Information processing device and information processing method

Abstract

This disclosure provides an information processing apparatus and an information processing method. It improves the accuracy of road anomaly detection. The apparatus includes a control unit that, in response to receiving first information about the possibility of a road anomaly, determines the road anomaly based on the movements of multiple vehicles within a predetermined range, including the positions corresponding to the first information.

Description

Technical Field

[0001] This disclosure relates to information processing apparatus and information processing methods. Background Technology

[0002] It is known that when a vehicle encounters an abnormal road surface, the abnormal conditions are determined based on the vehicle's behavior, and the state of the road surface is inferred based on the determination result (for example, see Patent Document 1).

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2020-013537 Summary of the Invention

[0006] The purpose of this disclosure is to improve the accuracy of road anomaly detection.

[0007] One aspect of this disclosure is an information processing device comprising a control unit that, in response to receiving first information about the possibility of an anomaly on the road, determines the anomaly on the road based on the actions of multiple vehicles within a predetermined range including the locations corresponding to the first information.

[0008] One aspect of this disclosure is an information processing method in which a computer, in response to receiving first information about the possibility of an anomaly on a road, determines the anomaly based on the behavior of multiple vehicles within a predetermined range, including the location corresponding to the first information.

[0009] Alternatively, other embodiments of this disclosure include a program that enables a computer to execute the above-described information processing method or a storage medium that non-temporarily stores the program.

[0010] According to this disclosure, the accuracy of road anomaly detection can be improved. Attached Figure Description

[0011] Figure 1 This is a diagram showing the general structure of the system involved in the implementation.

[0012] Figure 2 This is a block diagram that schematically illustrates an example of the structure of the vehicle, user terminal, and server that constitute the system involved in the implementation.

[0013] Figure 3 This is a diagram showing an example of a vehicle's trajectory.

[0014] Figure 4 This is a view of the road from above.

[0015] Figure 5 This is a diagram used to illustrate the calculation method for lateral offset.

[0016] Figure 6 It is a graph showing the shift of various data within the scope of analysis.

[0017] Figure 7 It is a graph showing the shift of various data within the scope of analysis.

[0018] Figure 8 It is a graph showing the shift of various data within the scope of analysis.

[0019] Figure 9 It is a graph showing the shift of various data within the scope of analysis.

[0020] Figure 10 It is a graph showing the shift of various data within the scope of analysis.

[0021] Figure 11 It is a graph showing the shift of various data within the scope of analysis.

[0022] Figure 12 It is a diagram showing the trajectory of the same vehicle when it passes by multiple times.

[0023] Figure 13 These are additional diagrams showing the travel trajectory of the same vehicle when it passes by multiple times.

[0024] Figure 14 This is a graph showing data of vehicles that had been driven on PH before the PH was repaired.

[0025] Figure 15 This is a graph showing data for vehicles that were not on the PH before the PH was repaired.

[0026] Figure 16 This is a graph showing the data of vehicles that have been repaired by PH and have been put into service at PH.

[0027] Figure 17 This is a graph showing data for vehicles that were repaired by PH but did not travel on PH.

[0028] Figure 18 It is a summary Figures 14 to 17 The graph is obtained from the sampled data shown.

[0029] Figure 19 It is a diagram showing the distribution of driving lanes.

[0030] Figure 20 This is a diagram showing an example of a PH that exists on a road.

[0031] Figure 21 Viewed from above Figure 20 The diagram shows the road.

[0032] Figure 22 Shown in Figure 20 as well as Figure 21 The example shown contains data about the vehicle that PH had driven onto before PH's repair.

[0033] Figure 23 Shown in Figure 20 as well as Figure 21 The example shown contains data for vehicles that were not on PH before PH was repaired.

[0034] Figure 24 It is a summary Figure 22 as well as Figure 23 The graph is obtained from the sampled data shown.

[0035] Figure 25 Show Figure 3 The analysis range shown includes vehicle data for rainy and sunny days.

[0036] Figure 26 It is shown Figure 3 The diagram shows the vehicle behavior in rainy and sunny weather within the analysis range of the road shown.

[0037] Figure 27 This is a diagram illustrating the functional structure of a server.

[0038] Figure 28 This is a diagram illustrating the table structure of vehicle information.

[0039] Figure 29 This is a diagram showing the functional structure of a vehicle.

[0040] Figure 30 This is a flowchart for determining the possibility of the presence of pH according to the first embodiment.

[0041] Figure 31 This is a flowchart of the process for collecting data corresponding to the PH candidate positions.

[0042] Figure 32 This is a flowchart for determining whether pH exists at the pH candidate position.

[0043] Figure 33 This is a flowchart for determining whether pH exists at the pH candidate position.

[0044] Figure 34 This is a flowchart used to determine whether to monitor the road via the server.

[0045] Figure 35 This is a flowchart of the process for determining whether pH exists, as described in the fifth embodiment.

[0046] Figure 36This is a flowchart of the process for determining whether a pH exists based on the driving lane, as described in the fifth embodiment.

[0047] Figure 37 It is a flowchart of the process for notifying the presence of PH based on data from multiple vehicles.

[0048] (Symbol Explanation)

[0049] 1: System; 10: Vehicle; 20: User terminal; 30: Server; 31: Control unit; 301: Processor; 302: Main storage unit; 303: Auxiliary storage unit; 304: Communication unit. Detailed Implementation

[0050] As one aspect of this disclosure, the information processing apparatus includes a control unit. The control unit responds to first information regarding the possibility of a road anomaly, and determines the road anomaly based on the actions of multiple vehicles within a predetermined range, including the locations corresponding to the first information.

[0051] Examples of road anomalies include road damage, asphalt or concrete peeling, road subsidence, unevenness, and cracks. The first piece of information regarding the possibility of a road anomaly can be information sent from vehicles corresponding to the anomaly, or information received from occupants of vehicles passing by or pedestrians crossing the road indicating a road anomaly. For example, when a vehicle passes through an area with an anomaly, the anomaly might manifest as vibration or a change in wheel speed. If such information is received from a vehicle, the probability of a road anomaly is high. Upon receiving information regarding the possibility of a road anomaly, the control unit determines whether an anomaly actually exists.

[0052] The location corresponding to the first piece of information is, for example, the location where the anomaly occurred, the location where a report of a road anomaly was received, or the location from which information corresponding to the road anomaly was sent from the vehicle. The predetermined range is, for example, the range within which the road anomaly affects the vehicle's movement. For instance, when a driver detects a road anomaly and takes evasive action, the predetermined range may be the range in which at least a portion of the vehicle's movement occurs. For example, the predetermined range may also include a range such as a change in the vehicle's direction of travel to the left or right, or a range such as the vehicle's passing position corresponding to the road anomaly.

[0053] When there are abnormalities on the road, drivers sometimes take evasive action. On the other hand, if a driver does not notice an abnormality, the vehicle may pass through the abnormal section, and based on the abnormality, such as vibration or changes in wheel speed, the vehicle may experience these phenomena. In vehicles that avoid abnormalities, no vibration or changes in wheel speed are observed. Therefore, there is a possibility of misjudging the road abnormality based on the occurrence of vibration or changes in wheel speed.

[0054] In such a road abnormality, there are vehicles that take evasive action and vehicles that pass through the abnormal location without taking evasive action, but there are differences in the actions of each vehicle. Therefore, it is possible to determine whether there is a road abnormality based on these differences in actions.

[0055] Here, when trying to determine road anomalies based on the actions of multiple vehicles for all parts of all roads, the computation becomes voluminous and time-consuming. On the other hand, by obtaining initial information about the possibility of road anomalies through responses and determining whether road anomalies exist, the computational load can be reduced.

[0056] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. The structures of the following embodiments are illustrative, and the present disclosure is not limited to the structures of the embodiments. In addition, the following embodiments can be combined as much as possible.

[0057] <First Implementation>

[0058] Figure 1 This is a diagram showing a schematic structure of system 1 according to this embodiment. Figure 1 In the example, System 1 includes vehicles 10, user terminals 20, and a server 30. System 1 is a system in which the server 30 obtains information related to road anomalies from multiple vehicles 10, and determines the road anomalies based on this information. Furthermore, in Figure 1 The system 1 shown includes, exemplarily, one vehicle 10, but there are multiple vehicles 10.

[0059] Vehicle 10, user terminal 20, and server 30 are interconnected via network N1. Network N1 may be a global public communication network such as the Internet, or it may be a WAN (Wide Area Network) or other communication network. In addition, network N1 may also include telephone communication networks such as mobile phones or wireless communication networks such as Wi-Fi (registered trademark).

[0060] User terminal 20 obtains information related to road anomalies from server 30. User terminal 20 is, for example, a terminal used by a user managing the road. Server 30, for example, sends information about locations identified as having anomalies to user terminal 20. The user who obtains the information from user terminal 20 then performs road repairs, etc.

[0061] Vehicle 10 is a networked vehicle that sends various driving data to server 30 via network N1. Vehicle 10 detects, for example, steering angle, wheel speed, vertical acceleration (which can also be set as vibration), and current position, and sends these data to server 30.

[0062] according to Figure 2 This describes the hardware and functional structure of vehicle 10, user terminal 20, and server 30. Figure 2 This is a block diagram that schematically illustrates an example of the structure of each of the vehicle 10, user terminal 20, and server 30 constituting the system 1 according to this embodiment.

[0063] Server 30 has a computer structure. Server 30 includes a processor 301, a main storage unit 302, an auxiliary storage unit 303, and a communication unit 304. These are interconnected via a bus.

[0064] Processor 301 is a CPU (Central Processing Unit), DSP (Digital Signal Processor), etc. Processor 301 controls server 30 and performs various information processing operations. Main storage unit 302 is RAM (Random Access Memory), ROM (Read Only Memory), etc. Auxiliary storage unit 303 is EPROM (Erasable Programmable ROM), hard disk drive (HDD), removable media, etc. The auxiliary storage unit 303 stores the operating system (OS), various programs, various tables, etc. Processor 301 loads the programs stored in auxiliary storage unit 303 into the operating area of ​​main storage unit 302 and executes them. Through the execution of these programs, it controls various structural units, etc. Thus, server 30 performs functions that conform to the predetermined purpose. Main storage unit 302 and auxiliary storage unit 303 are recording media that can be read by a computer. Furthermore, server 30 can be a single computer or multiple computers working together. Additionally, information stored in auxiliary storage unit 303 can also be stored in main storage unit 302. Conversely, information stored in main storage unit 302 can also be stored in auxiliary storage unit 303. Furthermore, processor 301 is an example of a control unit.

[0065] The communication unit 304 is a unit that communicates with the vehicle 10 and the user terminal 20 via the network N1. The communication unit 304 may be, for example, a LAN (Local Area Network) interface board or a wireless communication circuit for wireless communication. The LAN interface board and the wireless communication circuit are connected to the network N1.

[0066] Furthermore, the series of processes executed by server 30 can be performed either through hardware or through software.

[0067] Next, user terminal 20 will be described. User terminal 20 is, for example, a small computer such as a personal computer (PC), smartphone, mobile phone, tablet terminal, personal information terminal, or wearable computer (smartwatch, etc.). User terminal 20 has a processor 201, main storage unit 202, secondary storage unit 203, input unit 204, display 205, and communication unit 206. These are interconnected via a bus. The processor 201, main storage unit 202, and secondary storage unit 203 are the same as those of the processor 301, main storage unit 302, and secondary storage unit 303 of server 30, so their description is omitted.

[0068] Input unit 204 is a unit that accepts input operations performed by the user, such as a touch panel, mouse, keyboard, or button. Display 205 is a unit that provides prompts to the user, such as an LCD (Liquid Crystal Display) or EL (Electroluminescence) panel. Input unit 204 and display 205 can also be configured as a single touch panel display.

[0069] Communication unit 206 is a communication unit for connecting user terminal 20 to network N1. Communication unit 206 is, for example, a circuit for communicating with other devices (such as server 30, etc.) via network N1 using mobile communication services (such as 5G (5th Generation), 4G (4th Generation), 3G (3rd Generation), LTE (Long Term Evolution) telephone communication networks), Wi-Fi (registered trademark), Bluetooth (registered trademark), and other wireless communication networks.

[0070] Next, vehicle 10 will be described. Vehicle 10 includes a processor 101, a main storage unit 102, an auxiliary storage unit 103, a communication unit 104, a position information sensor 105, a rudder angle sensor 106, a wheel speed sensor 107, and an external camera 108. These are interconnected via a bus. The processor 101, main storage unit 102, auxiliary storage unit 103, and communication unit 104 are the same as those in the user terminal 20, including the processor 201, main storage unit 202, auxiliary storage unit 203, and communication unit 206, so their description is omitted.

[0071] The location information sensor 105 acquires the location information (e.g., latitude and longitude) of the vehicle 10 at predetermined intervals. The location information sensor 105 may be, for example, a GPS (Global Positioning System) receiver or a wireless communication unit. The information acquired by the location information sensor 105 is recorded, for example, in an auxiliary storage unit 103 or transmitted to the server 30.

[0072] The steering angle sensor 106 is a sensor that detects the steering angle obtained through steering operations. For example, the steering wheel angle is detected by the steering angle sensor 106. Furthermore, in this embodiment, the steering wheel angle is detected as the steering angle, but values ​​that directly or indirectly represent the tire's shear angle can also be used. The wheel speed sensor 107 is a sensor that detects the rotational speed of the wheels.

[0073] External camera 108 is a camera positioned facing the exterior of vehicle 10 and capturing images from the front of vehicle 10. External camera 108 may be a camera using an imaging element such as a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal Oxide Semiconductor) image sensor. The images captured by external camera 108 can be either still images or images from animation.

[0074] Here, when vehicle 10 steps on a pothole (hereinafter referred to as "PH") formed on the road, it is detected by wheel speed sensor 107. For example, wheel acceleration can be calculated based on the time change of wheel speed obtained by wheel speed sensor 107. Moreover, wheel acceleration increases when stepping on PH, so if the wheel acceleration increases by a predetermined value or more, it can be considered that PH has been stepped on. However, when the driver of vehicle 10 repeatedly passes through the same place, they sometimes remember the location of PH. When the driver remembers the location of PH, they sometimes drive vehicle 10 without stepping on PH. As a result, it is difficult to detect PH by wheel speed sensor 107. Thus, when more vehicles 10 avoid PH, it may be difficult to determine whether PH actually exists. Therefore, when investigating the behavior of vehicle 10 when the driver knows the location of PH or avoids PH before stepping on PH, and studying whether the presence or absence of PH can be determined based on the behavior of vehicle 10, it was found that the presence or absence of PH can be determined based on the behavior of vehicle 10.

[0075] First, at the location where the pH occurred, the behavior of vehicle 10 before and after the pH location was analyzed. Additionally, to observe changes in vehicle 10 behavior due to the presence or absence of pH, data was compared with data from repairs to the same location where pH occurred.

[0076] In the analysis, the moment when vehicle 10 traveled near PH was determined, and data was extracted for 2 seconds before and after passing PH. If vehicle 10 was traveling at, for example, a speed of 30-50 km / h, the distance at which PH could be identified was assumed to be approximately 30 meters. For example, when traveling at approximately 50 km / h, this distance would be approximately 30 meters before and after PH. Hereinafter, this 30-meter range before and after PH will be referred to as the analysis range.

[0077] For each vehicle 10, the driving position is calculated based on the position information obtained through the position information sensor 105, and the driving trajectory of the vehicle 10 is obtained by arranging these positions in chronological order. Based on the driving trajectory, the driving mode and lateral offset are analyzed. At this time, the road is divided into multiple lanes, and the driving trajectory of the vehicle 10 is analyzed. Figure 3This is a diagram illustrating an example of the driving trajectory of vehicle 10. The analysis assumes the road width is divided into 7 equal parts, with lanes 1 through 7 (#7). Additionally, Figure 4 This is a diagram showing the road as viewed from above. Here, regarding lanes 1 (#1) and 7 (#7) at both ends of the road, vehicles 10 rarely travel in these lanes, so the analysis will focus on lanes other than lanes 1 (#1) and 7 (#7). Figure 3 as well as Figure 4 In the diagram, a single-dot line represents the trajectory of vehicle 10. PH crosses both lane 3 (#3) and lane 4 (#4).

[0078] Additionally, lateral offset is the lateral movement distance of vehicle 10, calculated as described below. Figure 5 This is a diagram illustrating the method for calculating lateral offset. In Figure 5 In the diagram, symbol 10A represents vehicle 10 in the first position, and symbol 10B represents vehicle 10 in the second position. θ1 represents the rudder angle of vehicle 10A in the first position, and θ2 is the rudder angle of vehicle 10B in the second position, representing the rudder angle varying from θ1. The distance that vehicle 10 moves laterally from the first position to the second position (i.e., the lateral offset) can be expressed by the following Equation 1.

[0079] Lateral offset = V1 × ΔT × sin(θ1) … (Equation 1)

[0080] Where V1 is the speed of the vehicle 10A in the first position in the direction of travel (the direction the wheels are facing), and ΔT is the time required for the vehicle 10A to move from the first position to the second position.

[0081] Furthermore, the lateral offset from the second position to the third position after ΔT seconds can be expressed by the following Equation 2.

[0082] Lateral offset = V1×ΔT×sin(θ1) + V2×ΔT×sin(θ1+θ2)…(Equation 2)

[0083] Where V2 is the speed of the vehicle 10B in the second position in the direction of travel, and ΔT is the same value as ΔT in (Equation 1).

[0084] The larger driving trajectory of vehicle 10 is analyzed based on the driving pattern obtained from the position information, and the smaller movements of vehicle 10 are analyzed based on the lateral offset obtained from the rudder angle.

[0085] Figures 6 to 11This is a graph showing the shift of various data within the analysis range. These are data detected by the sensors of vehicle 10 from 2 seconds before vehicle 10 passes the location where PH exists to 2 seconds after vehicle 10 passes the location where PH exists. Vehicle 10 moves in the direction indicated by the arrow in the "direction of travel," and the corresponding data is plotted at each time point. "Vehicle speed" represents the velocity of vehicle 10, and "lateral offset" represents the speed at which vehicle 10 moves. Figure 5 The text describes the lateral offset. "Rudder angle" indicates the angle of the steering wheel, representing the direction and angle of rotation when the steering mechanism is turned to the right or left with 0 as the boundary. "FR wheel acceleration," "FL wheel acceleration," "RR wheel acceleration," and "RL wheel acceleration" represent the rotational acceleration of the right front, left front, right rear, and left rear wheels, respectively. This rotational acceleration increases when the PH pedal is engaged.

[0086] In addition, Figure 6 In the middle, "#2" to "#6" are related to... Figure 4 The lane correspondence is explained in the text. The "right wheel trajectory" corresponds to the trajectory of the right front or right rear wheel, and the "left wheel trajectory" corresponds to the trajectory of the left front or left rear wheel. The wheel trajectories are inferred based on the position information of vehicle 10.

[0087] exist Figure 6 In the example shown, vehicle 10 uses its right front and right rear wheels to engage PH. As vehicle 10 enters the analysis range, the steering mechanism turns left and then right. Furthermore, near PH, the steering mechanism turns left again and then right. Vehicle 10 decelerates within the analysis range. The acceleration of the right rear wheel (RR wheel acceleration) increases near PH, indicating that PH has been engaged.

[0088] exist Figure 7 In the example shown, vehicle 10 uses its right front and right rear wheels to step on PH. Vehicle 10 accelerates within the analysis range while moving veering to the right. The acceleration of the right front wheel (FR wheel acceleration) and the acceleration of the right rear wheel (RR wheel acceleration) increase near PH, indicating that PH has been stepped on.

[0089] exist Figure 8 In the example shown, vehicle 10 uses its left rear wheel to step on PH. After vehicle 10 enters the analysis range and moves in a straight line, the steering mechanism turns to the left before PH, and turns to the right after passing PH. Additionally, vehicle 10 increases its speed. The acceleration of the left rear wheel (RL wheel acceleration) increases near PH, indicating that it has stepped on PH.

[0090] exist Figure 9In the example shown, vehicle 10 avoids PH by swerving to the left. Vehicle 10 decelerates within the analysis range while its steering mechanism turns to the left. Thus, vehicle 10 passes PH without stepping onto it.

[0091] exist Figure 10 In the example shown, vehicle 10 avoids PH by swerving to the right. While traveling at a relatively high speed within the analysis range, vehicle 10 turns its steering mechanism to the right, and after avoiding PH, it turns its steering mechanism to the left. Thus, vehicle 10 passes PH without stepping onto it.

[0092] exist Figure 11 In the example shown, vehicle 10 avoids PH by driving with its right wheel positioned to the right of PH and its left wheel positioned to the left of PH. The vehicle speed remains almost unchanged. At this point, vehicle 10 passes over PH, but its wheels do not touch PH. It is assumed that the driver aimed at PH and drove vehicle 10 in a manner that allowed it to cross PH.

[0093] like Figures 6 to 11 As shown, there are three modes to avoid pH.

[0094] (1) Initially drive on the left, then operate the steering device to the left.

[0095] (2) Initially drive on the right, then operate the steering device to the right.

[0096] (3) While driving in the center of the road, operate the steering device in an S-shaped manner.

[0097] Furthermore, in S-shaped driving, there are cases where the steering device is operated in the order of left, right, left and cases where the steering device is operated in the order of right, left, right.

[0098] On the other hand, vehicles 10 that have stepped onto the PH tend to avoid the PH as described above and proceed in a straight line. In addition, vehicles 10 that have traveled to the same location multiple times tend to avoid stepping onto the PH even if they have stepped onto it for the first time, when passing through the same location again. Figure 12 This diagram shows the travel trajectories of the same vehicle 10 when it passed through the same analysis range multiple times. Vehicle 10 passed through the same analysis range from 1211 to 1215 five times. 1211 is the travel trajectory of vehicle 10 when it first passed through the analysis range. In the initial 1211, vehicle 10 was unable to avoid PH and stepped on PH with its right wheel. 1212 is the travel trajectory of vehicle 10 when it passed through the analysis range for the second time. Even in the second 1212, vehicle 10 was unable to avoid PH and stepped on PH with its right wheel.

[0099] On the other hand, in the third and subsequent instances (1213-1215), vehicle 10 avoids PH by veering to the right within the analysis range. Thus, by repeatedly passing the same location, the driver remembers the position of PH and drives along the PH avoidance route. At this point, if driving further ahead of the analysis range along the avoidance route, the rudder angle may remain almost unchanged within the analysis range. Therefore, it becomes difficult to determine whether the driver knew of PH and passed the route, or did not know of PH and passed the route.

[0100] Figure 13 This is another diagram showing the driving trajectory of the same vehicle 10 passing through multiple times. Vehicle 10 passed through the same analysis range twice, in 1311 and 1312. 1311 is the driving trajectory of vehicle 10 when it first passed through the analysis range. 1312 is the driving trajectory of the same vehicle 10 when it passed through the same analysis range again. In the first instance, 1311, vehicle 10 turned its steering device relatively sharply to avoid PH by passing it between its left and right wheels. On the other hand, in the second instance, 1312, it drove on the right side of the road to avoid PH before entering the analysis range. Thus, when only the data of 1312 is observed, even without a sharp change in the direction of travel, the vehicle does not step on PH, so even the detection value of wheel speed sensor 107 does not have an output corresponding to PH.

[0101] Here, we make the following assumptions.

[0102] (1) Compared to vehicles 10 that have stepped onto the PH, vehicles 10 that have not stepped onto the PH are more likely to perform S-shaped driving. That is, in order to avoid the PH, they perform S-shaped driving, so the amount of steering device operation is greater than that on roads without PH.

[0103] (2) There is a difference in the driving lanes before and after the PH repair. After the PH repair, there are more vehicles driving on the left side of the road.

[0104] The following will verify these hypotheses.

[0105] Figures 14 to 17 This is a graph showing the sampled data. Figure 14 This shows the data for vehicle 10 before it was repaired and before it entered the PH premises. Figure 14In the analysis, "PH" indicates whether the PH (Park) has been engaged; it is displayed as "1" when the PH has been engaged and as "0" when the PH has not been engaged. "Lane" indicates the lane in which vehicle 10 enters the analysis range. "Driving Action" indicates the actions observed in vehicle 10 within the analysis range. In "Driving Action," "L" indicates vehicle 10 veering to the left, "S" indicates vehicle 10 moving in a straight line, "S-shape" indicates vehicle 10 performing an S-shape, and "R" indicates vehicle 10 veering to the right. "Category" indicates the driving lane, driving action, and whether the PH has been engaged. The first number in "Category" corresponds to "Lane." The second number in "Category" corresponds to "Driving Action." The third number in "Category" corresponds to "PH." For example, a row recorded as "2-L-1" in the category records vehicle 10 entering the analysis range from lane 2 and veering to the left, thus engaging the PH. Additionally, in... Figure 14 In this context, "quantity" refers to the number of vehicles 10, and "quantity by lane" refers to the number of vehicles 10 corresponding to each lane.

[0106] in addition, Figure 15 This shows the data for vehicle 10 before PH repair and before it was driven on PH. In this case, the third number (PH) in "Classification" is represented by "0". Figure 16 This shows the data for vehicle 10, which was repaired by PH and then boarded PH. In this case, the third number (PH) in the "Classification" is represented by "1". Figure 17 The data for vehicle 10 that has been repaired by PH but has not been driven on PH is shown. In this case, the third number (PH) in "Classification" is represented by "0". Since there is no PH after PH repair, all vehicles 10 are classified as vehicles 10 that have not been driven on PH.

[0107] Figure 18 It is a summary Figures 14 to 17 The graph is obtained from the sampled data shown. Figure 18 In the middle, "moving to the left" means Figures 14 to 17 Vehicle 10 has a "driving action" of "L". "Straight forward" indicates... Figures 14 to 17 10. Vehicles whose "driving action" is marked with an "S". The "S" indicates... Figures 14 to 17 Vehicle 10, whose "driving action" is an "S" shape. "Moving to the right" indicates... Figures 14 to 17 Vehicle 10 with "Driving Action" set to "R". "Steering Input" indicates information related to the maximum angle when the steering wheel is operated.

[0108] Additionally, "before repair" corresponds to the sampling data before PH repair, and "after repair" corresponds to the sampling data after PH repair. "Passed" indicates that vehicle 10 entered PH, and "not passed" indicates that vehicle 10 did not enter PH. Figure 18 In the analysis, the lanes that enter the analysis range are reduced to lanes 2 to 4 for vehicles 10.

[0109] Before the repair, a large number of vehicles 10 were seen zigzagging in both the "passing" and "non-passing" directions. That is, it can be seen that a large number of vehicles 10 attempted to avoid the PH by zigzagging between vehicles 10 that had entered the PH and those that had not. In addition, when comparing the "steering operation amount" before and after the PH repair, it can be seen that before the repair, the standard deviation and maximum value of the "steering operation amount" were larger, and the steering wheel operation amount was more. Furthermore, regarding vehicles 10 zigzagging, when comparing the "passing" and "non-passing" directions before the repair, it can be seen that the proportion of "non-passing" was higher. Therefore, it can be seen that the above statement "(1) is correct: compared to vehicles 10 that have entered the PH, vehicles 10 that have not entered the PH have a higher tendency to zigzag" is correct.

[0110] in addition, Figure 19 This is a diagram showing the distribution of driving lanes. Solid lines represent the area before PH repair, and dashed lines represent the area after PH repair. In addition, a t-test was conducted to confirm that there were differences between the two parent groups before and after PH repair, which were considered as different groups. When observing each driving lane, before PH repair, the average number of lanes was 3.8 with a standard deviation of 1.33, while after PH repair, the average number of lanes was 3.3 with a standard deviation of 1.03. It can be seen that compared to before PH repair, the driving lanes shifted to the left after PH repair. Therefore, it is believed that before PH repair, as a PH avoidance action, vehicles drove to the right. Therefore, it can be seen that the above statement "(2) There are differences in the driving lanes before and after PH repair, and there are more vehicles driving on the left side of the road after PH repair" is correct.

[0111] Figure 20 This is a diagram showing an example of a PH (property safety feature) existing on a road. (The last part is incomplete and likely refers to a different topic.) Figure 3 Similarly, in the example shown, the road width is divided into 7 equal parts, resulting in 7 lanes: lane 1 (#1) to lane 7 (#7). Figure 20 In the example shown, there are two pH values. Additionally, Figure 21 Viewed from above Figure 20 The map shown. (Compared to...) Figure 4 Similarly, lanes other than lane 1 (#1) and lane 7 (#7) are included in the analysis scope. Figure 21 In the diagram, a single-dot dashed line represents the driving trajectory of vehicle 10. Regarding PH, there are PHs that cross lane 2 (#2) and lane 3 (#3) and PHs that cross lane 5 (#5) and lane 6 (#6).

[0112] Figure 22 Shown in Figure 20 as well as Figure 21 The example shown uses data from vehicle 10 before PH repaired the vehicle and before it entered PH. Additionally, Figure 23 Shown in Figure 20 as well as Figure 21 The example shown contains data for vehicle 10 before PH repair and before it was driven on PH. Regarding the data structure, [the following is relevant]. Figure 14 as well as Figure 15 same. Figure 24 It is a summary Figure 22 as well as Figure 23 The graph is obtained from the sampled data shown. Figure 24 In the text, "the first case before renovation" indicates... Figure 18 The data shown is from before the repair; "Before repair of case 2" indicates the data before the repair. Figure 22 as well as Figure 23 The corresponding data. In Figure 24 Before the second repair, the tendency of vehicle 10 to make an S-shaped maneuver was the same as before the first repair. That is, when comparing the "passing" and "not passing" in the second repair, it can be seen that the vehicle that "did not pass" was more likely to make an S-shaped maneuver.

[0113] In addition, considering that the behavior of vehicle 10 varies with the weather, an analysis is conducted based on data obtained on days with different weather conditions. Figure 25 Show Figure 3 The data for vehicles 10 on rainy and sunny days are shown within the analysis range of the road. "Rain" indicates data collected on rainy days, and "sunny" indicates data collected on sunny days. Vehicles 10 that have entered the PH (classified as "passing") tend to travel more in the center of the road, i.e., in lanes 3 and 4, regardless of whether it is rainy or sunny. On the other hand, vehicles 10 that have not entered the PH (classified as "non-passing") tend to travel more in the left and right directions of the road, i.e., in lanes 2, 5 to 7, regardless of whether it is rainy or sunny.

[0114] Figure 26 It is shown Figure 3 The diagram shows the behavior of vehicle 10 in rainy and sunny weather within the analysis range of the road shown. Vehicle 10 on the PH road tends to move in a straight line in both rainy and sunny weather. Figure 25 When comparing the data shown, vehicles 10 that have stepped onto the PH (presumably a road surface) tend to travel in a straight line near the center of the road. Even in rainy and sunny weather, some vehicles 10 still make S-shaped maneuvers. Furthermore, regardless of whether it's rainy or sunny, the proportion of "non-passing" vehicles 10 making S-shaped maneuvers is higher than that of "passing" vehicles 10.

[0115] As described above, in locations where a pH (problem) is suspected, more vehicles 10 exhibit actions to avoid the pH, and more vehicles 10 change their routes to avoid the pH. Therefore, in locations where a pH is suspected, the presence of a pH can be determined based on the amount of steering wheel operation or the lateral deviation of the vehicle 10 within two seconds before or after the suspected pH location. Additionally, the presence of a pH can also be determined based on the entry route of the vehicle 10 into the analysis range. Furthermore, "locations where a pH is suspected" refers to, for example, locations where the detection values ​​of multiple vehicle 10 wheel speed sensors 107 indicate the presence of a pH. Even in such locations, the wheel speed sensors 107 of vehicles 10 that have avoided the pH cannot detect the pH, so the presence of a pH is not immediately determined; rather, it is determined based on the actions of the vehicle 10.

[0116] Therefore, server 30 extracts locations where a PH (problem) is believed to have occurred based on data obtained from vehicle 10. Furthermore, for the extracted locations, an analysis range is set, and based on whether S-shaped driving or other similar actions occur within that analysis range, it is determined whether a PH has actually occurred. Additionally, S-shaped driving is an example of the first action.

[0117] Figure 27 This is a diagram illustrating the functional structure of server 30. Server 30 includes a control unit 31, vehicle information DB 321, and map information DB 322 as functional components. The processor 301 of server 30 executes the processing of control unit 31 via a computer program stored in main storage unit 302. However, any functional component or part of its processing can also be executed via hardware circuitry. Control unit 31 includes an anomaly extraction unit 311, an anomaly determination unit 312, and a notification unit 313.

[0118] Vehicle information DB321 and map information DB322 are constructed using data stored in auxiliary storage unit 303 through a database management system (DBMS) program executed by processor 301, such as a relational database. Furthermore, any functional component of server 30, or a portion thereof, can be executed by other computers connected to network N1.

[0119] Vehicle information DB321 is formed by storing information related to date and time, location, wheel speed, vehicle speed, and rudder angle in the auxiliary storage unit 303. Here, according to... Figure 28 This describes the structure of the vehicle information stored in the vehicle information DB321. Figure 28This is a diagram illustrating the table structure of vehicle information. A vehicle information table is formed for each vehicle 10. The vehicle information table has fields for date and time, location, wheel speed, vehicle speed, and steering angle. In the date and time field, information related to the date and time when data is obtained from vehicle 10 is entered. In the location field, information related to the location detected by the location information sensor 105 is entered. The location is represented, for example, by coordinates. In the wheel speed field, information related to the wheel speed detected by the wheel speed sensor 107 is entered. In the vehicle speed field, information related to the speed of vehicle 10 is entered. The speed of vehicle 10 can be calculated either from the detection value of the wheel speed sensor 107 and the pre-registered outer diameter of the tires of vehicle 10, or it can be obtained from the detection value of the speed sensor installed on vehicle 10. In the steering angle field, information related to the steering angle detected by the steering angle sensor 106 is entered. This data is sent from each vehicle 10 at predetermined intervals.

[0120] Map information DB322 stores map data, text representing the characteristics of various locations on the map data, photographs, and other map information including POI (Point of Interest) information. Furthermore, map information DB322 can also be provided from other systems connected to network N1, such as GIS (Geographic Information System).

[0121] The anomaly extraction unit 311 extracts locations where a potential phantom hazard (PH) is likely to exist based on data stored in the vehicle information DB321. Here, the vehicle information DB321 stores, for example, location information, wheel speed sensor 107 detection values, and date and time associated with each vehicle 10. Based on this information, the anomaly extraction unit 311 extracts locations where a PH is likely to exist. The anomaly extraction unit 311 calculates the wheel rotational acceleration based on the wheel speed sensor 107 detection values. Furthermore, it extracts locations where the wheel rotational acceleration is greater than or equal to a predetermined acceleration. Moreover, for example, in a predetermined number or more vehicles 10, locations where the wheel rotational acceleration is greater than or equal to a predetermined acceleration are extracted as locations where a PH is likely to exist.

[0122] Furthermore, in this embodiment, the anomaly extraction unit 311 extracts locations where a potential pH (prone to toxic phlegm) is present based on the detection value of the wheel speed sensor 107. However, it is not limited to this; it can also extract locations where a potential pH is present based on the detection values ​​of other sensors that output signals corresponding to pH. Alternatively, for example, locations reported by the driver of the vehicle 10 or pedestrians on the road indicating a potential pH can be extracted as locations where a potential pH is present. In this case, information related to the reported locations is sent from the user terminal 20 to the server 30. Alternatively, locations where a potential pH is present can also be extracted by analyzing images captured by the external camera 108.

[0123] The anomaly determination unit 312 determines whether a potential pH (PH) actually exists at a location extracted by the anomaly extraction unit 311 that has a possibility of being present (hereinafter also referred to as a PH candidate location). The anomaly determination unit 312 sets an analysis range for the PH candidate location. For example, based on the speed of vehicle 10, the analysis range is set to the distance within 2 seconds before and after the PH candidate location. Furthermore, the anomaly determination unit 312 divides the analysis range into, for example, 7 lanes. In this embodiment, the road is divided into 7 lanes for analysis, but the number of divisions is not limited to this. For example, the number of divisions can be increased based on the width of the road. Alternatively, the number of divisions can be determined based on the average speed of vehicle 10.

[0124] The anomaly determination unit 312 obtains the entry lane of the vehicle 10 that has passed through the analysis range from the vehicle information DB321. Then, the anomaly determination unit 312 determines whether the vehicle 10 that passed through the analysis range performed an S-shaped maneuver. For example, the anomaly determination unit 312 may determine that the vehicle 10 performed an S-shaped maneuver if its lateral deviation is within a predetermined amount (e.g., 30cm) to the left or right. Alternatively, instead of the lateral deviation, it may determine that the vehicle performed an S-shaped maneuver if the standard deviation of the steering wheel operation is above a predetermined value, the maximum value of the steering wheel operation is above a predetermined value, or the variance of the steering wheel operation is above a predetermined value. For example, in... Figure 18 The text explains that, in the presence of pH, the standard deviation and maximum value of steering wheel input increase, thus enabling judgments based on these values.

[0125] Furthermore, the anomaly determination unit 312 determines whether a PH (Potentially Hazardous Area) actually exists by comparing the detection values ​​of the wheel speed sensor 107 at the PH candidate position with those of vehicles 10 that obtained the corresponding detection value when stepping on the PH and those that did not obtain the corresponding detection value when stepping on the PH. Specifically, the anomaly determination unit 312 determines that a PH exists if the proportion of vehicles 10 that did not step on the PH and performed an S-shaped maneuver is greater than the proportion of vehicles 10 that did step on the PH and performed an S-shaped maneuver. That is, if a PH exists... Figure 18 If the value enclosed by the dashed line shows a tendency, it is determined that PH exists.

[0126] Furthermore, if the anomaly determination unit 312 determines that a pH has occurred, the notification unit 313 outputs information indicating that a pH has occurred to the user terminal 20. This information includes information about the location where the pH occurred (e.g., latitude and longitude). Additionally, the location where the pH occurred can also be determined based on data stored in the map information DB322.

[0127] Next, the functions of vehicle 10 will be explained. Figure 29 This is a diagram illustrating the functional structure of vehicle 10. Vehicle 10 includes a data transmission unit 11 as a functional component. The processor 101 of vehicle 10 executes the processing of the data transmission unit 11 via a computer program on the main storage unit 102. However, any functional component or part of its processing can also be executed via hardware circuitry.

[0128] The data transmission unit 11 acquires data from the position information sensor 105, rudder angle sensor 106, wheel speed sensor 107, and external camera 108 at predetermined intervals and sends it to the server 30.

[0129] Next, the PH determination process in server 30 will be explained. When server 30 receives data from vehicle 10, it determines the possibility of the presence of a PH. Figure 30 This is a flowchart illustrating the process for determining the likelihood of the presence of a pH, as described in this embodiment. In server 30, this is executed at predetermined intervals. Figure 30 The processing is shown.

[0130] In step S101, the anomaly extraction unit 311 determines whether vehicle information has been received from vehicle 10. Vehicle information is stored in the vehicle information DB321. If the determination is positive in step S101, the process proceeds to step S102; otherwise, the current routine ends. In step S102, the anomaly extraction unit 311 stores the vehicle information in the vehicle information DB321. Next, in step S103, the anomaly extraction unit 311 calculates the wheel acceleration. For example, it calculates the wheel acceleration based on the wheel speed of the previous routine, the wheel speed of the current routine, and the execution interval of the routine. At this time, the wheel acceleration for each of the four wheels is calculated.

[0131] In step S104, the anomaly extraction unit 311 determines whether the wheel acceleration is above a predetermined acceleration. The predetermined acceleration referred to here is the lower limit of the wheel acceleration when the pedal is pressed (PH). In step S104, a positive determination is made if the wheel acceleration of even just one of the four wheels is above the predetermined acceleration. If a positive determination is made in step S104, the process proceeds to step S106; otherwise, the process ends.

[0132] In step S105, the anomaly extraction unit 311 stores the position of vehicle 10. In step S106, if the same position is stored, the anomaly extraction unit 311 determines whether the number of stored positions is greater than or equal to a predetermined number. This predetermined number is set to a value indicating a high probability of a potential PH (Potential Hazard). That is, if there are more than a predetermined number of vehicles 10 at the same position whose wheel acceleration is greater than or equal to a predetermined acceleration, it is considered that the probability of a PH at that position is high. If the determination is affirmative in step S106, the process proceeds to step S107; otherwise, the process ends. In step S107, the anomaly extraction unit 311 registers the position stored in step S105 as a potential PH position. Thus, the anomaly extraction unit 311 extracts a potential PH position.

[0133] Next, we will explain the processing of data collected at locations registered as PH candidate sites. Figure 31 This is a flowchart illustrating the process of collecting data corresponding to the PH candidate positions. In server 30, in... Figure 30 After the example shown, execute Figure 31 The processing is shown.

[0134] In step S201, the anomaly determination unit 312 determines whether the position of vehicle 10 is a candidate PH position. The anomaly determination unit 312 compares the registered candidate PH position with the position sent from vehicle 10. If the determination is positive in step S201, the process proceeds to step S202; otherwise, the process ends. In step S202, the anomaly determination unit 312 determines whether the wheel acceleration is above a predetermined acceleration. The same processing as in step S104 is performed. If the determination is positive in step S202, the process proceeds to step S203; otherwise, the process proceeds to step S204.

[0135] In step S203, the anomaly determination unit 312 causes the auxiliary storage unit 303 to store information indicating that the vehicle 10 has passed the PH (stepped on the PH). On the other hand, in step S204, the anomaly determination unit 312 causes the auxiliary storage unit 303 to store information indicating that the vehicle 10 has not passed the PH (did not step on the PH).

[0136] In step S205, the anomaly determination unit 312 determines whether the left and right offset of the vehicle 10 is greater than or equal to a predetermined amount. In this step S205, it is determined whether the vehicle 10 has performed an S-shaped maneuver. The left and right lateral offset is the distance offset to the right and left respectively. The predetermined amount referred to here is the offset amount when performing an S-shaped maneuver to avoid PH, for example, 30cm. That is, when the travel direction changes in the order of right, left, right, if the vehicle offsets to the right by, for example, 30cm and then to the left by, for example, 30cm, it is determined that the left and right lateral offset is greater than or equal to a predetermined amount. Even when the travel direction changes in the order of left, right, left, similarly, if the vehicle offsets to the left by, for example, 30cm and then to the left by, for example, 30cm, it is determined that the left and right lateral offset is greater than or equal to a predetermined amount. If the determination is affirmative in step S205, the process proceeds to step S206; if the determination is negative, the process proceeds to step S207.

[0137] In step S206, the anomaly determination unit 312 causes the auxiliary storage unit 303 to store information indicating that the vehicle 10 has performed an S-shaped maneuver. On the other hand, in step S207, the anomaly determination unit 312 causes the auxiliary storage unit 303 to store information indicating that the vehicle 10 has not performed an S-shaped maneuver. In this way, information related to wheel acceleration and lateral offset is accumulated regarding the vehicle 10 passing near the PH candidate position.

[0138] Next, we will explain the process for determining whether pH exists at the pH candidate position. Figure 32 This is a flowchart for determining whether pH exists at the pH candidate position. In server 30, this is executed at predetermined intervals. Figure 32 The processing is shown.

[0139] In step S301, the anomaly determination unit 312 determines whether the number of data obtained corresponding to the PH candidate position is greater than or equal to a predetermined number. That is, it determines whether the number of vehicles 10 that have passed the PH candidate position has reached a sufficient number to determine the presence of PH. The predetermined number referred to here is the number when the presence of PH is determined, and it is pre-stored in the auxiliary storage unit 303. The more predetermined numbers there are, the higher the determination accuracy can be, but it takes time to collect data, so the predetermined number is determined based on the priority between determination accuracy and the time required to collect data. If the determination is affirmative in step S301, the process proceeds to step S302; if the determination is negative, the routine ends.

[0140] In step S302, the anomaly determination unit 312 determines whether a PH (problem) exists. That is, it compares the proportion of vehicles 10 that were stored as having passed a PH in step S203 and were stored as having performed an S-shaped descent in step S206 (hereinafter referred to as "S-shaped-1 vehicles") with the proportion of vehicles 10 that were stored as not having passed a PH in step S204 and were stored as having performed an S-shaped descent in step S206 (hereinafter referred to as "S-shaped-0 vehicles").

[0141] Vehicle 10, designated as "S-shaped -1," is considered as a vehicle 10 where the driver noticed the pedestrian hazard (PH) and took evasive action but still stepped onto the PH. Conversely, vehicle 10, designated as "S-shaped -0," is considered as a vehicle 10 where the driver noticed the PH and took evasive action but did not step onto the PH. As observed by... Figure 18 As can be seen from the area enclosed by the dotted line, the proportion of "S-shaped-1" vehicles among the vehicles 10 that did not pass through PH is higher than the proportion of "S-shaped-0" vehicles among the vehicles 10 that did not pass through PH. Thus, even among the vehicles 10 that made S-shaped movements, there is a clear difference between those that entered PH and those that did not.

[0142] Therefore, if such a trend is observed in the PH candidate positions, it is determined that PH exists. In step S302, the anomaly determination unit 312 determines whether the proportion of "S-shaped-0" vehicles among the vehicles 10 that did not pass PH is higher than the proportion of "S-shaped-1" vehicles among the vehicles 10 that have passed PH. If the determination is affirmative in step S302, the process proceeds to step S303; if the determination is negative, the process proceeds to step S306.

[0143] In step S303, the anomaly determination unit 312 determines that a pH exists at a candidate pH location. Since the anomaly determination unit 312 determines that a pH exists at a candidate pH location, in step S304, the notification unit 313 generates notification information to notify the user terminal 20 that a pH has occurred. This notification information includes information related to the location where the pH occurred. Furthermore, in step S305, the notification unit 313 sends the notification information to the user terminal 20. Upon receiving the notification information, the user terminal 20, for example, displays the location of the pH on the display 205. At this time, for example, the location of the pH may also be displayed on a map shown on the display 205.

[0144] On the other hand, in step S306, the anomaly determination unit 312 determines that there is no pH at the pH candidate position. In this case, the user terminal 20 may also be notified that there is no pH. In step S307, the anomaly determination unit 312 resets the data related to the corresponding pH candidate position.

[0145] As explained above, according to the first embodiment, the candidate position for pH is determined based on the detection value obtained from the wheel speed sensor 107 of the vehicle 10. However, relying solely on the detection value of the wheel speed sensor 107, for example, even if the vehicle has stepped on a manhole cover, the detection value may be the same as if it had stepped on a pH, thus there is a possibility of misjudgment. In contrast, the anomaly determination unit 312 determines whether pH exists based on whether an S-shaped drive was performed at the candidate position for pH. Even among vehicles 10 that have performed S-shaped drives, there is a clear difference in proportion between vehicles 10 that have stepped on a pH and those that have not, for example, showing a different tendency than when stepping on a manhole cover. Determining whether pH exists based on this tendency allows for a more accurate determination of whether pH exists compared to simply determining it based on whether an S-shaped drive was performed or simply based on the detection value of the wheel speed sensor 107.

[0146] <Second Implementation Method>

[0147] In the second embodiment, the presence or absence of a potential PH (problem location) is determined based on the lane in which the vehicle 10 is traveling. Other structural aspects are the same as in the first embodiment, so descriptions are omitted. The anomaly detection unit 312 of the server 30 compares past data with current data and determines that a PH exists at a changing location on the vehicle 10's travel lane. Therefore, the anomaly extraction unit 311 stores the data obtained from the vehicle 10 into the auxiliary storage unit 303. Furthermore, for example, if a PH candidate location is extracted, past data at that PH candidate location is compared with current data. For example, such as... Figure 19As shown, the driving lane of vehicle 10 changes depending on whether PH (presumably a traffic hazard) is present. Therefore, for example, the average value of the driving lanes in the past predetermined period (first period) and the average value of the driving lanes in the future predetermined period (second period) from the current time point are calculated respectively. If the difference or ratio is greater than a predetermined value, it is determined that PH exists.

[0148] Furthermore, the driving lane can be obtained either using the detection values ​​of the position information sensor 105 or by analyzing images acquired by the external camera 108. Additionally, in Figure 19 The road shown is divided into 7 lanes, but the number of lanes can be changed depending on the width of the road.

[0149] Abnormal extraction section 311 passes through Figure 30 The process shown extracts a pH candidate position. Figure 33 This is a flowchart for determining whether pH exists at the pH candidate position. In server 30, this is executed at predetermined intervals. Figure 33 The processing is shown. Furthermore, regarding execution and... Figure 32 The routines shown follow the same processing steps, with the same symbols added but the explanations omitted.

[0150] In step S401, the anomaly determination unit 312 determines whether the number of data acquired corresponding to the PH candidate position is greater than or equal to a predetermined number. This acquired number is the number of data acquired from the same position after the PH candidate position is extracted. The predetermined number referred to here is the number of data points that allow for a high-precision calculation of the average value of the lanes traveled by vehicle 10, and is pre-stored in the auxiliary storage unit 303. A higher predetermined number improves the determination accuracy, but collecting data takes time; therefore, the predetermined number is determined based on the priority between determination accuracy and the time required to collect data. If the determination is affirmative in step S401, the process proceeds to step S402; otherwise, the process ends.

[0151] In step S402, the anomaly determination unit 312 calculates the average value of the driving lanes of vehicle 10 at the current time point at the PH candidate position. The anomaly determination unit 312 sets an analysis range based on the PH candidate position, and for each vehicle 10, extracts the driving lane when it enters the analysis range and calculates its average value. For example, this average value is calculated for a predetermined number of vehicles 10 that have recently passed through the analysis range.

[0152] In step S403, the anomaly determination unit 312 calculates the average driving lane of the vehicles 10 at the PH candidate positions in the past. Here, "past" refers to the period before the PH candidate positions were extracted. The anomaly determination unit 312 extracts records from the vehicle information stored in the vehicle information DB321 whose dates and times fall within a predetermined past period and whose locations fall within the analysis range, and obtains the driving lanes based on each location information. For example, by dividing road 7, the anomaly determination unit 312 calculates the range of locations for each lane and stores it in the auxiliary storage unit 303. Furthermore, it determines which of the lane ranges stored in the auxiliary storage unit 303 contains the location information stored in the vehicle information DB321. For example, the anomaly determination unit 312 extracts data from a predetermined number of vehicles 10 related to step S401 to calculate the average lane.

[0153] In step S404, the anomaly determination unit 312 determines whether the difference between the average value of the current driving lane calculated in step S402 and the average value of the past driving lane calculated in step S403 is greater than or equal to a predetermined value. The predetermined value is stored in the auxiliary storage unit 303 as the difference between the driving lanes in the presence of a PH (hypostasis) and in the absence of a PH. If the determination is affirmative in step S404, the process proceeds to step S303; otherwise, it proceeds to step S306.

[0154] As explained above, according to the second embodiment, by comparing the current and past driving lanes obtained from vehicle 10, it is possible to determine whether a PH exists.

[0155] Furthermore, in the second embodiment, the average lanes in the current and past locations are compared, but the past average lanes used in the comparison can also be average lanes in different locations. For example, the average lanes when vehicle 10 travels can be pre-calculated on roads throughout the country and used as the comparison object.

[0156] <Third Implementation Method>

[0157] In the first embodiment, it is determined whether an S-shaped maneuver has occurred using current vehicle information, and the presence of a potential hazard (PH) is determined based on the result. However, alternatively, it can be determined that a PH exists if the number of vehicles 10 performing S-shaped maneuvers increases when compared with past data. For example, vehicle data from periods when no PH has occurred can be stored in the auxiliary storage unit 303. Furthermore, a PH can also be determined to exist if the difference or ratio between the proportion of vehicles 10 that performed S-shaped maneuvers in the past and the proportion of vehicles 10 currently performing S-shaped maneuvers at the PH candidate position is greater than or equal to a predetermined difference or ratio.

[0158] The anomaly detection unit 312 of server 30 calculates the proportion of vehicles 10 that performed S-shaped maneuvers among all vehicles 10 that passed through the analysis range during a predetermined period in the past. Furthermore, the anomaly detection unit 312 calculates the proportion of vehicles 10 that performed S-shaped maneuvers among all vehicles 10 that passed through the analysis range during a predetermined period from the current time. Moreover, it compares the proportion of vehicles 10 that performed S-shaped maneuvers in the past with the current proportion; if the difference is greater than a predetermined difference, it determines that a PH (problem arises). The predetermined difference, referred to here, is the increased proportion of vehicles 10 that performed S-shaped maneuvers due to the occurrence of a PH, and is stored in auxiliary storage unit 303.

[0159] In this way, by comparing the proportion of vehicles 10 that have made S-shaped movements in the present and the past, it is also possible to determine whether a PH exists.

[0160] <Fourth Implementation>

[0161] In the fourth embodiment, the presence of a potential hazardous point (PH) is determined by combining human inspection of the road with road inspection. Sometimes, humans visually inspect the road. For example, it is also considered to detect road anomalies missed during monitoring by server 30. Furthermore, when vehicle traffic at the PH candidate location is low, determining the presence of a PH takes time, so it is also considered that humans go to the site for visual confirmation. On the other hand, visually inspecting all roads solely requires a large amount of manpower and time. Therefore, in the fourth embodiment, road monitoring is combined with human visual inspection and monitoring by server 30.

[0162] Server 30 obtains a schedule of visual inspections of the road by personnel. Within a predetermined period after the visual inspection, Server 30 does not determine the presence of a PH (Problem Area). That is, if a visual inspection is conducted and there is no PH or even if there is, it has been repaired, the presence of the PH is not determined within the predetermined period after the visual inspection. Furthermore, the presence or absence of a PH is determined after a predetermined period has elapsed since the visual inspection. For example, the predetermined period from the start of the visual inspection may not be executed in the first to third embodiments. Figure 30 This reduces the load on server 30. Furthermore, since a new PH (problem) may occur after a predetermined period has elapsed since the visual inspection, server 30 monitors this process. Specifically, it executes at predetermined intervals. Figure 30 This allows for the handling of pH issues. Therefore, in the event of a pH problem, the presence of pH can be identified without waiting for a subsequent visual inspection, enabling early intervention and repair.

[0163] Figure 34This is a flowchart for determining whether to monitor roads via server 30. This routine is executed at predetermined times on server 30. Additionally, this routine is executed, for example, for each road, for each region, or for each predetermined area. In step S501, the anomaly extraction unit 311 obtains a schedule of visual road inspections. For example, the schedule of visual road inspections is entered into user terminal 20 and stored in auxiliary storage unit 303 of server 30. This schedule includes the date and time of the visual inspection and location-related information.

[0164] In step S502, the anomaly extraction unit 311 determines whether the number of days elapsed since the visual inspection is more than a predetermined number of days. The predetermined number of days is stored in advance as the number of days at which a pH (problem) may occur in the auxiliary storage unit 303. If the determination is affirmative in step S502, the process proceeds to step S503; if the determination is negative, the process proceeds to step S504.

[0165] In step S503, the abnormal extraction unit 311 begins the process of determining the possibility of pH. This process is... Figure 30 The process is shown. On the other hand, in step S504, the anomaly determination unit 312 stops the process of determining the possibility of the presence of PH.

[0166] As explained above, according to the fourth embodiment, the presence of a PH is determined by combining visual inspection by a person with road monitoring by the server 30, thus reducing the computational load on the server 30. Furthermore, when relying solely on visual inspection, there is a possibility that it may take time until a sudden PH is detected; however, by combining the monitoring by the server 30, PHs can be detected at an earlier stage.

[0167] <Fifth Implementation>

[0168] In the fifth embodiment, the presence of a PH is determined by comparing the past actions and current actions of the same vehicle 10. For example, in... Figure 12 The explanation states that when the same vehicle 10 passes through a location where a PH (presumably a location with a stop) multiple times, even if the driver initially steps on the PH, they may not step on it again because they remember its location. Therefore, for example, even if the wheel speed sensor 107 detects a possibility of stepping on the PH, but subsequently detects no further possibility, the presence of a PH is considered. For example, the presence of a PH can also be determined if the wheel speed sensor 107 no longer detects it and the vehicle 10 performs an S-shaped maneuver. Furthermore, the presence of a PH can also be determined if the wheel speed sensor 107 no longer detects it and the vehicle 10 changes its lane.

[0169] Figure 35 This is a flowchart illustrating the process for determining the presence of pH according to the fifth embodiment. In the server 30, for each vehicle 10, execution is performed at predetermined times. Figure 35 The example shown. For steps that perform the same processes as in the flowchart above, the same symbols are used, but explanations are omitted. Figure 35 In the example shown, upon a positive determination in step S104, the process proceeds to step S601. In step S601, the anomaly extraction unit 311 registers the position of vehicle 10 as a PH candidate position. Here, if the wheel acceleration is above a predetermined acceleration, the position is immediately registered as a PH candidate position. This PH candidate position is a position that corresponds only to vehicle 10, and it is not processed as a PH candidate position even if other vehicles 10 pass through this position.

[0170] On the other hand, if the determination is negative in step S104, the process proceeds to step S602. In step S602, the exception determination unit 312 determines whether the position of vehicle 10 is a position registered as a PH candidate position. That is, it determines whether the information corresponding to stepping on the PH has been output from vehicle 10. If the determination is positive in step S602, the process proceeds to step S603; if the determination is negative, the process ends.

[0171] In step S603, the anomaly determination unit 312 determines whether the left and right offset of vehicle 10 is greater than or equal to a predetermined amount. In this step S603, it is determined whether vehicle 10 has performed an S-shaped maneuver. If, after being registered as a PH candidate position, the wheel acceleration is not greater than a predetermined acceleration and the lateral offset is greater than or equal to a predetermined amount, it is considered that vehicle 10 has performed an S-shaped maneuver to avoid a PH. If the determination is affirmative in step S603, the process proceeds to step S604; otherwise, it proceeds to step S605.

[0172] In step S604, the anomaly determination unit 312 determines that a PH exists at the PH candidate position. That is, due to changes in the behavior of vehicle 10 in the past (first period) and the present (second period), vehicle 10 exhibits behavior that avoids the PH, so it is determined that the PH exists. On the other hand, in step S605, the anomaly determination unit 312 determines that the PH does not exist at the PH candidate position. That is, after the wheel acceleration reaches a predetermined acceleration or higher, if vehicle 10 does not perform an S-shaped movement when passing through the road again, there is a high probability that the user will not recognize the PH. In this case, it is considered that the PH does not exist. Therefore, the anomaly determination unit 312 determines that the PH does not exist at this location. Moreover, in step S606, the anomaly determination unit 312 resets the PH candidate position by deleting the information stored as the PH candidate position about this location.

[0173] in addition, Figure 36 This is a flowchart illustrating the process of determining the presence of a pH based on the driving lane, as described in the fifth embodiment. In server 30, for each vehicle 10, execution is performed at predetermined intervals. Figure 36 The example shown. For steps that perform the same processes as in the flowchart above, the same symbols are used, but explanations are omitted. Figure 36 In the example shown, when a positive determination is made in step S104, in step S601, the anomaly extraction unit 311 registers the position of vehicle 10 as a potential PH position. In step S701, the anomaly extraction unit 311 stores the driving lane in which vehicle 10 enters the analysis range including the potential PH position in the auxiliary storage unit 303. This driving lane is a lane where a PH is possible.

[0174] On the other hand, if the determination is positive in step S602, in step S702, the anomaly determination unit 312 compares the driving lane stored in step S702 with the current driving lane to determine whether there has been a change. That is, it determines whether the driver remembered the location of PH and changed the driving lane in advance. The current driving lane is read from the vehicle information DB321 updated in step S102. If the determination is positive in step S702, the process proceeds to step S703; if the determination is negative, the process proceeds to step S704.

[0175] In step S703, the anomaly determination unit 312 determines that a PH exists at the PH candidate position. On the other hand, in step S704, the anomaly determination unit 312 determines that a PH does not exist at the PH candidate position. That is, after the wheel acceleration reaches or exceeds a predetermined acceleration, when the vehicle 10 passes through the road again, although the driving lane remains unchanged, the wheel acceleration becomes less than the predetermined acceleration. Therefore, it is considered that when the PH candidate position was stored, the wheel acceleration reached or exceeded the predetermined acceleration due to a major reason other than PH. Thus, the anomaly determination unit 312 determines that a PH does not exist at that location.

[0176] Furthermore, when determining the presence of a PH based solely on the result of one vehicle 10, there is a possibility of misjudgment due to the influence of factors other than the PH. Therefore, for example, if the presence of a PH is determined to occur in multiple vehicles 10, the notification unit 313 may generate a notification message. In this case, no further action is taken. Figure 34 as well as Figure 35 The processing in steps S304 and S305 is performed. Furthermore, the following processing is executed.

[0177] Figure 37 This is a flowchart illustrating the process of notifying multiple vehicles 10 of the presence of a PH (Potential Notice). In server 30, execution is performed at predetermined intervals. Figure 37The routine is shown below. In step S801, the notification unit 313 determines whether the number of vehicles 10 identified as having a pH is greater than or equal to a predetermined number. The predetermined number is stored in the auxiliary storage unit 303 as the number of vehicles 10 required for high-precision pH detection. In this step S801, it is determined whether the number of vehicles 10 identified as having a pH in step S604 or step S703 is sufficiently large. If the determination is affirmative in step S801, the process proceeds to step S802; if the determination is negative, the routine ends. In the case of a negative determination, no notification is sent to the user terminal 20.

[0178] In step S802, the notification unit 313 determines that a PH exists. That is, if a PH is determined to exist in multiple vehicles 10, then a PH is determined to exist at that location, and a notification message is sent to the user terminal 20 through processing after step S304.

[0179] As explained above, according to the fifth embodiment, it is possible to determine whether a PH exists based on the changes in the behavior of the same vehicle 10 when it passes through the same road.

[0180] <Other Implementation Methods>

[0181] The above-described embodiments are merely examples, and this disclosure can be implemented with appropriate modifications without departing from its spirit.

[0182] The processes and units described in this disclosure can be freely combined and implemented as long as they do not create technical contradictions.

[0183] Furthermore, it can be shown that processing intended for one device can also be performed by multiple devices. Alternatively, it can be shown that processing intended for different devices can also be performed by one device. In a computer system, the hardware architecture (server architecture) used to implement each function can be flexibly changed. For example, vehicle 10 or user terminal 20 may have some or all of the functions of server 30.

[0184] This disclosure can also be implemented by supplying a computer program with the functions described in the above embodiments to a computer, which has one or more processors that read and execute the program. Such a computer program can be provided to the computer either through a non-transitory computer-readable storage medium that can be connected to the computer's system bus, or via a network. Non-transitory computer-readable storage media include, for example, any type of disk such as a floppy disk, hard disk drive (HDD), optical disk (CD-ROM, DVD, Blu-ray disc, etc.), read-only memory (ROM), random access memory (RAM), EPROM, EEPROM, magnetic cards, flash memory, optical cards, and any type of medium suitable for storing electronic commands.

Claims

1. An information processing device comprising a control unit, The control unit, upon receiving initial information about the possibility of a road anomaly, determines the anomaly based on the behavior of multiple vehicles within a predetermined range, including the location corresponding to the initial information. The control unit obtains the first information based on the output values ​​of sensors that are respectively installed on the plurality of vehicles and output signals associated with the state of the road when the vehicle passes through a location on the road where there is a possibility of an anomaly. Furthermore, within the predetermined range, the control unit determines whether there is an anomaly on the road based on the number of vehicles that output information related to a first action taken when avoiding an anomaly on the road. Within the predetermined range, if the proportion of vehicles that output the first information (i.e., vehicles that passed through areas of road where an anomaly is likely to occur) and output information related to the first action is higher than the proportion of vehicles that did not output the first information (i.e., vehicles that did not pass through areas of road where an anomaly is likely to occur), the control unit determines that the road is abnormal. The first action is to drive in an S-shape.

2. The information processing apparatus according to claim 1, wherein, In the first action, the vehicle moves a predetermined distance to the right and to the left respectively.

3. The information processing apparatus according to claim 1, wherein, The control unit determines whether there is an anomaly on the road within the predetermined range based on the driving positions of the plurality of vehicles in the past first period and the driving positions of the plurality of vehicles in the second period after the first period.

4. The information processing apparatus according to claim 3, wherein, The control unit determines that there is an anomaly on the road if there is a predetermined difference between the driving positions of the plurality of vehicles in the first period and the driving positions of the plurality of vehicles in the second period within the predetermined range.

5. The information processing apparatus according to claim 1, wherein, The control unit determines whether there is an anomaly on the road based on changes in the behavior of the same vehicle when it traveled within the predetermined range in a first period and a second period after the first period.

6. The information processing apparatus according to claim 5, wherein, The control unit obtains the first information based on the output values ​​of sensors that output signals associated with the road state, respectively installed on the plurality of vehicles. Furthermore, the control unit responds to the situation where the same vehicle travels within the predetermined range during the first period and the second period, and outputs the first information during the first period but does not output information related to the first action as an action to avoid the abnormality of the road, and does not output the first information during the second period but outputs information related to the first action, and determines that there is an abnormality in the road.

7. The information processing apparatus according to claim 1, wherein, The control unit obtains the movement of the plurality of vehicles within the predetermined range by acquiring detection values ​​from sensors respectively installed on the plurality of vehicles and associated with the direction of travel.

8. The information processing apparatus according to claim 1, wherein, If the control unit determines that there is an anomaly on the road, it will notify the location of the anomaly to an external terminal.

9. The information processing apparatus according to claim 1, wherein, It has a storage unit that stores data relating to the movement of the plurality of vehicles that have traveled within the predetermined range.

10. The information processing apparatus according to claim 1, wherein, After a predetermined time has elapsed since the user began the inspection, the control unit determines whether there is any abnormality in the road.

11. An information processing method, The computer response obtains first information about the possibility of road anomalies, and determines the road anomaly based on the behavior of multiple vehicles within a predetermined range, including the location corresponding to the first information. The computer obtains the first information based on the output values ​​of sensors that are respectively installed on the plurality of vehicles and output signals associated with the state of the road when the vehicle passes through a location on the road where there is a possibility of an anomaly. Furthermore, within the predetermined range, the computer determines whether there is an anomaly on the road based on the number of vehicles that output information related to a first action taken when avoiding an anomaly on the road. Within the predetermined range, if the proportion of vehicles that output the first information (i.e., vehicles that passed through potentially abnormal sections of the road) and output information related to the first action is higher than the proportion of vehicles that did not output the first information (i.e., vehicles that did not pass through potentially abnormal sections of the road) and output information related to the first action, the computer determines that the road is abnormal. The first action is to drive in an S-shape.

12. The information processing method according to claim 11, wherein, The computer determines whether there is an anomaly on the road within the predetermined range based on the driving positions of the plurality of vehicles in a past first period and the driving positions of the plurality of vehicles in a second period after the first period.

13. The information processing method according to claim 12, wherein, If, within the predetermined range, the computer responds to a predetermined difference between the driving positions of the plurality of vehicles in the first period and the driving positions of the plurality of vehicles in the second period, it determines that the road is abnormal.

14. The information processing method according to claim 11, wherein, The computer determines whether there is an anomaly on the road based on changes in the behavior of the same vehicle within the predetermined range during a first period and a second period after the first period.

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