JP2015035081A - Driving support method, driving support device and driving support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To support drivers to perform safe driving.SOLUTION: A driving support method applied to a driving support device includes the steps of: acquiring pieces of biological information detected in drivers of a plurality of vehicles, and pieces of position information and time information indicating positions and time at which the respective pieces of biological information are detected; on the basis of the plurality of pieces of acquired position information, time information and biological information, estimating travel directions of vehicles of which drivers have predetermined biological information; and transmitting information corresponding to the predetermined biological information to vehicles other than the vehicles of which travel directions are estimated.

Description

  The present application relates to a driving support method, a driving support device, and a driving support program.

  There is a near-miss map that shows information on dangerous points (hereinafter referred to as “near-miss points”) that did not result in a vehicle accident but could be there. The driver can prevent an accident from occurring by using the near-miss map described above. The near-miss map is created by collecting detection of a driver's (driver's) biological information and abnormal operation of the vehicle (for example, a sudden handle, a sudden brake, etc.) and the detected location (for example, see Patent Document 1). .

JP 2007-47914 A

  However, for example, a nuisance vehicle that performs dangerous overtaking or abnormal low-speed driving is moving due to traveling, so that its position cannot be recognized as a fixed near-miss point. Therefore, even if the above-described troubled vehicle is approaching, the driver cannot grasp the content.

  In one aspect, the present invention is directed to assisting the driver in safe driving.

  In the driving support method in one aspect, the driving support device acquires the biological information detected for each driver of the plurality of vehicles, the positional information and the time information from which the biological information is detected, Based on the position information, the time information, and the biological information, the movement direction of the vehicle group indicating the predetermined biological information is estimated, and the vehicle information other than the vehicle group in the estimated movement direction is added to the predetermined biological information. Send the corresponding information.

  The driver can be supported for safe driving.

It is a figure which shows an example of schematic structure of a driving assistance system. It is a figure which shows an example of the hardware constitutions of a driving assistance device. It is a figure which shows an example of a process of a driving assistance system. It is a figure for demonstrating biometric information. It is a figure which shows the specific example of the biometric information obtained from the biometric sensor. It is a flowchart which shows an example of the process in a mental condition judgment part. It is a figure which shows an example of a driving | running state database. It is a flowchart which shows an example of a process of the troublesome vehicle estimation part. It is a figure for demonstrating the driving | running state of a vehicle. It is a figure which shows an example of the route estimation in a troublesome vehicle route estimation part.

  Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

<Example of schematic configuration of driving support system>
FIG. 1 is a diagram illustrating an example of a schematic configuration of a driving support system. The driving support system 10 illustrated in FIG. 1 includes one or more vehicles 11-1 to 11-n (hereinafter collectively referred to as “vehicle 11” as necessary) and a driving support device 12. The vehicle 11 is connected by a communication network 13 typified by the Internet or the like in a state where data can be transmitted and received.

  In the driving support system 10 shown in the example of FIG. 1, the vehicle 11 is a vehicle registered in advance in the driving support device 12. For example, the vehicle 11 acquires biological information of a driver who is driving the vehicle 11, position information of the vehicle 11, time information, and the like every predetermined time. In addition, driving | running | working means the state in which the engine of the vehicle 11 is working, for example, Although the case where the vehicle is not only driving | running | working but is stopping is included, it is not limited to this. Further, the vehicle 11 determines the current mental state of the driver from the acquired biological information and the normal biological information as a reference, and the communication network 13 together with the acquired positional information and time information. Is transmitted to the driving support device 12 via.

  In addition, the vehicle 11 sends a message (driving support information) corresponding to the driving support information obtained from the driving support device 12 via the communication network 13 from the information providing unit provided in the vehicle 11 to the driver. provide.

  The vehicle 11 is, for example, a passenger car, a truck, a bus, a trailer, a taxi, a motorcycle, or the like, but is not limited thereto, and may be an emergency vehicle such as a fire truck, an ambulance, or a police car.

  The driving support device 12 receives driving state information (mental state information, position information, time information) during driving of each driver at predetermined time intervals from, for example, the plurality of vehicles 11-1 to 11-n. Moreover, the driving assistance apparatus 12 estimates the moving direction of the vehicle group which shows a predetermined mental state (for example, an irritation state, a near state, etc.) based on the received several driving state information. The vehicle group indicates, for example, a plurality of vehicles existing in a predetermined area.

  Moreover, the driving assistance apparatus 12 transmits the driving assistance information according to the estimated moving direction via the communication network 13 with respect to vehicles other than the vehicle group mentioned above based on the estimated result.

  The driving support device 12 is a server, a personal computer (PC), or the like, but is not limited to this, and may be realized by, for example, cloud computing having one or more information processing devices.

  The communication network 13 transmits and receives data to and from the driving support device 12 using, for example, wireless communication. However, the communication network 13 is not limited to this, and for example, a part of the communication network 13 may have a wired line. For example, the communication network 13 enables transmission / reception of data between the vehicle 11 and the driving support device 12 via a communication antenna, a base station, a relay device, and the like installed at predetermined intervals on the road.

  Here, a functional configuration example of the vehicle 11 and the driving support device 12 applied to the present embodiment will be specifically described. In addition, about the vehicle 11, the functional component part which concerns on this embodiment shall be demonstrated, and description of the structure in the engine part, the vehicle operation part, etc. which are normally provided as the vehicle 11 is abbreviate | omitted.

<Example of functional configuration of vehicle 11>
The vehicle 11 includes a biological sensor 21 as an example of a biological information acquisition unit, a Global Positioning System (GPS, global positioning system) 22 as an example of a position information acquisition unit, a time unit 23, a mental state determination unit 24, The map data 25 as an example of a storage unit, a communication unit 26, and an information providing unit 27 are included.

  The biometric sensor 21 senses biometric information while the driver of the vehicle 11 is driving. The biological information obtained from the biological sensor 21 is, for example, at least one piece of biological information such as a driver's heartbeat, brain wave, and body temperature, but is not limited thereto.

  The driver acquires biological information by wearing a wristband or the like that measures the heart rate in the vehicle 11 or wearing a headgear, helmet, headband, glasses, or the like that measures brain waves or body temperature. It is not limited to this. For example, in the present embodiment, the driver's biometric information can be acquired using various biosensors 21 such as a thermometer, a thermosensor, and a heart rate measurement sensor provided on a handle.

  The GPS 22 acquires position information (for example, latitude / longitude) of the vehicle 11 using a communication satellite or the like. In addition, about the method of acquiring the positional information on the vehicle 11, it is not limited to the method using GPS22. For example, in the present embodiment, the installation position of the communication device is determined depending on which communication device the vehicle 11 is communicating with among the communication devices of the wireless antenna, the base station, and the relay device installed at each point on the road. It can be acquired as position information of the vehicle 11.

  The time unit 23 acquires date and time information from a clock or the like provided in the vehicle 11. The time unit 23 may be, for example, a radio timepiece that automatically corrects an error from a standard radio wave received from the communication unit 26, but is not limited thereto.

  The mental state determination unit 24 determines the mental state of the driver of the vehicle 11 using biological information obtained from the biological sensor 21, biological information in a normal state, map data 25 stored in advance, and the like. For example, the mental state determination unit 24 determines whether the mental state is a normal mental state or a different mental state. Examples of normal different mental states include, but are not limited to, an irritated state, a near-missed state, and the like.

  The map data 25 is a road map or the like through which the vehicle 11 passes, but is not limited to this, and the type of road (for example, an expressway, a motorway, a main road, an urban road, an intersection, a T-junction) Etc.), place name, target, resting place, information on refueling place, etc. The map data 25 can be, for example, map data used in a car navigation system or the like, but is not limited to this.

  The communication unit 26 obtains driving state information having mental state information for each predetermined time obtained from the mental state determination unit 24, position information obtained from the GPS 22, and time information obtained from the time unit 23, from the communication network 13. Is transmitted to the driving support device 12 via. The transmitted information includes vehicle identification information or driver identification information. Moreover, the communication part 26 may transmit not only the mental state information mentioned above but each biological information obtained from the biological sensor 21, and may transmit both mental state information and biological information. For example, when the communication unit 26 transmits only biometric information to the driving support device 12, the driving support device 12 determines the mental state of the driver.

  Further, the communication unit 26 outputs driving support information obtained from the driving support device 12 to the information providing unit 27. Note that the driving support information is, for example, information that informs that a nuisance vehicle that is presumed to be a frustrating or disabling factor for the driver is near the traveling point of the vehicle, but is not limited thereto. It is not something. For example, the driving assistance information may be information (for example, during safe driving) notifying that the vehicle is in a normal state.

  The information providing unit 27 displays the driving support information obtained from the communication unit 26 on a display screen or the like, or outputs the information as a voice to provide information to the driver of the vehicle 11, passengers, and the like. The information providing unit 27 is, for example, a car navigation system, but is not limited thereto.

<Example of Functional Configuration of Driving Support Device 12>
Next, a functional configuration example of the driving support device 12 will be described. The driving support device 12 illustrated in FIG. 1 includes a communication unit 31, a driving state database 32 as an example of a storage unit, an annoying vehicle estimation unit 33, an annoying vehicle route estimation unit 34, and a distribution determination unit 35.

  The communication unit 31 receives driving state information (for example, mental state information, position information, time information) and the like for each predetermined time from the vehicles 11-1 to 11-n registered in advance via the communication network 13. Note that the driving state information received by the communication unit 31 from the vehicle 11 may include biological information instead of mental state information, or may include both mental state information and biological information. The communication unit 31 stores the driving state information received from each vehicle 11 in the driving state database 32 together with the vehicle identification information or the driver identification information. In addition, the communication unit 31 transmits driving support information to the distribution target vehicle obtained by the distribution determination unit 35.

  The driving state database 32 includes driving state information (for example, mental state information, position information, time information, etc.) obtained by the communication unit 31 from each of the vehicles 11-1 to 11-n, vehicle identification information or driver identification information. Remember with. The driving state database 32 may be grouped in units of predetermined regions based on position information, or may be grouped and stored in units of vehicles or drivers.

  The nuisance vehicle estimation unit 33 analyzes the data stored in the driving state database 32, and in a predetermined area at a predetermined time, an abnormal mental state such as an irritated state or a near state (hereinafter referred to as "abnormal" It is determined whether a large number of "states" are occurring. Moreover, the nuisance vehicle estimation part 33 estimates that a nuisance vehicle exists, when the generation | occurrence | production number of an abnormal condition is more than the preset threshold value.

  If the data stored in the driving state database 32 is not a mental state but biometric information, the nuisance vehicle estimation unit 33 performs the same processing as the mental state determination unit 24 described above to obtain mental state information. You may get it.

  When the nuisance vehicle estimation unit 33 estimates that there is a possibility that the nuisance vehicle exists, the nuisance vehicle route estimation unit 34 determines the future arrival time to the route or a predetermined point of the nuisance vehicle based on the moving direction. Etc. are estimated.

  The distribution determination unit 35 refers to the driving state database 32 and determines the vehicle 11 that may encounter the nuisance vehicle as the distribution target of the driving support information in correspondence with the nuisance vehicle position and the route. Further, the distribution determination unit 35 causes the communication unit 31 to distribute driving support information including information indicating that the distance to the nuisance vehicle is approaching, for example, to the distribution target vehicle. At this time, the distribution determination unit 35 outputs the vehicle identification information to be distributed and the like obtained from the driving state database 32 to the communication unit 31 together with the driving support information.

  In addition, when the vehicle estimated to be a nuisance vehicle by the nuisance vehicle estimation unit 33 is a vehicle registered in the driving support system 10, the distribution determination unit 35 stops the nuisance driving for the driver of the vehicle. Message (driving support information) may be generated and distributed.

  The configuration of the driving support device 12 is not limited to this, and an input unit that receives an instruction from a user who uses the driving support device 12, setting information, a processing result, and the like in the present embodiment are stored. You may have a memory | storage part, the output part etc. which display various setting information, a processing result, etc.

  For example, if the occurrence position distribution that a driver in a similar abnormal state (for example, annoyance, near-miss, etc.) is moving with time, for example, there is a low-speed driving car, a drowsy driving car due to falling asleep, a runaway car, etc. Is considered to be the cause of frustration. Therefore, in the present embodiment, even when the nuisance vehicle causing the nuisance vehicle is moving, the presence of the nuisance vehicle is notified to drivers of other vehicles existing on a route that may encounter the nuisance vehicle. By doing so, it is possible to give time to take a workaround in advance. Therefore, according to the present embodiment, it is possible to assist the driver with safe driving.

<Hardware configuration example of driving support device 12>
Next, a hardware configuration example of the driving support device 12 will be described with reference to the drawings. FIG. 2 is a diagram illustrating an example of a hardware configuration of the driving support device. In the example of FIG. 2, the driving support device 12 includes an input device 41, an output device 42, a drive device 43, an auxiliary storage device 44, a main storage device 45, a central processing unit (CPU) 46, and a network connection. And a device 47, which are connected to each other by a system bus B.

  The input device 41 has a pointing device such as a keyboard and a mouse operated by a user or the like, and a voice input device such as a microphone, and activates a program execution instruction, various operation information, software, or the like from the user or the like. The input of the information etc. is received.

  The output device 42 includes a display that displays various windows, data, and the like necessary for operating the computer main body (driving support device 12) for performing processing in the present embodiment. The output device 42 can display program execution progress, results, and the like by a control program of the CPU 46. The output device 42 is, for example, the information providing unit 27 described above.

  Here, in the present embodiment, for example, the execution program installed in the computer main body is provided by the recording medium 48 or the like. The recording medium 48 can be set in the drive device 43. Based on the control signal from the CPU 46, the execution program stored in the recording medium 48 is installed in the auxiliary storage device 44 from the recording medium 48 via the drive device 43.

  The auxiliary storage device 44 is a storage means such as a hard disk drive (HDD) or a solid state drive (SSD). The auxiliary storage device 44 stores an execution program (driving support program) in this embodiment, a control program provided in a computer, and the like based on a control signal from the CPU 46, and performs input / output as necessary. The auxiliary storage device 44 can read and write necessary information from each stored information based on a control signal from the CPU 46 and the like.

  The main storage device 45 stores an execution program read from the auxiliary storage device 44 by the CPU 46. The main storage device 45 is a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

  The CPU 46 performs processing of the entire computer, such as various operations and input / output of data with each hardware component, based on a control program such as an operating system (OS) and an execution program stored in the main storage device 45. Control each process. Various information necessary during the execution of the program can be acquired from the auxiliary storage device 44, and the execution results and the like can also be stored.

  Specifically, the CPU 46 executes processing corresponding to the program on the main storage device 45 by executing the program installed in the auxiliary storage device 44 based on, for example, a program execution instruction obtained from the input device 41. Do. For example, the CPU 46 executes the driving support program, thereby determining whether or not there is an annoying vehicle by the annoying vehicle estimation unit 33, estimating the route of the annoying vehicle by the annoying vehicle route estimation unit 34, and determining the distribution by the distribution determination unit 35, Perform the process. The processing content in the CPU 46 is not limited to the above-described content. The contents executed by the CPU 46 are stored in the auxiliary storage device 44 or the like as necessary.

  The network connection device 47 communicates with the vehicle 11 and external devices via the communication network 13 such as the Internet described above. The network connection device 47 acquires an execution program, software, setting information, and the like from an external device or the like by connecting to a communication network or the like based on a control signal from the CPU 46. Further, the network connection device 47 provides the vehicle 11 or an external device with the execution result obtained by executing the program or the execution program itself in the present embodiment.

  The recording medium 48 is a computer-readable recording medium that stores an execution program and the like as described above. The recording medium 48 is a semiconductor memory such as a flash memory, or a portable recording medium such as a CD-ROM or DVD, but is not limited thereto.

  By installing an execution program (for example, a driving support program or the like) in the hardware configuration shown in FIG. 2, the hardware support and software can cooperate to realize the driving support process or the like in the present embodiment.

<Example of processing in driving support system 10>
Next, an example of processing (driving support processing) in the driving support system 10 in the present embodiment will be described with reference to a sequence diagram. FIG. 3 is a diagram illustrating an example of processing of the driving support system. In the example of FIG. 3, a driving support process using the vehicle 11 and the driving support device 12 will be described for convenience of description.

  In the example of FIG. 3, the vehicle 11 acquires biological information by the biological sensor 21, acquires position information from the GPS 22, and acquires time information from the time unit 23 (S01). Next, the mental state determination unit 24 of the vehicle 11 determines the mental state from the biological information acquired by the process of S01 (S02).

  Next, the communication unit 26 of the vehicle 11 sends driving state information including mental state information obtained in the process of S02 and position information and time information acquired in the process of S01 via a communication network. 12 (S03). Note that the above-described processing of S01 to S03 is performed by each of the vehicles 11-1 to 11-n in the driving support system 10 periodically or at a predetermined timing, for example, while the driver is driving.

  The communication unit 31 of the driving support device 12 acquires the driving state information transmitted in the process of S03, and stores the acquired driving state information in the driving state database 32 (S04).

  Next, the nuisance vehicle estimation unit 33 of the driving support device 12 estimates the presence / absence of the nuisance vehicle from the driving state information of each vehicle 11 stored in the driving state database 32 (S05). For example, the nuisance vehicle estimation unit 33 is based on the driving state information of each driver obtained from the plurality of vehicles 11-1 to 11-n. ) Exists, it can be estimated that there is a nuisance vehicle.

  Next, when it is estimated that there is a nuisance vehicle, the nuisance vehicle route estimation unit 34 of the driving support device 12 estimates the route of the nuisance vehicle (S06). If there are multiple nuisance vehicles, the route of each nuisance vehicle is estimated.

  Next, the distribution determination unit 35 of the driving support device 12 selects a vehicle to which the driving support information is distributed from the driving state information stored in the driving state database 32 based on the route information of the annoying vehicle acquired in the process of S06. Judgment is made (S07). For example, the distribution determination unit 35 sets a vehicle that is in the same movement direction along the route of the nuisance vehicle and that exists in a predetermined area (section) and is close to the nuisance vehicle as a distribution target vehicle. However, the present invention is not limited to this. For example, vehicles other than the vehicle group showing the same abnormal state described above are set as distribution target vehicles.

  Next, the distribution determination unit 35 of the driving support apparatus 12 distributes the driving support information corresponding to the distribution target vehicle to the distribution destination vehicle via the communication network 13 (S08).

  The vehicle 11 provides information by displaying a message or the like corresponding to the driving support information obtained by the process of S08 on the screen of the information providing unit 27 or by outputting the voice (S09).

  Note that the driving support system 10 according to the present embodiment does not need to perform the processing after S06 when it is determined that there is no nuisance vehicle in the above-described processing of S05, and there is no nuisance vehicle around. Information to that effect may be presented from the information providing unit 27.

  Moreover, although the process of S01-S09 mentioned above is performed continuously during the driving | running | working of the vehicle 11, etc., you may make it not redistribute the driving assistance information same as the information delivered once to the same vehicle 11, for example. Alternatively, it may not be redistributed until a predetermined time elapses after being distributed once. In the present embodiment, the driving support device 12 may monitor the driving state of the vehicle 11 after distribution for a predetermined time, and redistribute the driving support information if the driving content is not improved. . Thereby, when a delivery target is an annoying vehicle, annoying driving can be improved appropriately.

<Specific examples of biological information>
Next, a specific example of the biological information acquired by the above-described biological sensor 21 will be described. FIG. 4 is a diagram for explaining biological information. The biological information includes, for example, a heart rate, a heartbeat waveform, an α (alpha) wave, a β (beta) wave, a δ (delta) wave, a θ (theta) wave as a brain wave, a body temperature, and the like.

  For example, the heart rate and heart rate waveform data can be acquired by attaching a sensor directly to a body such as a driver's chest, or by attaching the sensor to a wrist like a wristband. In the present embodiment, the biological sensor 21 may be attached to the handle portion of the vehicle 11, and the heart rate, heart rate waveform, and the like may be acquired from the palm of the driver of the vehicle 11. Alternatively, the biometric information may be acquired using radio waves or the like without bringing the sensor into contact with the body, such as putting the biometric sensor 21 into the driver's breast pocket or hanging from the neck. In the example of FIG. 4, biological information such as respiration (analysis 1) and heartbeat data (analysis 2) is acquired by analyzing a waveform (sensor acquisition waveform) acquired from the biological sensor 21, for example.

  In addition, the acquisition of an electroencephalogram can be performed by using, for example, a headgear type covering a head on which a plurality of electrodes for measuring an electroencephalogram are mounted or a headband type electroencephalogram sensor wound around the head. Moreover, body temperature can be acquired by a sensor that is in contact with a part of the body in the same way as heartbeat.

  FIG. 5 is a diagram illustrating a specific example of biological information obtained from a biological sensor. In the example of FIG. 5, the history information of the biological reaction in a certain user is shown. Examples of the history information items include “time information”, “position information”, “biological information”, and the like, but are not limited thereto.

  “Time information” is the time when the biological sensor 21 acquires the biological information, and is information obtained by the time unit 23. “Position information” is the position of the vehicle 11 when the biological sensor 21 acquires biological information, and is information obtained by the GPS 22. The “position information” shown in FIG. 5 is latitude / longitude, but is not limited to this, and may be indicated by preset position coordinates.

  In the “biological information”, data acquired by various biological sensors 21 is stored. The biological information includes, for example, heart rate (Hz), respiration (Hz), body temperature (° C.) and the like, but is not limited thereto. In the example of FIG. 5, the biological information is acquired at intervals of 1 second, but the acquisition time interval is not limited to this.

<Example of mental state determination processing>
Next, an example of mental state determination processing in the above-described mental state determination unit 24 will be described using a flowchart. FIG. 6 is a flowchart illustrating an example of processing in the mental state determination unit. In the example of FIG. 6, the mental state determination unit 24 acquires biological information from the biological sensor 21, positional information from the GPS 22, and time information from the time unit 23. (S11).

  Next, the mental state determination unit 24 determines the road segment of the current position from the current position data and map data stored in advance (S12). The road classification includes, for example, an expressway, an automobile exclusive road, a main road, and an urban road, but is not limited thereto.

  Next, the mental state determination unit 24 determines the travel time zone from the current time (S13). The traveling time zone is a predetermined time zone set corresponding to, for example, early morning, daytime, evening, nighttime, but is not limited thereto.

  Next, the mental state determination unit 24 refers to the biometric information as shown in FIG. 5 described above, and for the predetermined number (N number) from the latest among the data with the same conditions as the current road classification and travel time zone. Is extracted (S14). In addition, the mental state determination unit 24 obtains an average value of N data extracted by the process of S14, for example, and sets it as reference biometric information in a normal state (S15).

  Next, the mental state determination unit 24 determines whether or not the difference between the current (latest) biological information and the reference biological information is equal to or greater than a predetermined threshold (for example, P) (S16). If the difference between the current (latest) biometric information and the reference biometric information is P or more (NO in S16), the mental state determination unit 24 determines that the mental state is the normal state (S17).

Furthermore, mental state determination unit 24, the difference (in S16, YES) the current when the difference between the biometric information and reference biometric information (latest) is not less than P, the last T ₁ seconds, the biological information and the reference biometric information It is determined whether or not has continued for P or more (S18). The above-mentioned T ₁ second is a relatively short time such as 0.5 seconds to 3 seconds, but is not limited thereto.

The mental state determination unit 24 determines that the mental state is a near state (hat state, throbbing state, etc.) if the difference between the biological information and the reference biological information continues for P or more for the past T ₁ second (YES in S18). (S19). If the difference between the biological information and the reference biological information has not continued for P or more for the past T ₁ second (NO in S18), the mental state determination unit 24 determines the biological information and the reference biological information for the past T ₂ seconds. It is determined whether or not the difference of P continues for more than P (S20). The T ₂ seconds described above is a time longer than T ₂ seconds such as 5 to 100 seconds, but is not limited thereto.

Mental state determination unit 24, past T ₂ seconds, (in S20, YES) when the difference between the biometric information and reference biometric information continues over P, and determines the mental condition of the driver and irritated condition (S21). Further, when the difference between the biological information and the reference biological information has not continued for P or more for the past T ₂ seconds (NO in S20), the mental state determination unit 24 does not correspond to the near state or the frustrated state. The driver's mental state is determined as the normal state (S22). For example, when the state different from the reference biometric information (abnormal state) continues for longer than T ₂ are, or have heat the driver, since the contemplated if frustrated for reasons other than operation, if the Treats the mental state as a normal state.

  The determination of the mental state in the present embodiment described above is “normal”, “near”, “irritated”, but is not limited to this. For example, the number of times the mental state changes, the time, etc. Accordingly, the level (level) of the near or frustrated state may be set. For example, if the near-miss state lasts 5 times or more, the near-miss level is set high, or if the frustration state continues for 5 minutes or more, the frustration level is set high, etc. It is not a thing.

  Next, the mental state determination unit 24 transmits driving state information including mental state information, position information, and time information to the driving support device 12 via the communication network 13 (S23). The position information at this time may be the first position or the average position in the time zone of several seconds in which the mental state is determined, and the position when the mental state determination unit 24 determines the mental state of the driver. However, it is not limited to this. Similarly, the time information may be the first time in the time zone of several seconds when the mental state is determined, or may be an average time, and the time when the mental state determination unit 24 determines the driver's mental state. However, it is not limited to this.

<Example of operation state database 32>
Next, an example of the operation state database 32 described above will be described with reference to the drawings. FIG. 7 is a diagram illustrating an example of an operation state database. In the example of FIG. 7, the items of the driving state database 32 include, for example, “vehicle ID”, “time information”, “position information”, “mental state information”, but are not limited thereto. For example, instead of “mental state information”, biological information obtained from the biological sensor 21 may be used.

  “Vehicle ID” is vehicle identification information for identifying the vehicle 11, for example. In the example of FIG. 7, three vehicles are identified by A to C, but the identification information is not limited to this.

  “Time information”, “position information”, and “mental state information” are each information included in the driving state information transmitted from the vehicle 11. In the example of FIG. 7, “position information” is indicated by latitude and longitude, but is not limited thereto.

  “Mental state information” is usually set to any mental state among frustrated and near, but is not limited to this. For example, the degree (level) of frustrated or near is set. Good.

  Although the driving state database 32 shown in the example of FIG. 7 stores driving state information of the vehicles A to C at intervals of 1 second, the stored time interval is not limited to this.

<Processing Example of Nuisance Vehicle Estimation Unit 33>
Next, a processing example of the nuisance vehicle estimation unit 33 will be described using a flowchart. FIG. 8 is a flowchart illustrating an example of processing of the annoying vehicle estimation unit. In the example of FIG. 8, the nuisance vehicle estimation unit 33 determines whether or not the driving state of a certain driver (for example, vehicle ID: A) from the driving state database 32 is an irritated state (S31).

  When the driving state of the driver is in an irritated state (YES in S31), the nuisance vehicle estimating unit 33 refers to the driving state database 32 and simultaneously counts the data in the irritated state around the position (S32). ). In addition, the periphery of the position includes, for example, a vehicle within a radius r (m) centered on the position (latitude / longitude) of the vehicle 11 driven by the driver in an irritated state, or the position of the vehicle 11. Although it is within a rectangular area of length N (m) × width M (m), it is not limited to this. In the process of S32, for example, not only the data in the vicinity but also the data of the frustrated state with respect to the vehicle having the same moving direction may be counted. Further, for example, the speed for each vehicle may be calculated based on time information and position information included in the driving state database 32, and the data of the frustrated state may be counted based on the calculated speed information.

  The nuisance vehicle estimation unit 33 determines whether or not the count number of the frustrated state is equal to or greater than a preset threshold value (for example, K) (S33). In the process of S33, for example, it is determined whether or not there is a vehicle group (a group of vehicles in a certain region (range)) in which the driver is in a near state. Note that K is, for example, three or more, but is not limited thereto.

  If the count number is not equal to or greater than K (NO in S33), nuisance vehicle estimation unit 33 returns to S31 and performs subsequent processing on the obtained driving state information. Further, when the count number is K or more (YES in S33), the nuisance vehicle estimation unit 33 acquires information on traffic accidents and road construction points from an organization that distributes road information such as a road traffic information communication center. (S34). Note that the information that can be acquired in S34 is not limited to traffic accidents and road construction, but may be information on large falling objects on the road, accident vehicles, parked vehicles, and the like.

  Next, the nuisance vehicle estimation unit 33 determines whether or not there is a traffic accident or road construction in the applicable range (S35). If there is a traffic accident or road construction or the like (YES in S35). Then, it is determined that there is no nuisance vehicle, and the process returns to S31. In addition, when there is no traffic accident or road construction or the like (NO in S35), the nuisance vehicle estimation unit 33 estimates the presence of the nuisance vehicle from position information, time information, etc. in an irritated state in the corresponding range (S36). .

  Further, if the driving state is not an irritated state in S31 (NO in S31), the nuisance vehicle estimation unit 33 determines whether the driving state is a near-miss state (S37). If the driving state is not a near-missing state (NO in S37), the processing returns to S31, and the subsequent processing is performed on the driving state information obtained next.

  Further, when the driving state is the near state (YES in S37), the nuisance vehicle estimation unit 33 refers to the driving state database 32 and similarly counts the data in the near state around the position (S38). ).

  Next, the nuisance vehicle estimation unit 33 determines whether or not the number of near-miss counts is a predetermined threshold (for example, J or more) (S39). In the process of S39, for example, it is determined whether there is a vehicle group in which the driver is in a near-miss state. In addition, although J is 3 or more units, for example, it is not limited to this.

  If the count number is not equal to or greater than J (NO in S39), nuisance vehicle estimation unit 33 returns to the process of S31. Moreover, when the number of counts is J or more (YES in S39), the nuisance vehicle estimation unit 33 acquires position information of an ambulance vehicle such as an ambulance or a police vehicle from a communication command center or the like (S40). It is determined whether or not an emergency vehicle exists in the range (S41).

  When the emergency vehicle is present in the corresponding range (YES in S41), the nuisance vehicle estimation unit 33 determines that there is no nuisance vehicle and returns to the process of S31. Further, when there is no emergency vehicle in the corresponding range (NO in S41), the nuisance vehicle estimation unit 33 estimates the presence of the nuisance vehicle from the position information, time information, and the like of the near range in the corresponding range (S42). .

  In the above-described nuisance vehicle estimation process, the nuisance vehicle estimation unit 33 does not perform the processes of S34 and S35 but performs the process of S34 and S35 when the count of the irritated state is K or more as shown in S36. It may be estimated that a nuisance vehicle exists. In addition, in the process of S39, the nuisance vehicle estimation unit 33 estimates that a nuisance vehicle exists as shown in S42 without performing the processes of S40 and S41 when the number of near-miss counts is J or more. Also good.

  In the processes of S34 and S40, various external information centers referred to include, for example, a communication command center, a road traffic information communication center, a municipal information center, and a private information center, but are not limited thereto. It is not something.

<Annoying vehicle route estimation and delivery judgment>
Next, annoying vehicle route estimation and distribution determination when the above-described annoying vehicle estimation unit 33 determines that annoying vehicles are present will be described.

  FIG. 9 is a diagram for explaining a driving situation of the vehicle. FIG. 9A shows an example of propagation in an irritated state by a low speed vehicle, and FIG. 9B shows an example of propagation in a near state by a dangerous vehicle. The examples of FIGS. 9A and 9B show a two-lane highway on one side. Further, the low-speed vehicle 51 in FIG. 9A and the dangerous vehicle (for example, runaway vehicle, overtaking vehicle, etc.) 52 in FIG. 9B are examples of the above-described troublesome vehicle.

  In the example of FIGS. 9A and 9B, when the annoying vehicle estimation unit 33 estimates that an annoying vehicle exists, the annoying vehicle route estimation unit 34 determines the position of the frustrated state or the near state in the corresponding range. Based on the time and the like, the movement path of the nuisance vehicle is estimated from the time flow of the mental states of the vehicles 11-1 to 11-6. In addition, the distribution determination unit 35 estimates a vehicle that will be in an irritated state (sometimes) in a near time zone, and transmits driving support information such as alert information.

  For example, in the example of FIG. 9A, the vehicles 11-1 to 11-3 are in an irritated state due to the influence of the low-speed vehicle 51. Accordingly, the distribution determination unit 35 determines the vehicle group (vehicles 11-1 to 11-3) from information such as the future movement route (direction) of the low-speed vehicle 51 and the movement speed of each vehicle by the annoying vehicle route estimation unit 34. A vehicle 11-6 that may possibly reach the low-speed vehicle 51 with a vehicle other than the vehicle 11-6 is determined. The distribution determination unit 35 distributes corresponding driving support information to the vehicle 11-6. The driving support information in this case is, for example, “Please note that a low-speed vehicle is traveling ahead”, but is not limited thereto.

  In the example of FIG. 9B, the vehicles 11-3 to 11-6 are in a near-miss state. The distribution determination unit 35 determines whether the vehicles 11-3 to 11-6 are in a near-miss state, information about the future moving route of the dangerous vehicle 52, the moving speed of each vehicle, and other vehicles other than the vehicle group. Thus, the vehicles 11-1 and 11-2 that the dangerous vehicle 52 may reach are determined. In addition, the distribution determination unit 35 distributes corresponding driving support information to the vehicles 11-1 and 11-2. The driving support information in this case is, for example, “Please note that a runaway vehicle is approaching from behind”, but is not limited thereto.

  Thereby, since the vehicle can receive driving assistance information and can know the approach and presence of an annoying vehicle by a display or an audio | voice by the information provision part 27, it can drive appropriately.

<About annoying vehicle estimation for frustrated conditions>
As described above, in the present embodiment, a point where the frustrated state frequently occurs in a certain time zone is extracted from the driving state information (driving state database 32) of each vehicle 11 collected in the driving support device 12. At this time, the cause of frequent frustrations may be annoying vehicles such as low-speed driving that hinder traffic flow, and traffic jams caused by parked vehicles, road construction, falling objects, road damage, etc. .

  Therefore, it is necessary to distinguish between an annoying vehicle such as a low-speed driving and a traffic jam that occurs from a certain point. This can be determined by whether or not the location of the frustration occurrence point moves with time. When the cause of the frustration is due to the low-speed driving vehicle, the frustration occurrence point also moves as the low-speed driving vehicle moves. On the other hand, when it is caused by a parked vehicle, road construction, or a fallen object, there is no movement of the frustrating point. In addition, the individual driver's frustration may result from daily life. Since this is not directly related to driving, it does not depend on time or position.

  Therefore, in this embodiment, as described above, since the mental states of a plurality of drivers in the same range at the same time are aggregated, there are a small number of mental states that are not caused by driving. Unaffected by the result of the estimation process. Thereby, estimation of an appropriate junk vehicle can be performed.

<Annoying vehicle estimation for near-miss conditions>
As described above, in the present embodiment, points where the near state frequently occurs in a certain time zone are extracted from the driving state information (driving state database 32) of each vehicle 11 collected in the driving support device 12. At this time, there is a possibility that the cause of the frequent occurrence of the near state is an emergency vehicle such as an ambulance or a police vehicle, as well as an annoying vehicle such as a meandering operation or an abnormal approaching operation.

  Therefore, it is necessary to distinguish between annoying vehicles and emergency vehicles. The location information of the emergency vehicle is obtained by, for example, the communication command center, and as described above, the location of the emergency vehicle is obtained at the center at any time and the location of the emergency vehicle is confirmed. An appropriate nuisance vehicle can be estimated by distinguishing it from an emergency vehicle.

  If the nuisance vehicle is a vehicle registered in the driving support system 10, the nuisance vehicle can be estimated by comparing the speed with other surrounding vehicles. For example, using the speed information calculated from the position information and time information in the driving state database 32, the first vehicle in the surrounding vehicle, the first vehicle in the low-speed vehicle group, or the low-speed vehicle group A vehicle that is not in an irritated state can be estimated as a nuisance vehicle. Further, for example, a vehicle overtaking another vehicle at a higher speed than other surrounding vehicles can be estimated as a nuisance vehicle. Therefore, the driving assistance device 12 can transmit driving assistance information indicating that the annoying driving is stopped to the estimated annoying vehicle.

<Example of route estimation in nuisance vehicle route estimation unit 34>
An example of route estimation in the nuisance vehicle route estimation unit 34 will be described with reference to the drawings. FIG. 10 is a diagram illustrating an example of route estimation in the nuisance vehicle route estimation unit.

  The nuisance vehicle route estimation unit 34 can associate the position of the vehicle 11 and the route information thereof with reference to the position information using the driving state information and the map data 25 similar to the map data 25 of the vehicle 11, for example. Further, the nuisance vehicle route estimation unit 34 can estimate the traveling direction of the nuisance vehicle based on the time information.

  Note that the information illustrated in FIG. 10 may be provided to each vehicle around the estimated position of the nuisance vehicle, for example, as real-time map information on a near-miss corresponding to each vehicle.

  As described above, according to the present embodiment, it is possible to assist the driver with safe driving. In the present embodiment, the presence of a nuisance vehicle is estimated by combining the biological information of the driver of the vehicle 11 with a state different from the normal time, and combining the vehicle position information and counting the data with the driving support device 12. Can do. Therefore, if there is no abnormal operation of the vehicle, such as a sudden handle or a sudden brake, but the driver's condition appears in the surrounding vehicle as well, It can be estimated that the cause is annoying vehicle. Thereby, this embodiment can perform safe driving support by alerting a nuisance vehicle and providing information to the driver of the surrounding vehicle.

  For example, if the position distribution of drivers (cars) with similar mental states (irritated, near-missing) moves with time, for example, the existence of low-speed vehicles, drowsy hovering vehicles, runaway vehicles, etc. may cause irritation. It is thought that. Therefore, in the present embodiment, even when the cause vehicle is moving, it is possible to notify the other driver existing on the route that may encounter the cause vehicle, and to avoid the cause in advance. Can afford to take time.

  Although the embodiments have been described in detail above, the invention is not limited to the specific embodiments, and various modifications and changes can be made within the scope described in the claims. Moreover, it is also possible to combine a part or all of each Example mentioned above.

In addition, the following additional remarks are disclosed regarding the above Example.
(Appendix 1)
Driving assistance device
Biometric information detected for each driver of a plurality of vehicles, position information and time information where the biometric information is detected,
Based on the obtained plurality of position information, time information, and biological information, estimate the moving direction of the vehicle group indicating the predetermined biological information,
A driving support method, comprising: transmitting information corresponding to the predetermined biological information to vehicles other than the vehicle group in the estimated moving direction.
(Appendix 2)
The driving support method according to appendix 1, wherein the predetermined biological information is an irritated state or a near-missed state.
(Appendix 3)
It is determined whether or not the predetermined biological information is caused by an annoying vehicle, and when the predetermined biological information is obtained by the annoying vehicle, information corresponding to the predetermined biological information is transmitted. The driving support method according to appendix 1 or 2.
(Appendix 4)
A vehicle that estimates a future movement route of the nuisance vehicle and transmits information corresponding to the predetermined biological information based on the movement direction corresponding to the estimated movement route and the movement direction of the vehicle group. The driving support method according to supplementary note 3, wherein the determination is made.
(Appendix 5)
The driving support method according to appendix 3 or 4, wherein information indicating that the annoying driving is stopped is transmitted to the vehicle estimated to be the annoying vehicle.
(Appendix 6)
Based on the biometric information detected for each driver of the plurality of vehicles, the position information and the time information where the biometric information is detected, and based on the obtained plurality of the position information, time information, and biometric information, An annoying vehicle estimation unit for estimating the presence or absence of annoying vehicles;
When the nuisance vehicle estimation unit determines that the nuisance vehicle is present, the movement direction of the vehicle group indicating predetermined biological information is estimated, and for vehicles other than the vehicle group in the estimated movement direction, A driving support apparatus comprising: a distribution determination unit that transmits information according to predetermined biological information.
(Appendix 7)
Biometric information detected for each driver of a plurality of vehicles, position information and time information where the biometric information is detected,
Based on the obtained plurality of position information, time information, and biological information, estimate the moving direction of the vehicle group indicating the predetermined biological information,
A driving support program for causing a computer to execute processing for transmitting information corresponding to the predetermined biological information to vehicles other than the vehicle group in the estimated moving direction.

DESCRIPTION OF SYMBOLS 10 Driving assistance system 11 Vehicle 12 Driving assistance apparatus 13 Communication network 21 Biosensor (biological information acquisition part)
22 GPS (location information acquisition unit)
23 Time unit 24 Mental state determination unit 25 Map data 26, 31 Communication unit 27 Information providing unit 32 Driving state database 33 Annoying vehicle estimation unit 34 Annoying vehicle route estimation unit 35 Distribution determination unit 41 Input device 42 Output device 43 Drive device 44 Auxiliary Storage device 45 Main storage device 46 CPU
47 Network connection device 48 Recording medium 51 Low-speed vehicle 52 Dangerous vehicle

Claims (6)

Driving assistance device
Biometric information detected for each driver of a plurality of vehicles, position information and time information where the biometric information is detected,
Based on the obtained plurality of position information, time information, and biological information, estimate the moving direction of the vehicle group indicating the predetermined biological information,
A driving support method, comprising: transmitting information corresponding to the predetermined biological information to vehicles other than the vehicle group in the estimated moving direction.   The driving support method according to claim 1, wherein the predetermined biological information is an irritated state or a near-missed state.   It is determined whether or not the predetermined biological information is caused by an annoying vehicle, and when the predetermined biological information is obtained by the annoying vehicle, information corresponding to the predetermined biological information is transmitted. The driving support method according to claim 1 or 2.   A vehicle that estimates a future movement route of the nuisance vehicle and transmits information corresponding to the predetermined biological information based on the movement direction corresponding to the estimated movement route and the movement direction of the vehicle group. The driving support method according to claim 3, wherein a determination is made. Based on the biometric information detected for each driver of the plurality of vehicles, the position information and the time information where the biometric information is detected, and based on the obtained plurality of the position information, time information, and biometric information, An annoying vehicle estimation unit for estimating the presence or absence of annoying vehicles;
When the nuisance vehicle estimation unit determines that the nuisance vehicle is present, the movement direction of the vehicle group indicating predetermined biological information is estimated, and for vehicles other than the vehicle group in the estimated movement direction, A driving support apparatus comprising: a distribution determination unit that transmits information according to predetermined biological information. Biometric information detected for each driver of a plurality of vehicles, position information and time information where the biometric information is detected,
Based on the obtained plurality of position information, time information, and biological information, estimate the moving direction of the vehicle group indicating the predetermined biological information,
A driving support program for causing a computer to execute processing for transmitting information corresponding to the predetermined biological information to vehicles other than the vehicle group in the estimated moving direction.

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