Information processing device, information processing system, information processing method and program

Abstract

To provide an information processing device which analyzes a caution-necessary event according to whether a driving state of a mobile object is in an automatic driving state or in a manual driving state to the collected caution-necessary event.SOLUTION: An information processing device comprises: caution point information collection means for collecting caution point information regarding a point where a caution-necessary event affecting a driving operation of a mobile object occurs; and setting means for weighting the caution point information according to whether the caution point information is collected in an automatic driving mode in which at least a part of the driving operation of the mobile object is automatically performed or in a manual driving mode in which the driving operation of the mobile object is manually performed so as to set the point as a caution point by using the weighted caution point information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing system, an information processing method, and a program for providing information to a moving object. [Background technology]

[0002] 2. Description of the Related Art In recent years, occurrence of an event requiring attention in a moving object has been detected by detecting a change in acceleration.

[0003] For example, Patent Document 1 discloses a factor analysis device that compares information regarding the situation in which a detected event requiring attention occurred with past events requiring attention that occurred in the same location as the event requiring attention, and estimates whether the event requiring attention was caused by environmental factors or driver factors. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-071492 A Summary of the Invention [Problem to be solved by the invention]

[0005] The factor analysis device of Patent Document 1 acquires probe information, including the traveling speed of a moving object, from the moving object, and analyzes the acquired probe information.

[0006] However, the factor analysis device of Patent Document 1 acquires probe information without distinguishing whether the mobile body that is the sender is in an autonomous driving state or a manual driving state. Therefore, for example, one example of a problem is that information on an event that requires attention when the mobile body is in an autonomous driving state, that is, an event that requires attention due to factors in the surrounding environment, is buried under other information.

[0007] The present invention has been made in consideration of the above-mentioned points, and one of its objectives is to provide an information processing device that analyzes collected events requiring attention depending on whether the driving state of a moving object is an automatic driving state or a manual driving state. [Means for solving the problem]

[0008] The information processing device described in claim 1 of the present application is characterized by having an attention point information collecting means for collecting attention point information related to a point where a cautionary event that affects the driving operation of a moving body has occurred, and a setting means for weighting the attention point information depending on whether the attention point information has been collected in an automatic driving mode in which at least a part of the driving operation of the moving body is performed automatically, or the attention point information has been collected in a manual driving mode in which the driving operation of the moving body is performed manually, and setting the point as an attention point using the weighted attention point information. [Brief description of the drawings]

[0009] [Figure 1] 1 is an overall view of an information processing system according to a first embodiment. [Diagram 2] FIG. 2 is a block diagram showing the configuration of a measurement terminal and a server according to the first embodiment. [Diagram 3] FIG. 4 is a flow chart showing information processing of the server according to the first embodiment. [Figure 4] FIG. 11 is a block diagram showing the configuration of a measurement terminal and a server according to a second embodiment. [Diagram 5] 5 is a conceptual diagram showing attention region information generated by the information generating means of FIG. 4. [Figure 6] FIG. 11 is a flow chart showing information processing of a server according to the second embodiment. [Figure 7] FIG. 11 is a diagram illustrating a detailed flow of a first mode transition determination process in the information processing system according to the second embodiment. [Figure 8] FIG. 11 is a diagram illustrating a detailed flow of a second mode transition determination process in the information processing system according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS EXAMPLES

[0010] Fig. 1 shows the overall configuration of an information processing system 100 according to the first embodiment. As shown in Fig. 1, the information processing system 100 is configured by connecting a measurement terminal 10 mounted on an automobile M as a moving body and a server 20 as an information processing device via a network NW. The moving body may be a moving body other than an automobile, such as an automobile, a motorcycle, an airplane, a ship, or a moving person.

[0011] Fig. 2 shows functional blocks of the measurement terminal 10 and the server 20 of the information processing system 100. As shown in Fig. 2, in the information processing system 100, the measurement terminal 10 mounted on each of a plurality of automobiles M and the server 20 are connected to each other so as to be able to communicate with each other.

[0012] The metering terminal 10 can be mounted on the vehicle M or can travel together with the vehicle M. The metering terminal 10 can be part of the vehicle M's navigation system.

[0013] The acceleration sensor 11 is a capacitance type or piezo-resistance type acceleration sensor, etc. The acceleration sensor 11 is, for example, a two-axis acceleration sensor, and detects acceleration in the front-rear direction, which is the moving direction of the automobile M, and acceleration in the left-right direction, which is perpendicular to the front-rear direction.

[0014] The GPS (Global Positioning System) device 12 is a device that receives signals (GPS signals) from GPS satellites, and obtains the position information of the automobile M.

[0015] The communication unit 13 is an interface that is communicably connected to the server 20 and the measurement terminals 10 mounted on other automobiles M via a network NW.

[0016] The control unit 14 includes, for example, a CPU (Central Processing Unit) that performs arithmetic processing. The control unit 14 can control the operation of each unit of the measurement terminal 10 including the acceleration sensor 11, the GPS device 12, and the communication unit 13.

[0017] The behavior information calculation unit 15 is one of the functional blocks of the control unit 14. The behavior information calculation unit 15 can calculate behavior information including the speed of the automobile M, acceleration (including forward and backward acceleration in the traveling direction of the automobile M, and left and right acceleration in a direction approximately perpendicular to the traveling direction), accelerator opening, braking strength, free running distance with the accelerator off, inter-vehicle distance from the automobile traveling in front of the automobile M, and the position of the automobile M, from the signals of the acceleration sensor 11 and the GPS device 12. That is, the behavior information calculation unit 15 can acquire probe information. The behavior information is, for example, information regarding the behavior of the automobile M per predetermined period. In the case of acceleration, it is, for example, the acceleration of the automobile M per 15 seconds. The predetermined period can be determined arbitrarily.

[0018] The acceleration and speed of the automobile M may be calculated and obtained, for example, based on an acceleration signal from the acceleration sensor 11 or a GPS signal from the GPS device 12. In addition, the speed of the automobile M may be calculated and obtained, for example, based on a vehicle speed pulse supplied from the automobile M.

[0019] The position of the automobile M may be acquired based on, for example, a GPS signal from the GPS device 12. The position of the automobile M may be calculated based on the amount of movement from a reference position, attitude information of the automobile M from a gyro device, or vehicle speed information obtained from a vehicle speed pulse of the automobile M. The behavior information calculation unit 15 may be capable of acquiring map information. That is, the position of the automobile M may be calculated and acquired by combining at least one of the GPS information from the GPS device 12, the attitude information of the automobile M from the gyro device, and the vehicle speed information of the automobile M with the map information.

[0020] In the following description, the longitudinal acceleration when the automobile M accelerates in the traveling direction (front-rear direction) of the automobile M is referred to as positive acceleration, and the acceleration when the automobile M decelerates is referred to as negative longitudinal acceleration. Note that negative longitudinal acceleration in the traveling direction is also referred to as deceleration. Regarding the lateral acceleration in the left-right direction, the lateral acceleration in the left direction in the traveling direction of the automobile M is referred to as positive acceleration, and the lateral acceleration in the right direction in the traveling direction is referred to as negative acceleration.

[0021] The communication unit 21 of the server 20 is communicatively connected via a network NW to the communication unit 13 of the measurement terminal 10 mounted on each of the multiple automobiles M or moving together with the automobiles M. The communication unit 21 can receive, for example, behavior information or probe information including the acceleration, speed, and position of the automobile M from the communication unit 13.

[0022] The control unit 23 includes, for example, a CPU (Central Processing Unit) that performs arithmetic processing, and is realized by a computer. The control unit 23 can control the operation of each unit of the server 20 including the communication unit 21. The control unit 23 can also obtain various information from the outside via the communication unit 21 and perform processing such as analysis on the obtained information. The CPU reads out a program corresponding to the processing content from the storage unit 22, and executes the read program to realize various functions.

[0023] The behavior information acquiring means 23a is one of the functional blocks of the control unit 23. The behavior information acquiring means 23a is capable of acquiring behavior information of the automobile M from each measurement terminal 10 mounted on the automobile M.

[0024] The attention point information collecting means 23b is one of the functional blocks of the control unit 23. The attention point information collecting means 23b is capable of collecting attention point information including a point where an event that requires attention and affects the driving operation of the automobile M has occurred (hereinafter, referred to as an event occurrence point) from the behavior information.

[0025] An event requiring attention (hereinafter, referred to as a "caution event") that affects the driving operation of the automobile M is, for example, a situation in which any one of the driving operations of sudden steering, sudden acceleration, or sudden deceleration occurs. In addition, the location where the caution event occurs is, for example, a location where a factor that may require any one of the driving operations of sudden steering, sudden acceleration, or sudden deceleration is encountered, a location where any one of the driving operations of sudden steering, sudden acceleration, or sudden deceleration occurs, etc.

[0026] The attention point information collecting means 23b is capable of determining that an attention event affecting the driving operation of the automobile M has occurred based on the acquired behavior information, and determining the event occurrence point based on the position information of the automobile M corresponding to the behavior included in the acquired behavior information. The event occurrence point may be a predetermined area including the point where the attention event occurred. For example, if the point where the attention event occurred is an intersection, the predetermined area may be a range with a radius of several tens of meters centered on the intersection.

[0027] Specifically, the attention point information collecting means 23b judges whether the acceleration corresponding to the sudden steering, the sudden acceleration, or the sudden deceleration is included in the behavior information, and collects the position where the acceleration corresponding to the sudden steering, the sudden acceleration, or the sudden deceleration occurred as the event occurrence point. Whether the acceleration corresponding to the sudden steering, the sudden acceleration, or the sudden deceleration occurred is judged, for example, by whether it exceeds a predetermined threshold value.

[0028] The attention point information collecting means 23b distinguishes whether the automobile M equipped with each measurement terminal 10 is in an automatic driving mode or a manual driving mode based on the acceleration included in the behavior information, and collects attention point information. That is, the attention point information collecting means 23b distinguishes between attention point information when the automobile M is in an automatic driving mode and attention point information when the automobile M is in a manual driving mode and collects the information.

[0029] Here, the automatic driving mode and the manual driving mode are driving control modes (hereinafter also referred to as driving modes) of the automobile M. The driving modes include a plurality of driving modes that differ depending on the degree of automation of the operation related to the driving of the automobile M.

[0030] For example, the manual driving mode corresponds to autonomous driving level 0. The autonomous driving modes correspond to autonomous driving levels 1 to 5. Here, the autonomous driving levels are defined by the Japanese government and the National Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation.

[0031] The autonomous driving mode includes a mode that assists with at least one of the accelerator operation, the brake operation, and the steering operation. The mode that assists with an operation is a mode that intervenes in the driving operation when the driver's operation does not satisfy a predetermined condition, such as an emergency brake.

[0032] The attention point information collecting means 23b, for example, determines that the vehicle is in the autonomous driving mode when the acceleration (deceleration) in the traveling direction of the vehicle M is equal to or less than a predetermined threshold, and collects the attention point information. Also, for example, the attention point information collecting means 23b determines that the vehicle is in the autonomous driving mode when the absolute value of the acceleration in the lateral direction of the vehicle M is equal to or less than a predetermined threshold, and collects the attention point information.

[0033] Specifically, when automobile M is traveling in autonomous driving mode, if there is no automobile traveling ahead, it maintains a constant speed on a straight road, with minimal acceleration and deceleration, little variation, and smoothness.

[0034] In addition, when automobile M traveling in autonomous driving mode is following a vehicle traveling in front, its speed on a straight road changes to match the vehicle traveling in front, and acceleration / deceleration is minimal, has little variation, and is smooth.

[0035] Furthermore, when the automobile M traveling in the autonomous driving mode is following a vehicle traveling ahead, the distance between the automobile M and the vehicle traveling ahead of the automobile M is constant. In other words, the automobile M traveling in the autonomous driving mode travels while maintaining a constant distance between the automobiles, regardless of the weather or the time of day (daytime or nighttime).

[0036] In addition, as the speed range of the automobile M increases, the stopping distance of the automobile M increases. Therefore, the inter-vehicle distance increases as the speed range of the automobile M increases. In other words, the automobile M traveling in the autonomous driving mode travels at a constant inter-vehicle distance according to the traveling speed.

[0037] In contrast, it is difficult for the automobile M traveling in manual driving mode to maintain a strict constant speed. Also, the automobile M traveling in manual driving mode is subject to acceleration indicating sudden deceleration. Furthermore, the automobile M traveling in manual driving mode has a variation in the distance between the automobile M traveling in front and the automobile traveling in front when traveling and when stopped.

[0038] The attention point information collection means 23b determines whether the automobile M is in an automatic driving mode or a manual driving mode, taking into account the characteristics and factors of the automatic driving mode and manual driving mode, and collects attention point information for each determined driving mode.

[0039] The attention point information collecting means 23b judges whether at least a part of the driving operations related to acceleration, at least a part of the driving operations related to braking, and at least a part of the driving operations related to steering are performed automatically. For example, in an auto cruise control that maintains the traveling speed of the automobile M at a set speed, some of the driving operations related to acceleration are performed automatically. In this case, the attention point information collecting means 23b judges that the driving mode is automatic even if the driving operations related to braking and steering are performed manually.

[0040] In addition, the attention point information collecting means 23b determines whether at least one of the driving operations of steering, accelerating, and braking of the automobile M is being performed automatically.

[0041] In other words, when determining the autonomous driving mode, the attention point information collection means 23b may determine the autonomous driving mode for only one of the driving operations related to acceleration, braking, and steering, or may determine the autonomous driving mode for two or more of these driving operations.

[0042] Note that the determination of the driving mode by the attention point information collecting means 23b is not limited to this, and for example, the attention point information collecting means 23b may determine whether each of the steering, acceleration, and braking operations of the automobile M is performed automatically. That is, the automatic driving mode may be determined when each of the steering, acceleration, and braking operations of the automobile M is performed automatically. In addition, the automatic driving level does not necessarily need to be determined, and for example, whether or not the automatic driving mode is set may be determined for each of the accelerator operation, brake operation, and steering operation.

[0043] The traffic environment acquisition means 23c is one of the functional blocks of the control unit 23. The traffic environment acquisition means 23c is capable of acquiring traffic environment information of an event occurrence point. The traffic environment acquisition means 23c may acquire the traffic environment information by referring to a traffic environment information database that stores the traffic environment information of the event occurrence point in the storage unit 22 described later. The traffic environment acquisition means 23c may acquire the traffic environment information by communicating with an external device via the communication unit 21.

[0044] The setting means 23d is one of the functional blocks of the control unit 23. The setting means 23d can set an event occurrence point as a caution point by weighting any one of the caution point information collected in the automatic driving mode and the caution point information collected in the manual driving mode.

[0045] For example, the setting unit 23d counts the number of caution events that have occurred at an event occurrence point included in the caution point information, and sets the caution point as a caution point when the number of caution events exceeds a predetermined value.

[0046] When setting a point as a caution point, the setting means 23d assigns a weight to the caution point information depending on whether the caution point information was collected in an automatic driving mode or in a manual driving mode, and sets the event occurrence point as a caution point using the weighted caution point information.

[0047] For example, in the autonomous driving mode, there is no human error by the driver, so the occurrence of caution events is low. Therefore, the weighting is increased for caution events that occur in the autonomous driving mode and setting processing is performed. As an example, the setting means 23d performs the determination processing by multiplying the number of caution events that occurred at the event occurrence point included in the caution point information collected in the autonomous driving mode by "2".

[0048] On the other hand, in the manual driving mode, since human error by the driver is also included, it is considered that there are more caution events than in the automatic driving mode. Therefore, the setting means 23d performs setting processing by reducing the weight for caution events that occurred in the manual driving mode. As an example, the setting means 23d performs judgment processing by directly using the number of caution events that occurred at the event occurrence point included in the caution point information collected in the manual driving mode.

[0049] The setting means 23d may also weight each piece of attention point information according to the degree of automation of the driving operation and set the event occurrence point as the attention point. Specifically, weighting may be performed according to the autonomous driving level. For example, as the autonomous driving level becomes higher, that is, as the autonomous driving level approaches 5, the probability of an event requiring attention occurring due to human error by the driver becomes lower, so that the weighting may be increased for processing.

[0050] Furthermore, the setting unit 23d may set the event occurrence point according to the automatic driving mode and the manual driving mode as the attention point. In this way, it is possible to analyze the attention event according to the automatic driving mode and the manual driving mode.

[0051] The setting unit 23d may set the attention points according to the traffic volume. That is, the number of attention events that occurred at the event occurrence point included in the attention point information is divided by the traffic volume at the event occurrence point to perform leveling.

[0052] The providing means 23e is one of the functional blocks of the control unit 23. The providing means 23e can provide each vehicle M with a point of attention. The providing means 23e can provide each vehicle M with a point of attention according to, for example, an automatic driving mode and a manual driving mode.

[0053] The storage unit 22 includes, for example, a hard disk, a flash memory, a solid state drive (SSD), a random access memory (RAM), etc., and is capable of storing information such as moving object information received by the communication unit 21. The storage unit 22 is also capable of storing threshold information used by the attention point information collecting means 23b when determining the operation mode of the automobile M, and threshold information used by the setting means 23d when setting an event occurrence point as an attention point.

[0054] The storage unit 22 can store information that is used as a reference when each functional block is judged. The storage unit 22 can store map information, etc. The storage unit 22 stores various programs such as a Basic Input Output System (BIOS) and software.

[0055] The storage unit 22 has a traffic environment information database (not shown) that stores traffic environment information of an event occurrence point referred to by the traffic environment acquisition means 23c. The storage unit 22 also has an attention point information database (not shown) that stores attention point information collected by the attention point information collection means 23b. The storage unit 22 also has an attention point database (not shown) that stores attention points set by the setting means 23d.

[0056] Fig. 3 shows the autonomous driving mode determination process executed by the server 20. As shown in Fig. 3, the behavior information acquisition means 23a acquires behavior information of the automobile M from each measurement terminal 10 mounted on the automobile M (step S101).

[0057] The attention point information collecting means 23b acquires information on whether the driving operation of the automobile M is being performed automatically, that is, information on the driving mode of the automobile M, based on the behavior information acquired in step S101 (step S102).

[0058] The attention point information collecting means 23b determines whether the longitudinal acceleration of the behavior information is equal to or greater than a predetermined threshold value (step S103).

[0059] If it is determined in step S103 that the longitudinal acceleration is greater than or equal to the threshold (step S103: Y), the attention point information collection means 23b determines that a caution event has occurred in the vehicle M (step S104), determines the event occurrence point based on the position information of the vehicle M corresponding to the point where the caution event occurred in the vehicle M, which is included in the behavior information (step S105), and records the attention point information including the event occurrence point in the attention point information database (step S106).

[0060] If it is determined in step S103 that the longitudinal acceleration is not equal to or greater than the threshold (step S103: N), the attention point information collecting means 23b determines whether the lateral acceleration of the behavior information is equal to or greater than a predetermined threshold (step S107).

[0061] If it is determined in step S107 that the lateral acceleration is equal to or greater than the threshold (step S107: Y), the attention point information collecting means 23b determines that a caution event has occurred in the vehicle M (step S104), determines the event occurrence point based on the position information of the vehicle M corresponding to the point where the caution event occurred in the vehicle M, which is included in the behavior information (step S105), and records the caution point information including the event occurrence point in the attention point information database (step S106). In this way, the attention point information collecting means 23b collects caution point information.

[0062] If it is determined in step S107 that the lateral acceleration is not greater than or equal to the threshold (step S107: N), the attention point information collection means 23b determines that no attention event has occurred for the vehicle M, i.e., that the event has not yet occurred (step S108), and terminates the processing.

[0063] The setting means 23d reads out the number (S) of caution events that occurred at the event occurrence point included in the caution point information collected by the caution point information collecting means 23b for each operation mode. The setting means 23d calculates a judgment value (DV) by weighting the number (S) of caution events that occurred at the event occurrence point for each operation mode (step S109).

[0064] For example, the setting means 23d calculates the judgment value (DV) by adding up the number of caution events in the automatic driving mode (SA) and the number of caution events in the manual driving mode (SM). At this time, the setting means 23d calculates the judgment value (DV) by multiplying the number of caution events in the automatic driving mode (SA) by "2" as shown in the following formula (Formula 1), and by using the number of caution events in the manual driving mode (SM) as is.

[0065]

number

[0066] The setting means 23d judges whether or not the judgment value (DV) calculated in step S109 exceeds a predetermined specified value (K) as shown in the following formula (Math. 2) (step S110).

[0067]

number

[0068] When the setting unit 23d determines in step S110 that the judgment value (DV) exceeds the specified value (K) (step S110: Y), the setting unit 23d sets the event occurrence point as a caution point (step S111). The setting unit 23d records the set caution point in the caution point database.

[0069] The providing means 23e provides the attention points to the measurement terminals 10 mounted on the other traveling automobiles M by reading out the attention points from the attention point information database and transmitting the attention points (step S112).

[0070] When it is determined in step S110 that the determination value (DV) does not exceed the specified value (K) (step S110: N), the setting means 23d ends the process.

[0071] The process of step S109 may be executed when the number (S) of the caution events occurring at the event occurrence point exceeds a predetermined cutoff value. Specifically, when executing the process of step S109, the setting means 23d may determine whether the number (S) of the caution events exceeds the cutoff value (T) as shown in the following formula (Mathematical formula 3). The setting means 23d may execute the process of step S109 when the number (S) of the caution events exceeds the cutoff value (T). Also, the setting means 23d may end the process when the number (S) of the caution events is equal to or less than the cutoff value (T).

[0072]

number

[0073] The process of step S110 may be performed by leveling based on the traffic volume at the event occurrence point. For example, as shown in the following formula (Formula 4), the number (S) of events of caution occurring at the event occurrence point for each operation mode may be divided by the traffic volume (N) to obtain a judgment value (DV), and the judgment value (DV) may be determined based on whether or not it exceeds a specified value (K) determined for each operation mode. The traffic volume (N) is acquired by the traffic environment acquisition means 23c by referring to the traffic volume at the event occurrence point in the traffic environment information database.

[0074]

number

[0075] The setting means 23d may set the event occurrence point as a caution point for each operation mode. The setting means 23d may make a judgment using a different specified value (K) for each operation mode, for example, as shown in the following formula (Math. 5). Specifically, the setting means 23d judges whether the value obtained by dividing the number of caution events (SA) in the automatic driving mode by the traffic volume (N) exceeds the specified value (KA) for the automatic driving mode. The setting means 23d also judges whether the value obtained by dividing the number of caution events (SM) in the manual driving mode by the traffic volume (N) exceeds the specified value (KM) for the manual driving mode. The specified value (KA) for the automatic driving mode is an integer different from the specified value (KM) for the manual driving mode.

[0076]

number

[0077] Furthermore, in this embodiment, the steering mode information is obtained based on the forward / backward acceleration and left / right acceleration of the behavior information, but this is not limited to this. For example, the behavior information may include an identifier that identifies the automatic driving mode or the manual driving mode, and the steering mode may be determined by reading the identifier.

[0078] As described above, the information processing system 100 of this embodiment assigns a weight to either the attention point information collected in the automatic driving mode or the attention point information collected in the manual driving mode, and sets the event occurrence point as a attention point.

[0079] Therefore, according to the information processing system 100 of this embodiment, it is possible to analyze the collected caution events depending on the driving state of the automobile M, i.e., whether the driving mode is an automatic driving mode or a manual driving mode. EXAMPLES

[0080] An information processing system 100 according to a second embodiment will be described. The information processing system according to the second embodiment generates information on a position where the automobile M has shifted from one of the automatic driving mode and the manual driving mode to the other driving mode as an area to be careful of. Note that the same components as those in the information processing system 100 according to the first embodiment are denoted by the same reference numerals and will not be described.

[0081] Fig. 4 illustrates functional blocks of the measurement terminal 10 and the server 20 of the information processing system 100 according to the second embodiment. As illustrated in Fig. 4, the configuration of the information processing system 100 according to the second embodiment is different from that of the information processing system 100 according to the first embodiment in the configuration of the control unit 23 of the server 20.

[0082] The location information acquisition means 23f is one of the functional blocks of the control unit 23. It is possible to acquire location information of the automobile M that has undergone a mode transition from one of the automatic driving mode and the manual driving mode to the other driving mode.

[0083] Specifically, the location information acquisition means 23f can acquire location information of the automobile M where a mode transition has been made for at least one of the driving operations of steering, accelerating, and braking of the automobile M. The location information acquisition means 23f can also acquire location information of the automobile M where a mode transition has been made for each of the driving operations of steering, accelerating, and braking of the automobile M. The location information may be a predetermined area that is divided in advance and includes the point where the mode transition has been made.

[0084] For example, when determining a mode transition of the automobile M, the position information acquisition means 23f may determine a mode transition for only one of the driving operations related to acceleration, braking, and steering, or may determine a mode transition for two or more of these driving operations.

[0085] In addition, the location information acquisition means 23f can acquire location information of the automobile M in which a change has occurred in the degree of automation of driving operations in the automatic driving mode and location information of the automobile M in which a change has occurred in the degree of automation of driving operations in the manual driving mode. For example, when determining a mode transition of the automobile M, the location information acquisition means 23f may determine a change in the automatic driving level of the automobile M as the mode transition.

[0086] The position information acquisition means 23f can determine the mode transition based on, for example, a change in the longitudinal acceleration and a change in the lateral acceleration included in the behavior information. In addition, the behavior information may include operation mode change information indicating that the operation mode has been changed, and the position information acquisition means 23f may determine the mode transition by reading the behavior information.

[0087] The information generating means 23g is one of the functional blocks of the control unit 23. The information generating means 23g is capable of generating attention area information based on the position information acquired by the position information acquiring means 23f, with the position of the position information being set as an attention area.

[0088] The information generating means 23g generates attention area information using first location information where a first mode transition from an automatic driving mode to a manual driving mode has occurred and second location information where a second mode transition from a manual driving mode to an automatic driving mode has occurred in mutually different manners.

[0089] For example, the information generating means 23g generates attention area information as a heat map that changes according to the frequency of the first mode transition from the first position information, and the information generating means 23g generates attention area information as a heat map that changes according to the frequency of the second mode transition from the second position information.

[0090] The storage unit 22 has a position information database (not shown) that stores the position information of the automobile M where the mode transition has been performed, which is acquired by the position information acquisition means 23f. The storage unit 22 also has an attention area information database (not shown) generated by the information generation means 23g.

[0091] Fig. 5 is a conceptual diagram showing the attention area information generated by the information generating means 23g. As shown in Fig. 5, a road R on which an automobile M travels is divided into a plurality of areas E that are equally spaced apart. Each area E is displayed in a different color according to the frequency of mode transitions that occur within the area E.

[0092] For example, the area E where the frequency of the first mode transition or the second mode transition is relatively the highest, i.e., the area E of "occurrence frequency (high)", is displayed in red, for example. The area E where the frequency of the first mode transition or the second mode transition is slightly lower than the "occurrence frequency (high)", i.e., the area E of "occurrence frequency (medium-high)", is displayed in orange, for example. The area E where the frequency of the first mode transition or the second mode transition is lower than the "occurrence frequency (medium-high)", i.e., the area E of "occurrence frequency (medium-low)", is displayed in green, for example. The area E where the frequency of the first mode transition or the second mode transition is relatively the lowest, i.e., the area E of "occurrence frequency (low)", is displayed in blue, for example. The illustrated colors are merely examples, and the area E of "occurrence frequency (low)" in particular may be displayed in the same way as a normal map without being colored.

[0093] Fig. 6 shows the autonomous driving mode determination process executed by the server 20. As shown in Fig. 6, the behavior information acquisition means 23a acquires behavior information of the automobile M from each measurement terminal 10 mounted on the automobile M (step S201).

[0094] The position information acquisition means 23f performs a first mode transition determination process to determine whether the automobile M transitions from the automatic driving mode to the manual driving mode (step S202). The position information acquisition means 23f determines whether the first mode transition has occurred based on the process of step S202 (step S203).

[0095] When the location information acquisition means 23f determines in step S203 that a first mode transition has occurred (step S203: Y), it acquires first location information where the first mode transition has occurred (step S204) and records the first location information in the location information database.

[0096] The information generating means 23g reads out the first location information from the location information database, generates attention region information in a heat map form according to the frequency at which the first mode transition occurred (step S205), and records the attention region information in the attention region information database.

[0097] The providing means 23e reads out the attention area information generated in step S205 from the attention area information database, and provides it by transmitting it to the measurement terminal 10 mounted on the other automobile M (step S206).

[0098] When the location information acquisition means 23f determines in step S203 that the first mode transition has not occurred (step S203: N), the location information acquisition means 23f performs a second mode transition determination process to determine whether the automobile M has transitioned from the manual driving mode to the automatic driving mode (step S207). The location information acquisition means 23f determines whether the second mode transition has occurred based on the process of step S207 (step S208).

[0099] When the location information acquisition means 23f determines in step S208 that a second mode transition has occurred (step S208: Y), it acquires second location information where the second mode transition has occurred (step S209) and records the second location information in the location information database.

[0100] The information generating means 23g reads the second position information from the position information database, generates attention region information in a heat map form according to the frequency at which the second mode transition occurred (step S205), and records the attention region information in the attention region information database.

[0101] The providing means 23e reads out the attention area information generated in step S205 from the attention area information database, and provides it by transmitting it to the measurement terminal 10 mounted on the other automobile M (step S206).

[0102] When it is determined in step S208 that the second mode transition has not occurred (step S208: N), the position information acquisition means 23f performs processing assuming that the mode transition has not occurred (step S210), and ends the processing.

[0103] 7 is a diagram showing a more detailed flow of the first mode transition determination process (step S202). Step S202 may be configured as a subroutine as shown in FIG.

[0104] As shown in FIG. 7, the position information acquisition means 23f determines whether the longitudinal acceleration, which is the moving direction of the automobile M, has changed beyond a predetermined threshold (step S301). Specifically, the longitudinal acceleration, which is the moving direction of the automobile M traveling in the automatic driving mode, and the lateral acceleration with respect to the moving direction are assumed to be constant. In contrast, the longitudinal acceleration of the automobile M traveling in the manual driving mode is assumed to vary and exceed a predetermined threshold. Therefore, the position information acquisition means 23f analyzes the change in the longitudinal acceleration per predetermined time (e.g., one minute). For example, when the longitudinal acceleration is equal to or less than a predetermined threshold before a specific time within the predetermined time, and changes to exceed the predetermined threshold after the specific time, the position information acquisition means 23f considers that the mode has been shifted from the automatic driving mode to the manual driving mode.

[0105] When the position information acquisition means 23f determines in step S301 that the longitudinal acceleration, which is the moving direction of the automobile M, has changed beyond a predetermined threshold (step S301: Y), it processes the process as if a first mode transition has occurred (step S302). That is, when there is a change in the positive longitudinal acceleration that exceeds the threshold, the position information acquisition means 23f processes the process as if a first mode transition has occurred for the driving operation related to acceleration (accelerator operation). Also, when there is a change in the negative longitudinal acceleration that exceeds the threshold, the position information acquisition means 23f processes the process as if a first mode transition has occurred for the driving operation related to braking (brake operation).

[0106] If the position information acquisition means 23f determines in step S301 that the longitudinal acceleration of the automobile M has not changed beyond a predetermined threshold (step S301: N), it determines whether the lateral acceleration of the automobile M has changed beyond a predetermined threshold (step S303).

[0107] For example, if the lateral acceleration is below a predetermined threshold before a specific time within the specified period of time, and changes to exceed the predetermined threshold after the specific time, the location information acquisition means 23f considers that a mode transition has occurred from automatic driving mode to manual driving mode.

[0108] When the position information acquisition means 23f determines in step S303 that the lateral acceleration of the automobile M has changed beyond a predetermined threshold (step S303: Y), it performs processing assuming that a first mode transition has occurred (step S302). That is, when there is a change in the positive or negative lateral acceleration that exceeds a threshold, the position information acquisition means 23f performs processing assuming that a first mode transition has occurred regarding the driving operation related to steering (steering operation).

[0109] When it is determined in step S303 that the lateral acceleration of the automobile M has not changed beyond a predetermined threshold (step S303: N), the position information acquisition means 23f ends the process.

[0110] 8 is a diagram showing a more detailed flow of the second mode transition determination process (step S207). Step S207 may be configured as a subroutine as shown in FIG.

[0111] 8, the position information acquiring means 23f determines whether the longitudinal acceleration, which is the moving direction of the automobile M, has changed to a predetermined threshold or less (step S401). For example, when the longitudinal acceleration exceeds the predetermined threshold before a specific time within the given period of time and changes to the predetermined threshold or less after the specific time, the position information acquiring means 23f considers that the mode has been shifted from the manual driving mode to the automatic driving mode.

[0112] When the position information acquisition means 23f determines in step S401 that the longitudinal acceleration of the automobile M has changed to a predetermined threshold or less (step S401: Y), it processes the process as if a second mode transition has occurred (step S402). That is, when there is a change in the positive longitudinal acceleration that exceeds the threshold, the position information acquisition means 23f processes the process as if a second mode transition has occurred for the driving operation related to acceleration (accelerator operation). Also, when there is a change in the negative longitudinal acceleration that exceeds the threshold, the position information acquisition means 23f processes the process as if a second mode transition has occurred for the driving operation related to braking (brake operation).

[0113] If the position information acquisition means 23f determines in step S401 that the longitudinal acceleration of the automobile M has not changed below a predetermined threshold (step S401: N), it determines whether the lateral acceleration of the automobile M has changed below a predetermined threshold (step S403).

[0114] For example, if the position information acquisition means 23f detects that the lateral acceleration exceeds a predetermined threshold before a specific time within the specified period of time, and changes to a value below the predetermined threshold after the specific time, it is considered that a mode transition has occurred from manual driving mode to automatic driving mode.

[0115] When it is determined in step S403 that the lateral acceleration of the automobile M has changed to a predetermined threshold or less (step S403: Y), the position information acquisition means 23f performs processing assuming that a second mode transition has occurred (step S402). That is, when the lateral acceleration has changed to a threshold or less, the position information acquisition means 23f performs processing assuming that a second mode transition has occurred for the driving operation related to steering (handle operation).

[0116] When it is determined in step S403 that the lateral acceleration of the automobile M has not changed beyond a predetermined threshold (step S403: N), the position information acquisition means 23f ends the process.

[0117] As described above, the information processing system 100 of this embodiment generates information regarding the position where the automobile M has shifted from one of the driving modes, the automatic driving mode or the manual driving mode, to the other driving mode as an area requiring attention.

[0118] Therefore, according to the information processing system 100 of this embodiment, it is possible to analyze the collected caution events depending on the driving state of the automobile M, i.e., whether the driving mode is an automatic driving mode or a manual driving mode.

[0119] In particular, according to the information processing system 100 of this embodiment, it is possible to realize a safer driving environment by generating attention area information regarding switching points of driving modes from autonomous driving mode to manual driving mode, or from manual driving mode to autonomous driving mode. [Explanation of symbols]

[0120] 100 Information Processing Systems 10. Measurement terminal 20 Servers 23a Behavioral information acquisition means 23b Means of collecting information on caution points 23c Traffic environment acquisition means 23d Setting method 23e Means of provision 23f Location information acquisition means 23g Information generation means

Claims

[Claim 1] An attention point information collecting means for collecting attention point information regarding a point where an event that requires attention and affects the driving operation of a moving object has occurred; and a setting means for weighting the attention point information depending on whether the attention point information was collected in an automatic driving mode in which at least a part of the driving operation of the mobile body is performed automatically, or the attention point information was collected in a manual driving mode in which the driving operation of the mobile body is performed manually, and setting the location as an attention point using the weighted attention point information.

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