position in RAM. The GNSS receiver 51 inputs the corrected GNSS position (position data) to the communication device 50.

[0131] The GNSS receiver 51 calculates the speed of the moving object 500 based on the history of GNSS position coordinates. The GNSS receiver 51 inputs the speed data and GNSS accuracy data to the communication device 50. Upon receiving the input, the communication device 50 transmits a status dataset containing information on the corrected target position, information on the speed of the moving object 500, and information on the accuracy degradation coefficient.

[0132] The communication device 50 transmits a state dataset containing information on the corrected target position, target speed, and accuracy degradation coefficient. The processor 11 obtains the corrected target position, target speed, and accuracy degradation coefficient based on the received state dataset. The processor 11 performs object identification processing based on the corrected target position, target speed, and accuracy degradation coefficient.

[0133] Next, the correction information generation process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 11.

[0134] Step S81 is the same as step S71. In S82, the processor 11 calculates the navigation positioning error in the roadside unit 100. In S83, the processor 11 stores the navigation positioning error in RAM 12.

[0135] In S84, the processor 11 determines whether the navigation positioning error is stable or not. If the result in S84 is No, the correction information generation process is terminated. If the result in S84 is Yes, the process proceeds to S85. In S85, the processor 11 transmits the navigation positioning error and the satellite used to the communication device 50 using the communication unit 3. Note that step S84 may be omitted.

[0136] Next, the correction process performed by the GNSS receiver 51 in this embodiment will be explained using the flowchart shown in Figure 12.

[0137] In S91, the GNSS receiver 51 calculates its current position. The GNSS receiver 51 also calculates the accuracy degradation coefficient. In S92, the GNSS receiver 51 determines whether or not it has obtained navigation positioning error data from the communication device 50. Specifically, the GNSS receiver 51 determines whether or not received error data as navigation positioning error is stored in RAM. If the answer in S92 is Yes, the process proceeds to S93; otherwise, the process proceeds to S97.

[0138] In S93, the GNSS receiver 51 corrects the calculated GNSS position based on the reception error data. In S94, the GNSS receiver 51 stores the corrected GNSS position in RAM.

[0139] In S95, the GNSS receiver 51 calculates the speed of the moving object 500 based on the position history of the GNSS position. In S96, the GNSS receiver 51 inputs the corrected GNSS position, target speed, and accuracy degradation coefficient to the communication device 50. Upon receiving the input, the communication device 50 transmits a status dataset containing the corrected target position information, target speed information, and accuracy degradation coefficient information.

[0140] In S97, the GNSS position is stored in RAM. In S98, the GNSS receiver 51 calculates the speed of the moving object 500 based on the position history of the GNSS position. In S99, the GNSS receiver 51 inputs the GNSS position, target speed, and accuracy degradation coefficient to the communication device 50. Upon receiving the input, the communication device 50 transmits a status dataset containing information on the target position, target speed, and accuracy degradation coefficient.

[0141] The object identification process performed by the processor 11 in this embodiment is the same as the object identification process in the fifth embodiment (Figure 9). The process of extracting candidate objects may be omitted. In other words, steps S66 to S67 may be omitted.

[0142] <Summary of Embodiment 7> In this embodiment, the processor 11 calculates the navigation positioning error and transmits it to the communication device 50. The GNSS receiver 51 corrects the GNSS position based on the received error data, which is expected to reduce the positioning error included in the GNSS position. The communication device 50 transmits the corrected GNSS position to the communication unit 3, and the processor 11 extracts candidate objects using the corrected target position, thereby extracting more appropriate candidate objects. This improves the likelihood of identifying the correct communication partner.

[0143] When the GNSS receiver 51 starts correcting the GNSS position using reception error data, it may stop calculating the speed based on the GNSS position history for a certain period of time from the start of the correction. This is because the GNSS position changes significantly before and after the correction using reception error data, which may result in an inaccurate calculated speed value.

[0144] The correction of the GNSS position using the received error data may be performed by the communication device 50 instead of the GNSS receiver 51. The correction of the GNSS position using the received error data may also be performed by the ECU connected to the communication device 50 and the GNSS receiver 51. The functional arrangement in the mobile unit 500 may be changed as appropriate.

[0145] <Eighth Embodiment> In the previously described embodiment, the processor 11 determined the identification result when it identified the detected object as the target moving object at least once, but the configuration is not necessarily limited to this. In this embodiment, the processor 11 determines the identification result when it identifies the same detected object as the target moving object multiple times.

[0146] In this embodiment, the mobile unit 500 is pre-assigned a mobile unit number for identification with other mobile units. The mobile unit number may be an IP address, a vehicle identification number, a MAC address, or another type of identification number. The communication device 50 stores the mobile unit number and the type of mobile unit 500.

[0147] The type of mobile object 500 may be classified into vehicles, pedestrians, bicycles, traffic signs, obstacles, buildings, wild animals, and others. If the mobile object 500 is a vehicle, the detected object may be classified into passenger cars, buses, motorcycles, or trucks. The term "type" may be read as "category." In this embodiment, the type of mobile object 500 is a bus.

[0148] The status dataset in this embodiment includes information on the target location, target speed, accuracy degradation coefficient, mobile object number, and type of mobile object 500. Of course, the status dataset may also include other information, such as transmission time information.

[0149] The processor 11 narrows down the detected objects using the acquired type. The processor 11 extracts the detected objects that match the type as matching objects. If the processor 11 identifies the same detected object as the target moving object multiple times, it confirms the identification result.

[0150] Next, the object identification process performed by the processor 11 in this embodiment will be explained using the flowchart shown in Figure 13.

[0151] S101 is a step similar to S11. S102 is a step similar to S12. In S103, the processor 11 obtains the target position, target speed, accuracy degradation coefficient, mobile number, and type of mobile 500 based on the received dataset.

[0152] S104 is the same step as S14. S105 is the same step as S25. S106 is the same step as S46. In S107, the processor 11 extracts the detected object that matches the type from the first candidate object as a type-matching object.

[0153] Step S108 is the same as step S47. In S109, the processor 11 extracts a second candidate object from among the matching objects whose detected velocity direction falls within a candidate angle relative to the velocity direction of the target moving object. Step S110 is the same as step S49.

[0154] In S111, the processor 11 determines whether the detected object corresponding to the target moving object identified this time is the same as the detected object corresponding to the target moving object identified last time. If yes, proceed to S112; if no, proceed to S116.

[0155] In S112, the processor 11 increments counter N (i.e., adds 1). Counter N is a value used to determine a specific result and is stored in RAM 12. Counter N may be prepared for each data source (i.e., communication partner). In S113, the processor 11 determines whether counter N is greater than a specified value C. If yes, proceed to S114; if no, terminate the object identification process.

[0156] In S114, the identified target mobile object is confirmed to be the target mobile object. In S115, the processor 11 performs service-related processing. The processor 11 includes the mobile object number information in the warning signal during service-related processing and sends a warning signal. In S116, the processor 11 sets counter N to 0. Then, the processor 11 terminates the object identification process.

[0157] The default value C may be set to any value. The process of extracting the first candidate object, the second candidate object, and the object matching the type may be omitted. In other words, steps S105 to S109 may be omitted.

[0158] <Summary of the 8th Embodiment> In the configuration of this embodiment, the processor 11 confirms the identification result when it identifies the same detected object as a target moving object multiple times. This increases the reliability of the identification result.

[0159] Furthermore, in the configuration of this embodiment, detected objects of the same type are extracted as matching type objects before the target moving object is identified. This reduces the risk of mistakenly identifying a detected object of a different type as the target moving object.

[0160] <Other variations / supplements related to the whole> The functions of the communication device 50 may be distributed across multiple devices. Some of the functions of the communication device 50 may be provided by other devices (e.g., an ECU) connected to the communication device 50. For example, the correction of location information in the seventh embodiment may be performed by the ECU. [Explanation of Symbols]

[0161] 1...Control unit, 2...Object detection sensor, 3...Communication unit, 4...Positioning sensor, 10...Object identification device, 11...Processor, 12...RAM (Memory Unit), 13...Storage, 50...Communication device, 51...GNSS receiver, 52...Vehicle speed sensor, 100...Roadside unit, 500...Mobile unit

Claims

1. A communication unit (3) is configured to be able to communicate wirelessly with a communication device attached to a mobile unit, An object detection sensor (2) that detects objects within a predetermined range, The system includes a control unit (1) that acquires the detected speed, which is the movement speed of the detected object, which is an object detected by the object detection sensor, The control unit, Using the communication unit, a dataset is received from the communication device, which includes information relating to at least one of the position or velocity of the target mobile object, which is the mobile object associated with the communication device. Based on the received dataset, the target speed, which is the speed of the target moving object, is obtained. An object identification device configured to identify the detected object having the detection speed most similar to the target speed as the target moving object.

2. The dataset includes information on the target location, which is the position of the target moving object determined using GNSS (Global Navigation Satellite System), and information on the accuracy degradation coefficient, which is an indicator of positioning accuracy. The control unit, Based on the received dataset, the target position and the accuracy degradation coefficient are obtained. The detected objects located within a candidate distance from the target position are extracted as candidate objects, and from among the candidate objects, the candidate object having the detection speed that is most similar to the target speed is identified as the target moving body. The object identification device according to claim 1, wherein the higher the accuracy degradation coefficient, the larger the candidate distance is set to.

3. The dataset includes information on the target position, which is the position of the target moving object determined using GNSS (Global Navigation Satellite System), information on the combination of positioning satellites used for positioning, and information on the target speed. The aforementioned target speed is a parameter calculated based on the history of the aforementioned target position. The control unit, Based on the received dataset, the combination of the target position, the target speed, and the positioning satellite used for positioning is obtained. The acquired combination of positioning satellites is stored in the memory unit. If the combination of positioning satellites newly acquired from the communication device matches the latest combination of positioning satellites stored in the memory unit, the detected object corresponding to the target moving object is identified. The object identification device according to claim 1, wherein if the newly acquired combination of positioning satellites does not match the latest combination of positioning satellites stored in the memory unit, the device is configured not to identify the detected object corresponding to the target moving object.

4. The dataset includes information on the value and direction of the target velocity, The control unit, The value and direction of the detected velocity are obtained from the object detection sensor. Based on the received dataset, the value and direction of the target velocity are obtained. The detection objects having a direction of detection velocity that falls within a candidate angle from the direction of the velocity of the target moving object are extracted as candidate objects, and from among the candidate objects, the candidate object having a detection velocity value that is most similar to the value of the target velocity is identified as the target moving object. The object identification device according to claim 1, wherein the slower the target speed, the larger the candidate angle is set to.

5. The mobile body comprises a first calculation unit that calculates the target speed based on the history of the target position, which is the position of the target mobile body determined using GNSS (Global Navigation Satellite System), and a second calculation unit that calculates the target speed in a manner different from that of the first calculation unit. The dataset includes information on the first speed, which is the target speed calculated by the first calculation unit, and information on the second speed, which is the target speed calculated by the second calculation unit. The control unit, The object identification device according to claim 1, wherein the detection object corresponding to the target moving body is identified using at least one of the first speed and the second speed.

6. The control unit, It has a storage unit (12) that stores map information of the area surrounding the predetermined range, Based on the aforementioned map information, the target location is corrected to be located on a road in the vicinity of the target location. The object identification device according to claim 2, wherein the candidate object is extracted using the corrected target position.

7. The system further includes a positioning sensor (4) that determines its own position, which is the position of the device, based on navigation signals transmitted from multiple positioning satellites constituting the GNSS. The control unit, It has a storage unit that stores the position of the device on a map, The positioning error is calculated based on the position of the device determined by the positioning sensor and the position of the device obtained from the storage unit. The target position is corrected based on the calculated positioning error. The object identification device according to claim 2, wherein the candidate object is extracted using the corrected target position.

8. An object identification system comprising a mobile body (500) and an object identification device (10) for identifying an object, The mobile body has a communication device (50), The communication device transmits a dataset containing information relating to at least one of the position or velocity of the moving object. The object identification device is, A communication unit (3) configured to be able to communicate wirelessly with the aforementioned communication device, An object detection sensor (2) that detects objects within a predetermined range, The system includes a control unit (1) that acquires the detected speed, which is the speed of the detected object detected by the object detection sensor, The control unit, Using the communication unit, a dataset is received from the communication device, which includes information relating to at least one of the position or velocity of the target mobile object, which is the mobile object associated with the communication device. Based on the received dataset, the target speed, which is the speed of the target moving object, is obtained. An object identification system that identifies the detected object having the detection speed most similar to the target speed as the target moving object.

9. The object identification device is, It has a positioning sensor (4) that determines its own position, which is the position of the device, based on multiple navigation signals transmitted from multiple positioning satellites. The control unit, It has a storage unit (12) that stores the position of the device on the map, Based on the position of the device determined by the positioning sensor and the position of the device obtained from the storage unit, the navigation positioning error, which is the error that occurs in positioning using the navigation signal, is calculated. The calculated navigation positioning error is transmitted to the communication device. The mobile unit further includes a GNSS receiver (51), The GNSS receiver is, A positioning calculation process is performed to calculate the position based on the navigation signals transmitted from multiple positioning satellites. The object identification system according to claim 8, wherein the GNSS receiver, the communication device, or other device connected to the communication device is configured to correct the position generated by the positioning calculation process based on the navigation positioning error.

10. The GNSS receiver, the communication device, or the other device calculates the target speed based on the corrected position history. The aforementioned communication device is A dataset including the corrected position, which is the position of the target moving object, and the target velocity is transmitted. The control unit, Based on the received dataset, the target position and the target velocity are obtained. The object identification system according to claim 9, wherein the detected object located within a candidate distance from the target position is extracted as a candidate object, and from among the candidate objects, the candidate object having the detected speed that is most similar to the target speed is identified as the target moving object.

Images