Risk estimation device, risk estimation method, and risk estimation program

Abstract

The object detection unit (111) acquires observation data from the sensor device (201), detects each object present in the observation range of the sensor device (201) as a sensor-detected object using the observation data, acquires communication partner data from the wireless device (202), and detects each object present in the communication range of the wireless device (202) as a wireless-detected object using the communication partner data. The risk estimation unit (112) estimates an assumed risk assumed due to the presence of surrounding objects based on information on the sensor-detected objects and information on the wireless-detected objects.

Description

[Technical Field]

[0001] The present disclosure relates to a technique for estimating a risk assumed due to the presence of a surrounding object. [Background technology]

[0002] 2. Description of the Related Art Detection systems using sensors such as cameras are used for safety purposes in autonomous vehicles and surveillance systems.

[0003] A conventional method is known that uses images to detect and analyze objects to estimate risk of danger, but this method has difficulty detecting objects that exist outside the camera's observation range. There is also a known method for detecting the number and behavior of targets using low-cost beacons, but this method does not provide more detailed information than that obtained from the beacons.

[0004] Patent Document 1 discloses a technique for setting a risk area based on image information. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2022-187896 Summary of the Invention [Problem to be solved by the invention]

[0006] The present disclosure aims to make it possible to estimate the risk assumed due to the presence of an object outside the observation range of a sensor device. [Means for solving the problem]

[0007] The risk estimation device of the present disclosure comprises: an object detection unit that acquires observation data from a sensor device, detects each object present in an observation range of the sensor device as a sensor-detected object using the observation data, acquires communication partner data from a wireless terminal, and detects each object present in a communication range of the wireless terminal as a wireless-detected object using the communication partner data; a risk estimation unit that estimates an assumed risk due to the presence of a surrounding object based on the information on the sensor-detected object and the information on the wireless-detected object; Equipped with. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to estimate the risk that may be expected due to the presence of an object outside the observation range of a sensor device. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram of a risk detection system 200 according to the first embodiment. [Figure 2] FIG. 1 is a configuration diagram of a risk estimation device 100 according to a first embodiment. [Figure 3] 3 is a flowchart of a risk estimation method according to the first embodiment. [Figure 4] 10 is a flowchart of step S140 in the first embodiment. [Figure 5] FIG. 10 is a diagram showing an example of the situation of Case 1 in the first embodiment. [Figure 6] 10 is a table showing risk estimation (S141, S142) in the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of the situation of Case 2 in the first embodiment. [Figure 8] 10 is a table showing risk estimation (S143, S144) in the first embodiment. [Figure 9] FIG. 10 is a diagram showing an example of the situation of Case 3 in the first embodiment. [Figure 10] 10 is a table showing risk estimation (S145, S146) in the first embodiment. [Figure 11]FIG. 1 is a diagram showing an overview of a risk estimation method according to the first embodiment. [Figure 12] 10 is a flowchart of a risk estimation method according to the second embodiment. [Figure 13] 10 is a flowchart of step S240 in the second embodiment. [Figure 14] FIG. 10 is a diagram showing an outline of a risk estimation method according to the second embodiment. [Figure 15] 11 is a flowchart of a risk estimation method according to the third embodiment. [Figure 16] 11 is a flowchart of step S340 in the third embodiment. [Figure 17] FIG. 11 is a diagram showing an outline of a risk estimation method according to the third embodiment. [Figure 18] FIG. 1 is a hardware configuration diagram of a risk estimation device 100 according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] In the embodiments and drawings, the same or corresponding elements are denoted by the same reference numerals. The description of elements denoted by the same reference numerals as those already described will be omitted or simplified as appropriate. Arrows in the drawings primarily indicate the flow of data or the flow of processing.

[0011] Embodiment 1 The risk estimation device 100 will be described with reference to FIGS.

[0012] ***Configuration Description*** The configuration of a risk detection system 200 will be described with reference to FIG. The risk detection system 200 includes a sensor device 201 , a wireless device 202 , and a risk estimation apparatus 100 .

[0013] For example, the risk detection system 200 is mounted on a mobile object such as an automobile or a robot. Examples of robots include PMVs and AMRs. PMV is an abbreviation for personal mobility vehicle. AMR is an abbreviation for autonomous transport robot.

[0014] For example, the risk detection system 200 is mounted on infrastructure equipment. An example of an infrastructure device is an RSU. RSU is an abbreviation for Roadside Unit.

[0015] The sensor device 201 performs various observations of the surrounding environment, including photography and measurement. Examples of the sensor device 201 include a camera and a LiDAR. LiDAR is an abbreviation for Light Detection And Ranging.

[0016] Data obtained by observation by the sensor device 201 is called observation data.

[0017] The wireless device 202 is a device that communicates wirelessly and is also called a transmitting terminal. An example of a wireless device 202 is a BLE beacon or a Wifi device. BLE is an abbreviation for Bluetooth Low Energy. Bluetooth is a registered trademark. Wifi is an abbreviation for Wireless Fidelity. Wifi is a registered trademark.

[0018] Data indicating the communication partner of the wireless device 202 is referred to as communication partner data.

[0019] The configuration of the risk estimation device 100 will be described with reference to FIG. The risk estimation device 100 is a computer that includes hardware such as a processor 101, a memory 102, an auxiliary storage device 103, and an interface 104. These pieces of hardware are connected to each other via signal lines.

[0020] The processor 101 is an IC that performs arithmetic processing and controls other hardware. For example, the processor 101 is a CPU, a GPU, or a combination thereof. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit. GPU is an abbreviation for Graphics Processing Unit.

[0021] The memory 102 is a volatile or non-volatile storage device. The memory 102 is also called a primary storage device or a main memory. For example, the memory 102 is a RAM. Data stored in the memory 102 is saved in the secondary storage device 103 as needed. RAM is an abbreviation for Random Access Memory.

[0022] The auxiliary storage device 103 is a non-volatile storage device. For example, the auxiliary storage device 103 is a ROM, a HDD, a flash memory, or a combination thereof. Data stored in the auxiliary storage device 103 is loaded into the memory 102 as needed. ROM is an abbreviation for Read Only Memory. HDD is an abbreviation for Hard Disk Drive.

[0023] The interface 104 is a port to which various devices are connected. For example, a sensor device 201 and a wireless device 202 are connected to the interface 104. be.

[0024] The risk estimation device 100 includes elements such as an object detection unit 111, a risk estimation unit 112, and a risk notification unit 113. These elements are realized by software.

[0025] The auxiliary storage device 103 stores a risk estimation program for causing the computer to function as an object detection unit 111, a risk estimation unit 112, and a risk notification unit 113. The risk estimation program is loaded into the memory 102 and executed by the processor 101. The auxiliary storage device 103 also stores an OS. At least a part of the OS is loaded into the memory 102 and executed by the processor 101. The processor 101 executes a risk estimation program while running the OS. OS is an abbreviation for Operating System.

[0026] The data of the risk estimation program (input data, output data, etc.) is stored in the storage unit 120. The memory 102 functions as the storage unit 120. However, a storage device such as the auxiliary storage device 103, a register in the processor 101, or a cache memory in the processor 101 may function as the storage unit 120 instead of or together with the memory 102.

[0027] The risk estimation program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or flash memory.

[0028] ***Explanation of Operation*** The operation procedure of the risk estimation device 100 corresponds to a risk estimation method, and also corresponds to a processing procedure by a risk estimation program.

[0029] The risk estimation method will be described based on FIG. In step S110, the object detection unit 111 acquires observation data from the sensor device 201 and detects a sensor-detected object using the observation data.

[0030] The sensor-detected objects are objects that exist within the observation range (sensor observation range) of the sensor device 201. The sensor observation range is the range of an area that can be observed by the sensor device 201. Examples of sensor-detected objects include automobiles, pedestrians, and bicycles.

[0031] For example, the observation data is image data created by a camera capturing an image of the surroundings. The object detection unit 111 processes the image data and detects objects detected by each sensor. For example, the observation data is three-dimensional point cloud data created by LiDAR measuring the surroundings. The object detection unit 111 processes the three-dimensional point cloud data to detect objects detected by each sensor.

[0032] In step S120, the object detection unit 111 acquires communication partner data from the wireless device 202, and detects a wirelessly detected object using the communication partner data.

[0033] A wirelessly detected object is an object that carries a wireless terminal and exists within the communication range (wireless communication range) of the wireless device 202. A wireless terminal is a terminal that has a function of wireless communication. Examples of wireless terminals are an in-vehicle device and a smartphone. Examples of wireless detection objects include a car equipped with an on-board device, a pedestrian carrying a smartphone, and a bicycle driven by a person carrying a smartphone.

[0034] For example, the communication partner data indicates the identifier (ID) of each wireless terminal that is a communication partner. The object detection unit 111 refers to the communication partner data and identifies each wireless terminal as a wireless detection object.

[0035] In steps S130 and S140, the risk estimation unit 112 estimates the assumed risk based on the information on the sensor-detected object and the information on the wireless-detected object.

[0036] The assumed risk is the risk assumed due to the presence of surrounding objects. For example, the assumed risk is a risk assumed for a mobile object in which the risk detection system 200 is installed. For example, the risk estimation unit 112 estimates the expected risk due to the presence of an object outside the line of sight of the sensor device 201.

[0037] In step S130, the risk estimation unit 112 determines the magnitude relationship between the number of sensor detections and the number of wireless detections.

[0038] The sensor detection number is the number of objects detected by the sensor. The risk estimation unit 112 counts the number of objects detected by the sensor to obtain the sensor detection number. The number of wireless detections is the number of wireless detection objects. The risk estimation unit 112 counts the number of wireless detection objects to obtain the number of wireless detections.

[0039] In step S140, the risk estimation unit 112 estimates an assumed risk depending on the magnitude relationship between the number of sensor detections and the number of wireless detections.

[0040] Step S140 will be described in detail with reference to FIG. If the number of sensor detections and the number of wireless detections match, the process proceeds to step S141. In step S141, the risk estimation unit 112 estimates the assumed risk due to the presence of an object within the sensor observation range. In step S142, the risk estimation unit 112 estimates the assumed risk due to the presence of an object outside the sensor observation range.

[0041] An example of a situation where the number of sensor detections and the number of wireless detections match is shown in Figure 5. The entire area shown in Figure 5 is included in the wireless communication range. The automobile 210 is a moving body on which the risk detection system 200 is installed. The sector-shaped shading represents the sensor observation range. The blackened people are pedestrians carrying wireless terminals and present within the sensor observation range. The shaded people are pedestrians who do not carry wireless devices. The white-painted person is a pedestrian carrying a wireless terminal and located outside the sensor observation range. Within the wireless communication range, there are no objects carrying wireless terminals other than the pedestrian shown in Figure 5.

[0042] There are five pedestrians within the sensor observation range. However, one pedestrian is hidden behind the other pedestrians and is not detected by the sensor device 201. Therefore, the number of sensor detections is "4." There are four pedestrians carrying wireless devices within the wireless communication range, so the number of wireless detections is "4." Therefore, the number of sensor detections "4" and the number of wireless detections "4" match.

[0043] If the number of sensor detections and the number of wireless detections match, the following possibilities are possible: 1. In the sensor observation range, an object without a wireless terminal is hidden behind another object and is not detected by the sensor device 201. 2. An object without a wireless terminal exists within the sensor observation range. The object is detected by the sensor device 201. 3. An object without a wireless terminal exists outside the sensor detection range. 4. An object with a wireless terminal exists outside the sensor detection range. Because of these possibilities, even if the number of sensor detections and the number of wireless detections match, it is not possible for the sensor device 201 to identify all objects, and it is necessary to continue to be vigilant against risks that exist in the surrounding area.

[0044] The assumed risks estimated in steps S141 and S142 will be described with reference to FIG. There is a possibility that an object hidden behind another object may not be detected within the sensor observation range (step S141). Therefore, the risk estimation unit 112 estimates that the expected risk is a "contact accident." In other words, the risk estimation unit 112 estimates that there is a risk of a contact accident. Outside the sensor observation range (step S142), there is a possibility that an object without a wireless terminal will not be detected. Therefore, the risk estimation unit 112 estimates a "peripheral risk" as the assumed risk. In other words, the risk estimation unit 112 estimates that there is a risk in the vicinity of the risk detection system 200.

[0045] Returning to FIG. 4, the description of step S140 continues. If the number of sensor detections is greater than the number of wireless detections, the process proceeds to step S143. In step S143, the risk estimation unit 112 estimates the assumed risk due to the presence of an object within the sensor observation range. In step S144, the risk estimation unit 112 estimates the assumed risk due to the presence of an object outside the sensor observation range.

[0046] FIG. 7 shows an example of a situation where the number of sensor detections is greater than the number of wireless detections. There are five pedestrians within the sensor observation range, so the sensor detection count is "5". There are four pedestrians carrying wireless devices within the wireless communication range, so the number of wireless detections is "4." Therefore, the number of sensor detections "5" is greater than the number of wireless detections "4".

[0047] If the number of sensor detections is greater than the number of wireless detections, the following possibilities are possible: 1. There are objects that do not have wireless terminals within the sensor observation range. Some objects are detected by the sensor device 201. However, it is not possible to deny the existence of objects hidden behind other objects in densely populated areas. 2. An object with a wireless terminal exists outside the sensor observation range. 3. There is an object outside the sensor observation range that does not have a wireless terminal. The assumed scenario is almost the same as when the number of sensor detections and wireless detections match. However, since there are certainly objects without wireless terminals, greater vigilance is required in the surrounding area.

[0048] The assumed risks estimated in steps S143 and S144 will be described with reference to FIG. There is a possibility that an object without a wireless terminal will not be detected within the sensor observation range (step S143). Therefore, the risk estimation unit 112 estimates the "risk of a collision accident" and the "risk of congestion" as the expected risks. In other words, the risk estimation unit 112 estimates that there is a risk of a collision accident and congestion. Outside the sensor observation range (step S144), there is a possibility that an object that cannot be detected by the sensor device 201 (an object outside the line of sight) will not be detected. Therefore, the risk estimation unit 112 estimates a "surrounding risk" as an assumed risk. In other words, the risk estimation unit 112 estimates that there is a risk in the vicinity of the risk detection system 200.

[0049] Returning to FIG. 4, the description of step S140 continues. If the number of sensor detections is less than the number of wireless detections, the process proceeds to step S145. In step S145, the risk estimation unit 112 estimates the assumed risk due to the presence of an object within the sensor observation range. In step S146, the risk estimation unit 112 estimates the assumed risk due to the presence of an object outside the sensor observation range.

[0050] FIG. 9 shows an example of a situation where the number of sensor detections is less than the number of wireless detections. There are four pedestrians within the sensor observation range. However, one pedestrian is hidden behind the other pedestrians and is not detected by the sensor device 201. Therefore, the number of sensor detections is "3." There are five pedestrians carrying wireless devices within the wireless communication range, so the number of wireless detections is "5." Therefore, the number of sensor detections "3" is less than the number of wireless detections "5".

[0051] If the number of sensor detections is less than the number of wireless detections, the following possibilities are possible: 1. There is an object with a wireless terminal outside the sensor observation range. When this object approaches, there is a risk that it may jump out of the field of view. 2. In the sensor observation range, there is an object holding a wireless terminal hidden behind another object in a densely populated area. In this case, there is a risk of congestion in the densely populated area and a risk of the object jumping out of the densely populated area. 3. There is an object outside the sensor observation range that does not have a wireless terminal. The big difference from when the number of sensor detections and the number of wireless detections match is that there are always objects that are not detected by the sensor device 201.

[0052] The assumed risks estimated in steps S145 and S146 will be described with reference to FIG. There is a possibility that an object hidden behind another object may not be detected within the sensor observation range (step S145). Therefore, the risk estimation unit 112 estimates the "risk of a collision accident" and the "risk of congestion" as the expected risks. In other words, the risk estimation unit 112 estimates that there is a risk of a collision accident and congestion. Outside the sensor observation range (step S146), there is a possibility that an out-of-sight object will not be detected. Therefore, the risk estimation unit 112 estimates a "peripheral risk" as the assumed risk. In other words, the risk estimation unit 112 estimates that there is a risk in the vicinity of the risk detection system 200.

[0053] Returning to FIG. 3, step S150 will be described.

[0054] In step S150, the risk notification unit 113 notifies the estimated assumed risk.

[0055] For example, the risk detection system 200 is installed in an automobile. The risk notification unit 113 notifies the driver of the anticipated risk by voice using the car navigation system. The driver operates the accelerator, brake, steering wheel, etc. in accordance with the anticipated risk. For example, the risk detection system 200 is installed in a mobile object equipped with an automatic driving device and capable of autonomous driving. The risk notification unit 113 inputs data indicating the anticipated risk to the automatic driving device. The automatic driving device controls the speed, driving direction, etc. in accordance with the anticipated risk.

[0056] The risk notification unit 113 may notify the magnitude of the assumed risk together with the assumed risk. For example, the risk notification unit 113 determines the magnitude of the assumed risk based on the difference between the number of sensor detections and the number of wireless detections (detection difference). The greater the detection difference, the greater the magnitude of the assumed risk.

[0057] The features of the risk estimation method will be explained based on FIG. The risk estimation device 100 performs detection in the same manner as in the past, using the sensor device 201. The sensor device 201 alone cannot detect an object outside the line of sight. In parallel, the risk estimation device 100 performs communication using the wireless device 202. The wireless device 202 is not affected by obstacles within the detection range, and is therefore capable of detecting objects outside the line of sight. The risk estimation device 100 estimates a risk outside the line of sight based on the difference between the detection situation by the sensor device 201 and the detection situation by the wireless device 202. By utilizing not only the fusion of sensors but also the difference in the detection conditions, it is possible to estimate the risk of an object that cannot be detected by the sensor device 201.

[0058] ***Effects of the First Embodiment*** Sensor devices such as cameras or LiDAR alone cannot recognize objects that exist outside the line of sight due to obstacles such as the shadows of objects, fences, and intersections. This makes it difficult to observe risks that are outside the line of sight. Mobile terminal devices such as smartphones are capable of wireless communication using BLE beacons or Wi-Fi, but it is difficult to obtain detailed information about the target using only mobile terminal devices. The first embodiment has the following objective: The number and positions of objects that exist outside the line of sight of the sensor device 201 are ascertained using the wireless device 202. This widens the detection range, improves detection accuracy, and provides information for risk avoidance.

[0059] The first embodiment has the following features. The risk estimation device 100 measures the number and positions of objects present in the vicinity using the wireless device 202. In this way, the risk estimation device 100 detects objects that are present outside the line of sight of the sensor device 201. The risk estimation device 100 fuses the wireless device 202 with the sensor device 201. The risk estimation device 100 then identifies objects that cannot be detected by the sensor device 201, and estimates the risk of congestion in the surrounding area or the user's own risk of contact. By virtue of these characteristics, the first embodiment provides the following advantages. It is possible to identify objects that exist outside the line of sight and estimate various risks outside the line of sight based on the movement and number of those objects, thereby making it possible to prevent accidents before they occur.

[0060] Embodiment 2 The embodiment for estimating the expected risk associated with the approach of an out-of-sight object will be described below with reference to Figs. 12 to 14, mainly with respect to the points that differ from the first embodiment.

[0061] ***Configuration Description*** The configuration of the risk estimation device 100 is the same as that in the first embodiment.

[0062] ***Explanation of Operation*** The risk estimation method will be described with reference to FIG. In step S210, the object detection unit 111 acquires observation data from the sensor device 201 and detects a sensor-detected object using the observation data. Step S210 corresponds to step S110 in the first embodiment.

[0063] At this time, the object detection unit 111 estimates the position information of each sensor-detected object. For example, the observation data is image data created by a camera capturing an image of the surroundings. The object detection unit 111 processes the image data and calculates the relative position of each object detected by the sensor with respect to the camera. For example, the observation data is three-dimensional point cloud data created by LiDAR measuring the surroundings. The object detection unit 111 processes the three-dimensional point cloud data and calculates the relative position of each sensor-detected object with respect to the LiDAR.

[0064] In step S220, the object detection unit 111 acquires communication partner data from the wireless device 202, and detects a wireless detection object using the communication partner data. Step S220 corresponds to step S120 in the first embodiment.

[0065] At this time, the object detection unit 111 estimates the position information of each wirelessly detected object. For example, if the communication partner has a positioning function, the communication partner data indicates the location of the communication partner. An example of a positioning function is satellite positioning. An example of a satellite positioning system is the Global Positioning System (GPS). For example, the wireless device 202 calculates the direction and distance of a communication partner by DOA estimation using an array antenna. The communication partner data indicates the direction and distance of each communication partner. DOA is an abbreviation for Direction of Arrival. For example, the communication partner data indicates the reception strength of radio waves from each communication partner. The reception strength varies depending on the distance of the communication partner and the presence or absence of an obstruction. The object detection unit 111 refers to the communication partner data and estimates the distance of each communication partner based on the reception strength.

[0066] In step S230, the risk estimation unit 112 determines whether or not there is an out-of-line-of-sight object based on the position information of each sensor-detected object and the position information of each wireless-detected object.

[0067] A non-line-of-sight object is a wirelessly detected object that is not a sensor-detected object. The risk estimation unit 112 finds wirelessly detected objects whose position information does not match any of the sensor-detected objects as out-of-line-of-sight objects.

[0068] If an object outside the line of sight is present, the process proceeds to step S240. In step S240, the risk estimation unit 112 measures movement information of the out-of-line-of-sight object based on the position information of the out-of-line-of-sight object at each time, and estimates an assumed risk due to the presence of the out-of-line-of-sight object based on the movement information of the out-of-line-of-sight object.

[0069] Specifically, the risk estimation unit 112 measures the distance of the out-of-line-of-sight object as movement information of the out-of-line-of-sight object, and estimates the expected risk according to changes in the distance of the out-of-line-of-sight object.

[0070] Step S240 will be described in detail with reference to FIG. In step S241, the risk estimation unit 112 starts tracking an out-of-line-of-sight object.

[0071] Tracking of non-line-of-sight objects is performed, for example, as follows. The object detection unit 111 acquires communication partner data at each time and estimates the position information of out-of-sight objects. The risk estimation unit 112 records the position information of the out-of-sight object at each time.

[0072] In step S242, the risk estimation unit 112 measures the distance of the non-line-of-sight object from the wireless device 202 based on the position information of the non-line-of-sight object.

[0073] In step S243, the risk estimation unit 112 compares the distance of the out-of-line-of-sight object with a proximity threshold, which is a predetermined value.

[0074] If the distance of the out-of-line-of-sight object is equal to or less than the proximity threshold, the process proceeds to step S244. If the distance of the non-line-of-sight object is greater than the proximity threshold, the process proceeds to step S245.

[0075] In step S244, the risk estimation unit 112 estimates a "contact accident" as the assumed risk. After step S244, the process of step S240 ends.

[0076] In step S245, the risk estimation unit 112 compares the distance of the out-of-line-of-sight object with a far-away threshold, which is a predetermined value.

[0077] If the distance of the non-line-of-sight object is less than the far threshold, the process proceeds to step S242. If the distance of the non-line-of-sight object is equal to or greater than the far threshold, the process proceeds to step S246.

[0078] In step S246, the risk estimation unit 112 estimates a "peripheral risk" as an assumed risk. After step S246, the process of step S240 ends.

[0079] Returning to FIG. 12, the explanation will continue. After step S240, the process proceeds to step S260.

[0080] If it is determined in step S230 that no out-of-line-of-sight object exists, the process proceeds to step S250. In step S250, the risk estimation unit 112 estimates a "peripheral risk" as an assumed risk. After step S250, the process proceeds to step S260.

[0081] In step S260, the risk notification unit 113 notifies the assumed risk. Step S260 is the same as step S150 in the first embodiment.

[0082] ***Effects of the Second Embodiment*** The risk estimation device 100 uses the wireless device 202 to identify an object that cannot be detected by the sensor device 201, estimates the movement information (distance and direction) of the object, and estimates the risk associated with the approaching object. According to the second embodiment, it is possible to sense the approach of an object and estimate the risk associated with the approach of the object.

[0083] FIG. 14 shows an example of a situation in which an object (pedestrian, car) approaches the risk detection system 200. According to the second embodiment, for example, it is possible to estimate the risk of a collision accident caused by an object jumping out from behind a fence and the risk of a collision accident involving an object approaching from behind.

[0084] Embodiment 3 The embodiment for estimating the expected risk associated with a change in the traveling direction of an out-of-sight object will be described below with reference to Figs. 15 to 17, mainly with respect to the differences from the first and second embodiments.

[0085] ***Configuration Description*** The configuration of the risk estimation device 100 is the same as that in the first embodiment.

[0086] ***Explanation of Operation*** The risk estimation method will be described with reference to FIG. In step S310, the object detection unit 111 acquires observation data from the sensor device 201 and detects a sensor-detected object using the observation data. At this time, the object detection unit 111 estimates the position information of each sensor-detected object. Step S310 is the same as step S210 in the second embodiment.

[0087] In step S320, the object detection unit 111 acquires communication partner data from the wireless device 202, and detects a wireless detection object using the communication partner data. At this time, the object detection unit 111 estimates the position information of each wirelessly detected object. Step S320 is the same as step S220 in the second embodiment.

[0088] In step S330, the risk estimation unit 112 determines whether or not there is an out-of-line-of-sight object based on the position information of each sensor-detected object and the position information of each wireless-detected object. Step S330 is the same as step S230 in the second embodiment.

[0089] If a non-line-of-sight object is present, the process proceeds to step S340. In step S340, the risk estimation unit 112 measures movement information of the out-of-line-of-sight object based on the position information of the out-of-line-of-sight object at each time, and estimates an assumed risk due to the presence of the out-of-line-of-sight object based on the movement information of the out-of-line-of-sight object.

[0090] Specifically, the risk estimation unit 112 measures the movement path of the out-of-sight object as movement information of the out-of-sight object, and estimates the expected risk according to changes in the movement path of the out-of-sight object.

[0091] Step S340 will be described in detail with reference to FIG. In step S341, the risk estimation unit 112 starts tracking an out-of-line-of-sight object. Step S341 is the same as step S241 in the second embodiment.

[0092] In step S342, the risk estimation unit 112 measures the movement path of the out-of-sight object based on the position information of the out-of-sight object at each time.

[0093] In step S343, the risk estimation unit 112 calculates the amount of change in the travel route. The risk estimation unit 112 then compares the amount of change in the travel route with a threshold value, which is a predetermined value.

[0094] If the amount of change in the movement path is less than the threshold, the process proceeds to step S342. If the amount of change in the movement path is equal to or greater than the threshold, the process proceeds to step S344.

[0095] In step S344, the risk estimation unit 112 determines whether the traveling direction of the out-of-sight object has changed based on the position information of the out-of-sight object at each time.

[0096] Specifically, the risk estimation unit 112 determines whether the traveling direction of the out-of-line-of-sight object has changed from the direction toward the object having the risk detection system 200.

[0097] If the traveling direction of the out-of-sight object has changed, the process proceeds to step S342. If the traveling direction of the out-of-line-of-sight object has not changed, the process proceeds to step S345.

[0098] In step S345, the risk estimation unit 112 estimates a "peripheral risk" as an assumed risk.

[0099] Returning to FIG. 15, the explanation will continue. After step S340, the process proceeds to step S360.

[0100] If it is determined in step S330 that no non-line-of-sight object exists, the process proceeds to step S350. In step S350, the risk estimation unit 112 estimates a "peripheral risk" as an assumed risk. After step S350, the process proceeds to step S360.

[0101] In step S360, the risk notification unit 113 notifies the assumed risk. Step S360 is the same as step S150 in the first embodiment.

[0102] ***Effects of the Third Embodiment*** The risk estimation device 100 tracks out-of-line-of-sight objects detected by the wireless device 202. Each wireless terminal is assigned an ID, making it possible to identify each individual. In addition, since the approximate location can be determined, it is possible to ascertain the object's movement path. The risk estimation device 100 estimates a risk when the movement path of an object changes suddenly. FIG. 17 shows an example of a moving path of an object (pedestrian). The dashed circles represent obstacles. The dotted arrows indicate the path of movement of the pedestrian as he moves toward the automobile 210, avoiding obstacles. The solid arrow indicates the path of travel of the pedestrian proceeding in a direction different from that of the automobile 210. An object may deviate significantly from its direction of travel due to the presence of an obstacle or other reasons. If the object subsequently returns to its original path, it is possible that the object has avoided an obstacle. In this case, the risk estimation device 100 estimates the "surrounding risk." Furthermore, if the object continues moving forward without returning to its original path, it is considered that the object's direction of travel has changed, and in this case, the risk estimation device 100 determines that there is no risk. According to the third embodiment, it is possible to track out-of-sight objects and estimate risks.

[0103] ***Supplementary explanation of implementation form*** The hardware configuration of the risk estimation device 100 will be described with reference to FIG. The risk estimation device 100 comprises a processing circuit 109 . The processing circuit 109 is hardware that realizes an object detection unit 111 , a risk estimation unit 112 , and a risk notification unit 113 . The processing circuitry 109 may be dedicated hardware, or may be a processor 101 that executes a program stored in memory 102 .

[0104] When processing circuitry 109 is dedicated hardware, processing circuitry 109 may be, for example, a single circuit, a multiple circuit, a programmed processor, parallel programmed processors, an ASIC, an FPGA, or a combination thereof. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array.

[0105] The risk estimation device 100 may include multiple processing circuits replacing the processing circuit 109 .

[0106] In the processing circuit 109, some functions may be realized by dedicated hardware, and the remaining functions may be realized by software or firmware.

[0107] Thus, the functions of the risk estimation device 100 can be realized by hardware, software, firmware, or a combination thereof.

[0108] The "part" of each element of the risk estimation device 100 may be read as a "process," a "step," a "circuit," or a "circuitry."

[0109] The first to third embodiments may be implemented in combination with one another. The risk estimation unit 112 estimates the assumed risk using the method in each embodiment. The risk notification unit 113 notifies the assumed risk estimated by the method in each embodiment.

[0110] Each embodiment is an example of a preferred embodiment and is not intended to limit the technical scope of the present disclosure. Each embodiment may be implemented in part or in combination with other embodiments. Procedures described using flowcharts, etc. may be modified as appropriate. [Explanation of symbols]

[0111] 100 Risk estimation device, 101 Processor, 102 Memory, 103 Auxiliary storage device, 104 Interface, 109 Processing circuit, 111 Object detection unit, 112 Risk estimation unit, 113 Risk notification unit, 120 Memory unit, 200 Risk detection system, 201 Sensor device, 202 Wireless device, 210 Automobile.

Claims

1. An object detection unit that acquires observation data from a sensor device, uses the observation data to detect each object within the observation range of the sensor device as a sensor-detectable object, acquires communication partner data from a wireless terminal, and uses the communication partner data to detect each object within the communication range of the wireless terminal as a wireless-detectable object, A risk estimation unit determines the relative magnitude of the number of sensor-detected objects (number of sensor-detected objects) and the number of wireless-detected objects (number of wireless-detected objects) based on the information of the sensor-detected objects and the information of the wireless-detected objects, and estimates the expected risk that can be assumed due to the presence of surrounding objects according to the relative magnitude. A risk estimation device equipped with the following features.

2. The risk estimation device according to claim 1, wherein the risk estimation device estimates the assumed risk due to the presence of an object outside the observation range.

3. The object detection unit estimates the position information of each of the sensor-detected objects and the position information of each of the wirelessly detected objects. The risk estimation unit determines that objects among the wirelessly detected objects that are not sensor-detected objects are out-of-line objects based on the position information of each of the sensor-detected objects and the position information of each of the wirelessly detected objects, measures the movement information of the out-of-line objects based on the position information of the out-of-line objects at each time point, and estimates the assumed risk due to the presence of the out-of-line objects based on the movement information of the out-of-line objects. The risk estimation device according to claim 1 or claim 2.

4. The risk estimation unit measures the distance of the out-of-line object as the movement information of the out-of-line object, and estimates the assumed risk according to the change in the distance of the out-of-line object. The risk estimation device according to claim 3.

5. The risk estimation unit measures the movement path of the out-of-line object as the movement information of the out-of-line object, and estimates the assumed risk according to the change in the movement path. The risk estimation device according to claim 3.

6. Observation data is acquired from the sensor device, and each object within the observation range of the sensor device is detected as a sensor-detectable object using the observation data; communication partner data is acquired from the wireless terminal, and each object within the communication range of the wireless terminal is detected as a wireless-detectable object using the communication partner data; Based on the information of the sensor-detected objects and the information of the wirelessly detected objects, the relative magnitudes of the number of sensor-detected objects (number of sensor-detected objects) and the number of wirelessly detected objects (number of wirelessly detected objects) are determined, and the expected risk that can be anticipated due to the presence of surrounding objects is estimated according to the relative magnitudes. Risk estimation methods.

7. An object detection process that acquires observation data from a sensor device, uses the observation data to detect each object within the observation range of the sensor device as a sensor-detectable object, acquires communication partner data from a wireless terminal, and uses the communication partner data to detect each object within the communication range of the wireless terminal as a wireless-detectable object, A risk estimation process that determines the relative magnitude of the number of sensor-detected objects (number of sensor-detected objects) and the number of wireless-detected objects (number of wireless-detected objects) based on the information of the sensor-detected objects and the information of the wireless-detected objects, and estimates the expected risk that can be assumed due to the presence of surrounding objects according to the relative magnitude; A risk estimation program that is run on a computer.

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