System, vehicle, mobile terminal, and methods thereof

Abstract

To appropriately predict and report a risk level of accidence occurrence based on an environment around a traveling vehicle, and appropriately predict and report the risk level in accordance with driving characteristics of a driver of the vehicle.SOLUTION: A system comprises: acquisition means (111 and 211) for acquiring first environment information relating to an environment around a vehicle traveling at present; prediction means (112 and 212) for predicting a risk level of accident occurrence by comparing the first environment information acquired by the acquisition means with second environment information which is environment information in past accident occurrence; and report means (113 and 213) for reporting a result predicted by the prediction means to a driver of the vehicle.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a system, a vehicle, a mobile terminal, and a method thereof for alerting a driver of a vehicle in motion to a danger. [Background technology]

[0002] There are known technologies that identify behaviors that may cause vehicle accidents and support safe driving. For example, Patent Document 1 discloses a technology that acquires characteristic information indicating the characteristics of the road on which a vehicle is traveling based on current location information, and identifies driving behaviors that may cause accidents based on the road characteristics indicated by the acquired characteristic information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-35387 Summary of the Invention [Problem to be solved by the invention]

[0004] The above-mentioned conventional technology identifies driving behaviors that may cause accidents based on the road characteristics of the current location, and can only predict accidents at points where accidents are likely to occur. On the other hand, the risk of accidents varies depending on the surrounding environmental information and vehicle movement while driving, so if accidents can only be predicted at points where accidents are likely to occur, the range of predictions is quite limited. Therefore, such predictions cannot provide drivers with more appropriate information to prevent accidents.

[0005] An object of the present invention is to appropriately predict and notify the risk of an accident based on the environment around a traveling vehicle. Another object is to appropriately predict and notify the risk in accordance with the driving characteristics of the vehicle driver. [Means for solving the problem]

[0006] According to the present invention, there is provided a system comprising: a sensor for acquiring a state of the vehicle; and an identification means for identifying a driving characteristic of a driver relating to the behavior of the vehicle based on state information of the vehicle during traveling acquired and accumulated by the sensor for acquiring the state of the vehicle; an acquisition means for acquiring first environmental information relating to an environment surrounding a vehicle currently traveling; and and a matching rate indicating the rate at which corresponding parameters match when comparing a plurality of parameters included in the second environmental information, which is environmental information included in accident data at the time of a past accident, and predicting the risk of an accident from the matching rate, and adjusting the risk of an accident when the driving characteristics of the driver identified by the identifying means are related to the cause of the past accident. The vehicle is characterized by comprising a prediction means and a notification means for notifying the driver of the vehicle of the prediction result by the prediction means.

[0007] Further, according to the present invention, a first acquisition means, which is a mobile terminal, for acquiring, from a server, driving characteristics of a driver related to a behavior of the vehicle identified based on state information of the vehicle during travel that is acquired and accumulated by a sensor that acquires the state of the vehicle; Acquire first environmental information regarding the environment around the currently traveling vehicle. No. 2 acquisition means; No. 2 The first environmental information acquired by the acquisition means and a matching rate indicating the rate at which corresponding parameters match when comparing a plurality of parameters included in the first environmental information, which is environmental information included in accident data at the time of a past accident, and the risk of an accident occurring can be predicted from the matching rate, which indicates the rate at which corresponding parameters match when comparing a plurality of parameters included in the second environmental information, which is environmental information included in accident data at the time of a past accident, and the risk of an accident occurring can be adjusted when the driving characteristics of the driver acquired by the first acquisition means are related to the cause of the past accident. The vehicle is characterized by comprising a prediction means and a notification means for notifying the driver of the vehicle of the prediction result by the prediction means.

[0008] Further, according to the present invention, a vehicle, comprising: a sensor for acquiring a state of the vehicle; and an identification means for identifying a driving characteristic of a driver relating to a behavior of the vehicle based on state information of the vehicle while it is traveling that is acquired and accumulated by the sensor for acquiring the state of the vehicle; an acquisition means for acquiring first environmental information relating to an environment around the vehicle currently traveling; and and a matching rate indicating the rate at which corresponding parameters match when comparing a plurality of parameters included in the second environmental information, which is environmental information included in accident data at the time of a past accident, and predicting the risk of an accident from the matching rate, and adjusting the risk of an accident when the driving characteristics of the driver identified by the identifying means are related to the cause of the past accident. The vehicle is characterized by comprising a prediction means and a notification means for notifying the driver of the vehicle of the prediction result by the prediction means. [Effects of the Invention]

[0009] According to the present invention, it is possible to appropriately predict and notify the risk of an accident based on the environment around a traveling vehicle. Furthermore, it is possible to appropriately predict the risk in accordance with the driving characteristics of the vehicle driver. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a right side view of a saddle-ride type vehicle according to an embodiment. [Figure 2] FIG. 2 is a front view of the saddle-type vehicle of FIG. 1. [Figure 3] FIG. 2 is a block diagram showing the control configuration of the present system according to an embodiment. [Figure 4]FIG. 10 is a diagram showing a control configuration for predicting a risk level according to an embodiment. [Figure 5] FIG. 4 is a diagram illustrating a comparison between accident data and surrounding environment information according to an embodiment. [Figure 6] 1 is a flowchart showing a basic flow of predicting a risk level according to one embodiment. [Figure 7] 4 is a flowchart showing a notification and vehicle control processing procedure according to an embodiment. [Figure 8] 4 is a flowchart showing a processing procedure for generating a model of a driver's driving characteristics according to an embodiment. [Figure 9] FIG. 2 is a block diagram showing the control configuration of the present system according to an embodiment. [Figure 10] FIG. 10 is a diagram showing a control configuration for predicting a risk level according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention as claimed, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be combined in any desired manner. Furthermore, the same reference numerals are used to designate identical or similar components, and redundant descriptions will be omitted.

[0012] In each diagram, arrows X, Y, and Z indicate directions that are perpendicular to one another, with the X direction indicating the front-to-rear direction of the saddle-riding vehicle, the Y direction indicating the width direction (left-to-right direction) of the saddle-riding vehicle, and the Z direction indicating the up-to-down direction. The left and right of the saddle-riding vehicle are the left and right when viewed in the forward direction. Hereinafter, the front or rear in the front-to-rear direction of the saddle-riding vehicle may be simply referred to as the front or rear. Furthermore, the inside or outside in the width direction (left-to-right direction) of the saddle-riding vehicle may be simply referred to as the inside or outside.

[0013] First Embodiment <Overview of saddle-type vehicle> A first embodiment of the present invention will be described below. Fig. 1 is a right side view of a saddle-ride type vehicle 100 according to one embodiment of the present invention, and Fig. 2 is a front view of the saddle-ride type vehicle 100.

[0014] The saddle-type vehicle 100 is a touring motorcycle suitable for long-distance travel, but the present invention is applicable to various types of saddle-type vehicles, including other types of motorcycles, and is also applicable to vehicles powered by internal combustion engines as well as electric vehicles powered by motors. Hereinafter, the saddle-type vehicle 100 may be referred to as the vehicle 100. Furthermore, in this embodiment, a two-wheeled saddle-type vehicle will be described as an example of a vehicle, but this is not intended to limit the present invention, and the present invention can be applied to various types of vehicles, such as four-wheel drive vehicles.

[0015] The vehicle 100 is provided with a power unit 2 between the front wheels FW and the rear wheels RW. In this embodiment, the power unit 2 includes a horizontally opposed six-cylinder engine 21 and a transmission 22. The driving force of the transmission 22 is transmitted to the rear wheels RW via a drive shaft (not shown), thereby rotating the rear wheels RW.

[0016] The power unit 2 is supported by a body frame 3. The body frame 3 includes a pair of left and right main frames 31 extending in the X direction. A fuel tank 5 and an air cleaner box (not shown) are disposed above the main frames 31. In front of the fuel tank 5, there is provided a meter panel MP equipped with an electronic image display device and the like that displays various information to the rider, who is the driver.

[0017] A head pipe 32 is provided at the front end of the main frame 31, rotatably supporting a steering shaft (not shown) that is turned by the handlebars 8. A pair of left and right pivot plates 33 is provided at the rear end of the main frame 31. The lower ends of the pivot plates 33 are connected to the front end of the main frame 31 by a pair of left and right lower arms (not shown), and the power unit 2 is supported by the main frame 31 and the lower arms. A pair of left and right seat rails (not shown) extending rearward is also provided at the rear end of the main frame 31, and the seat rails support a seat 4a on which a rider sits, a seat 4b on which a passenger sits, a rear trunk 7b, etc.

[0018] The front end of a rear swing arm (not shown) extending in the fore-and-aft direction is supported so as to be able to swing freely on the pivot plate 33. The rear swing arm is able to swing up and down, and a rear wheel RW is supported at its rear end. An exhaust muffler 6 that silences exhaust from the engine 21 is provided extending in the X direction on the sides of the lower part of the rear wheel RW. Left and right saddlebags 7a are provided on the sides of the upper part of the rear wheel RW.

[0019] A front suspension mechanism 9 that supports a front wheel FW is configured at the front end of the main frame 31. The front suspension mechanism 9 includes an upper link 91, a lower link 92, a fork support 93, a cushion unit 94, and a pair of left and right front forks 95.

[0020] The upper link 91 and the lower link 92 are disposed at a vertical interval at the front end of the main frame 31. The rear ends of the upper link 91 and the lower link 92 are pivotally connected to supports 31a and 31b provided at the front end of the main frame 31. The front ends of the upper link 91 and the lower link 92 are pivotally connected to a fork support 93. The upper link 91 and the lower link 92 each extend in the fore-and-aft direction and are disposed substantially parallel to each other.

[0021] The cushion unit 94 has a structure in which a shock absorber is inserted into a coil spring, and its upper end is supported so as to be able to swing freely by the main frame 31. The lower end of the cushion unit 94 is supported so as to be able to swing freely by the lower link 92.

[0022] The fork support 93 is cylindrical and tilted backward. The front end of an upper link 91 is rotatably connected to the front upper part of the fork support 93. The front end of a lower link 92 is rotatably connected to the rear lower part of the fork support 93.

[0023] A steering shaft 96 is supported by the fork support 93 so as to be rotatable about its axis. The steering shaft 96 has a shaft portion (not shown) that passes through the fork support 93. A bridge (not shown) is provided at the lower end of the steering shaft 96, and a pair of left and right front forks 95 are supported on this bridge. A front wheel FW is rotatably supported by the front forks 95. An upper end of the steering shaft 96 is connected via a link 97 to a steering shaft (not shown) that is rotated by the handlebars 8. When the handlebars 8 are steered, the steering shaft 96 rotates, and the front wheel FW is steered.

[0024] The vehicle 100 is equipped with a braking device 19F that brakes the front wheel FW and a braking device 19R that brakes the rear wheel RW. The braking devices 19F, 19R are configured to be operable by the rider's operation of the brake lever 8a or the brake pedal 8b. The braking devices 19F, 19R are, for example, disc brakes. When there is no need to distinguish between the braking devices 19F, 19R, they are collectively referred to as the braking devices 19.

[0025] A headlight 11 that emits light ahead of the vehicle 100 is disposed at the front of the vehicle 100. The headlight 11 of this embodiment is a twin-lens headlight unit that includes a right light emitting portion 11R and a left light emitting portion 11L that are symmetrical on the left and right. However, a single-lens or triple-lens headlight unit, or an asymmetric twin-lens headlight unit can also be used.

[0026] The front of the vehicle 100 is covered by a front cowl 12, and the front sides of the vehicle 100 are covered by a pair of left and right side cowls 14. A screen 13 is disposed above the front cowl 12. The screen 13 is a windshield that reduces wind pressure that the rider experiences while riding, and is formed, for example, from a transparent resin member.

[0027] A pair of left and right side mirror units 15 are disposed on the sides of the front cowl 12. The side mirror units 15 support side mirrors (not shown) that allow the rider to view the rear.

[0028] In this embodiment, the front cowl 12 is made up of cowl members 121 to 123. The cowl member 121 extends in the Y direction and constitutes the main body of the front cowl 12, and the cowl member 122 constitutes the upper part of the cowl member 121. The cowl member 123 is disposed spaced apart from the cowl member 121 in the downward direction.

[0029] An opening for exposing the headlight 11 is formed between the cowl member 121 and the cowl member 123 and between the pair of left and right side cowls 14, with the upper edge of this opening being defined by the cowl member 121, the lower edge being defined by the cowl member 123, and the left and right side edges being defined by the side cowls 14.

[0030] An imaging unit 16A and a radar unit 16B are disposed behind the front cowl 12 as detection devices that detect the situation ahead of the vehicle 100. The radar unit 16B is, for example, a millimeter-wave radar. The imaging unit 16A includes an imaging element such as a CCD image sensor or a CMOS image sensor, and an optical system such as a lens, and captures an image ahead of the vehicle 100. The imaging unit 16A is disposed behind a cowl member 122 that constitutes the upper part of the front cowl 12. An opening 122a is formed through the cowl member 122, and the imaging unit 16A captures an image ahead of the vehicle 100 through the opening 122a.

[0031] The radar unit 16B is disposed behind the cowl member 121. The presence of the cowl member 121 makes the presence of the detection unit (external environment monitoring device) 16 less noticeable when viewed from the front of the vehicle 100, thereby preventing the appearance of the vehicle 100 from being impaired. The cowl member 121 is made of a material that is permeable to electromagnetic waves, such as resin.

[0032] The imaging unit 16A and the radar unit 16B are disposed in the center of the front cowl 12 in the Y direction when viewed from the front of the vehicle. By disposing the imaging unit 16A and the radar unit 16B in the center of the vehicle 100 in the Y direction, wider imaging ranges and detection ranges can be obtained on the left and right sides of the front of the vehicle 100, making it possible to detect conditions ahead of the vehicle 100 more accurately. Furthermore, since one imaging unit 16A and one radar unit 16B can monitor the front of the vehicle 100 evenly on the left and right sides, it is particularly advantageous to provide only one imaging unit 16A and one radar unit 16B rather than multiple imaging units 16A and radar units 16B.

[0033] <System control configuration> 3 is a block diagram showing the control configuration of the risk prediction system according to this embodiment, and only the configuration necessary for the explanation that follows is shown. This system includes a vehicle 100, a mobile terminal 200, and a server group 300. The mobile terminal 200 is a device owned by the driver who drives the vehicle 100. The server group 300 includes an accident data accumulation server 301, a driving characteristic server 302, and an environmental information server 303. Of course, the server group 300 may also include servers with other functions.

[0034] The vehicle 100 includes a control unit (ECU) 101, a memory unit 102, a clock 103, an external environment monitoring device 104, a notification device 105, an external communication device 106, and an on-board sensor 107. The control unit 101 includes an information acquisition unit 111, a risk prediction unit, and a risk notification unit 113, which execute processes required to implement the risk prediction system. While these processing units are indicated by dotted lines in FIG. 3 , this indicates that these processing units are not essential components of the vehicle 100, as these processes can also be executed by the mobile terminal 200. In other words, these processing units may be provided in at least one of the vehicle 100 and the mobile terminal 200, or the control units of both may cooperate to perform the processing. The external environment monitoring device 104 includes, for example, a radar 141, a laser 142, a camera 143, and a drive recorder 144. The on-board sensor 107 includes an acceleration sensor 171, an angular velocity sensor 172, a wheel speed sensor 173, and a brake pressure sensor 174. The notification device 105 includes a speaker 151 and a meter display 152. The external communication device 106 includes, for example, a GNSS 161, a short-range wireless device 162, and a wireless communication device 163. Of course, each component may include other configurations.

[0035] The control unit 101 includes a processor such as a CPU. The storage unit 102 stores programs executed by the processor, data used by the processor for processing, and the like. The storage unit 102 may be incorporated inside the control unit 101. The control unit 101 is connected to other components 102 to 107 via signal lines such as a bus, can send and receive signals, and controls the entire vehicle 100.

[0036] For example, the control unit 101 acquires the detection results of the radar unit 16B of the external environment monitoring device 104, and is able to constantly recognize targets and road conditions around the vehicle 100 and detect objects approaching the vehicle 100. The control unit 101 can also use the imaging unit 16A to capture and record the road surface position, road conditions, approaching objects, and the like while the vehicle is traveling. The control unit 101 can store image data captured by the imaging unit 16A in the storage unit 102 and transmit the image data to an external device using the external communication device 106. The control unit 101 also acquires various types of information via the GNSS 161, the short-range wireless device 162, and the wireless communication device 163. The GNSS 161 acquires the current position of the vehicle 100. The short-range wireless device 162 transmits and receives various types of data to and from a mobile terminal 200 owned by the driver via short-range wireless communication. The short-range wireless communication may be any communication method that allows communication within a predetermined range, such as wireless LAN (Wi-Fi), Bluetooth, NFC, or infrared communication. The communication range may be set, for example, as an area within a radius of 1 to 3 meters from the vehicle 100. This is intended to limit communication to a mobile terminal owned by the driver while the vehicle 100 is traveling, since environmental information about the vehicle's surroundings can also be obtained by the mobile terminal 200. The control unit 101 also communicates with various servers 301 to 303 of the server group 300 via a broadband wireless communication device 163, and transmits and receives various information. The vehicle 100 may obtain information from the server group 300 via the mobile terminal 200, in which case the vehicle 100 need not be equipped with the wireless communication device 163.

[0037] As described above, the control unit 101 may include the information acquisition unit 111, the risk prediction unit 112, and the risk notification unit 113. The information acquisition unit 111 acquires various information necessary for predicting the risk of an accident involving a traveling vehicle. This information includes surrounding environmental information while traveling, acquired from the external monitoring device 104 and the on-board sensor 107 of the vehicle 100, sensor information indicating the behavior of the vehicle 100, i.e., the driving characteristics of the driver, and time information from the clock 103. Furthermore, this information includes accident data, which are acquired from the server group 300 via the external communication device 106, such as regional information and weather information, which are environmental information, and model information indicating driving characteristics.

[0038] The risk prediction unit 112 compares the surrounding environmental information (first environmental information) acquired by the information acquisition unit 111 during driving with environmental information at the time of the accident (second environmental information) included in the past accident data, to predict the risk of a current accident. The risk prediction unit 112 can also weight the comparison result based on the driver's driving characteristics to predict the risk. Here, the driver's driving characteristics are obtained by accumulating sensor information acquired in the past from various sensors of the on-board sensor 107 and identifying the behavior of the vehicle 100 from the accumulated information. The accumulation of data and the identification of the driving characteristics from the accumulated data may be performed by the driving characteristic server 302. In this case, the driving characteristic server 302 generates a model indicating the driving characteristics for each driver and notifies the vehicle 100 at an arbitrary timing. The arbitrary timing may include timing when the model is updated, periodic timing, timing instructed by the user, etc.

[0039] The danger notification unit 113 notifies the driver of a danger via the notification device 105 according to the danger level predicted by the danger prediction unit 112. For example, the danger notification unit 113 may output an alarm sound or voice according to the danger level from the speaker 151, or may output a display (indicator) according to the danger level from the meter display 152. Alternatively, or additionally or alternatively, the danger notification unit 113 may output a display or an alarm sound according to the danger level using a display or speaker of the mobile terminal 200, or may notify other devices communicatively connected to the vehicle 100. Details of the danger level will be described later, but notifications may be made according to, for example, high danger level, medium danger level, zero danger level, etc. Note that if the danger level is zero, no particular notification may be made. Furthermore, the danger notification unit 113 may restrict the operation of the vehicle 100, for example, by slowing down the vehicle 100 while it is moving, according to the danger level.

[0040] Next, the configuration of the mobile terminal 200 will be described. The mobile terminal 200 is a device such as a smartphone, mobile phone, tablet terminal, or wearable terminal owned by the driver of the vehicle 100. The mobile terminal 200 includes a control unit 201, a storage unit 202, an external communication device 203, and an imaging unit 204. The external communication device 203 includes a GNSS 205, a short-range wireless device 206, and a wireless communication device 207. The imaging unit 204 includes a camera 208 and a drive recorder 209.

[0041] The control unit 201 includes a processor such as a CPU. The storage unit 202 stores programs executed by the processor, data used by the processor for processing, and the like. The storage unit 202 may be incorporated inside the control unit 201. The control unit 201 is connected to other components 203 and 204 via signal lines such as a bus, can send and receive signals, and controls the entire mobile terminal 200.

[0042] The control unit 201 can communicate with the vehicle 100 and the server group 300 via the short-range wireless device 206 and the wireless communication device 207 to send and receive information. The control unit 201 can also acquire location information of the mobile terminal 200 via the GNSS 205. Furthermore, image data captured by the imaging unit 204 can be stored in the storage unit 202 and can also be transmitted to the vehicle 100 and the server group 300 via the external communication device 203. The short-range wireless communication can be any communication method that allows communication within a predetermined range, such as wireless LAN (Wi-Fi), Bluetooth, NFC, or infrared communication. The communication range can be set as an area including, for example, a radius of 1 to 3 meters from the mobile terminal 200. This is assumed to be a device owned by the driver of the vehicle 100 while it is moving, and has a distance sufficient for short-range wireless communication with the vehicle 100.

[0043] The control unit 201 can predict the degree of risk of an accident using the acquired information and notify the driver of the predicted degree of risk. Therefore, the control unit 201 may have the same functions as the control unit 101 of the vehicle 100 described above. Alternatively, the control unit 201 and the control unit 101 may perform processing in cooperation with each other. Therefore, like the control unit 101, the control unit 201 includes an information acquisition unit 211, a risk prediction unit 212, and a risk notification unit 213. These processing units perform the same processing as the information acquisition unit 111, the risk prediction unit 112, and the risk notification unit 113 of the vehicle 100, and therefore their description will be omitted.

[0044] The server group 300 includes various servers, such as an accident data accumulation server 301, a driving characteristic server 302, and an environmental information server 303. Note that this does not intend to limit the present invention, and servers with other functions may also be included. The accident data accumulation server 301 accumulates accident data including environmental information at the time of past accidents. Part of the accident data includes environmental information, such as weather information 311, time information 312, illuminance information 313, road surface position information 314, and driving area information 315. This environmental information is data acquired from vehicles, mobile devices, various servers, etc., and is accumulated in association with the corresponding accident data. The accident data includes various information in addition to the environmental information, but detailed description thereof is omitted as it is not necessary for explaining the present invention. In the present invention, the environmental information included in the accident data is used to predict the risk of an accident involving a traveling vehicle.

[0045] The driving characteristic server 302 includes accumulated data 321 and a driving characteristic identification unit 322. The accumulated data 321 is data indicating the behavior of the vehicle, such as sensor information acquired by the on-board sensor 107 from the vehicle 100, etc. The driving characteristic identification unit 322 generates a model indicating the driving characteristics of the driver based on the accumulated data 321. The model may be generated at any timing, such as periodically, when instructed by the user, or when a predetermined amount of data has been accumulated.

[0046] The environmental information server 303 is a server that transmits various types of environmental information in response to requests from external devices such as the vehicle 100 and the mobile terminal 200. The environmental information server 303 includes a location information transmission unit 331 and a weather information transmission unit 332. The location information transmission unit 331 transmits map information and the like corresponding to the current location of the requesting vehicle 100 or mobile terminal 200. The weather information transmission unit 332 transmits weather information around the current location of the requesting vehicle 100 or mobile terminal 200, such as sunny, rainy, cloudy, snowy, sleet, high temperature, dryness, wind speed, and the like.

[0047] <Procedure for predicting risk> 4 shows a procedure for predicting a risk level according to this embodiment. In this embodiment, an example will be described in which the mobile terminal 200 performs risk prediction control. However, this is not intended to limit the present invention, and the procedure may be performed, for example, in the vehicle 100 or in the server group 300. An example of the procedure performed in the server group 300 will be described later in the second embodiment.

[0048] The on-board sensor 107 acquires information indicating the behavior of the vehicle 100 while it is traveling. Specifically, the acceleration sensor 171 measures the acceleration of the vehicle 100 while it is traveling, and detects behavior such as sharp turns and sudden acceleration. The angular velocity sensor 172 measures, for example, the angular velocity of the yaw axis, and detects behavior such as left-right sway. The wheel speed sensor 173 measures the vehicle speed of the vehicle 100 while it is traveling. The brake pressure sensor 174 measures the brake pressure, and detects behavior such as braking strength. The detected data is stored, for example, as accumulated data 321 transmitted to the driving characteristic server 302. Furthermore, the driving characteristic identification unit 322 of the driving characteristic server 302 identifies the driving characteristics of the corresponding driver based on the accumulated data 321. For example, as shown in 402, the driving characteristic identification unit 322 identifies driving characteristics such as swaying, corrective steering, unstable vehicle speed, and sudden braking based on the accumulated data 321. Furthermore, as shown in 403, it is desirable that the driving characteristic identification unit 322 generates a model for each driver that indicates the driving characteristics and transmits it to the vehicle 100 or the mobile terminal 200. The model held by the vehicle 100 or the mobile terminal 200 is used when predicting the risk of an accident.

[0049] Driving characteristics such as swaying, corrective steering, unstable vehicle speed, and sudden braking may be identified by comparing with a generalized normal driving model using the driving characteristics of multiple drivers stored in the driving characteristics server 302, or by setting threshold values for the measurement values of each sensor.

[0050] The mobile terminal 200 acquires environmental information from the vehicle 100 and the server group 300, and acquires accident data from the accident data accumulation server 301. The data accumulated in the accident data accumulation server 301 is environmental information at the time of past accidents, and therefore the amount of data is enormous. Therefore, the mobile terminal 200 may acquire a somewhat narrowed data group by, for example, passing any parameter of the surrounding environmental information acquired from a sensor or the like provided in the vehicle 100 or the mobile terminal 200 to the accident data accumulation server 301. This reduces the amount of communication traffic. In this embodiment, the mobile terminal 200 compares the environmental information around the vehicle 100 with the environmental information at the time of the accident and obtains the comparison result. However, as described in the second embodiment, the server group 300 may perform the comparison and transmit the comparison result to the mobile terminal 200. Note that, in this embodiment, the mobile terminal 200 performs the comparison with the accident data, which has the advantage of dynamically updating the risk level in response to changes in the surrounding environmental information. On the other hand, the second embodiment has the advantage of reducing the amount of communication traffic because accident data is not transmitted or received.

[0051] The mobile device 200 predicts the risk of an accident according to the driver based on the driving characteristics model identified in 403 and the matching result in 404. The matching result indicates, for example, the number of similar parameters (degree of agreement) among multiple parameters included as environmental information. The mobile device 200 weights the degree of agreement obtained from the matching result according to the driving characteristics model generated in 403, and estimates the risk according to the driver's driving characteristics. Thereafter, the mobile device 200 notifies the driver of the predicted risk according to the predicted risk, as shown in 406, and controls the vehicle as necessary, as shown in 407.

[0052] <Comparison with accident data> 5 explains the matching of the surrounding environment information and the environmental information of the accident data according to this embodiment. The matching according to this embodiment compares each parameter of the surrounding environment information with each parameter included in the accident data, and obtains the degree of match or precision from the number of similar items.

[0053] Reference numeral 500 denotes a portion of the accident data stored in the accident data storage server 301. The accident data includes various environmental information indicated by 501. The environmental information 501 includes, for example, time information, driving lane position (road surface position) information, illuminance information, area information, and weather information. Accident data "0001" shown in FIG. 5 is accident data of a past rear-end collision, and the time is "16:00-24:00," the driving lane position is "shoulder," the illuminance is "1000 lx or less," the area is "suburban area," and the weather is "sunny." The time is defined as a predetermined period that includes the time of the accident. This is because defining a strict time would reduce the degree of agreement and precision, which would adversely affect risk prediction, so a range is provided for the predetermined period. The driving lane position indicates the type of lane currently being driven, such as shoulder or passing lane. Note that the type of road currently being driven may also include dirt, asphalt, bridge, etc. Depending on the road type, for example, the likelihood of freezing may vary, and accidents may occur due to skidding caused by freezing. Regional information is information obtained from map information based on location information obtained by GNSS.

[0054] The mobile terminal 200 compares the time, GNSS, MAP, illuminance, and weather acquired in relation to the traveling vehicle 100 with the corresponding environmental information of the accident data, and obtains a matching rate 502 with the accident data based on the number of similar items. In the example of Figure 5, for accident data "0001," four out of five items are similar, indicating that a matching rate of 80% has been obtained. Also, for accident data "0002," two out of five items are similar, indicating that a matching rate of 40% has been obtained.

[0055] <Basic flow> 6 is a flowchart showing the processing steps for predicting and notifying the risk level of the vehicle 100 while it is moving according to this embodiment. The processing described below is realized, for example, by the control unit 201 of the mobile terminal 200 reading a program stored in the storage unit 202 into RAM and executing it. Note that the numbers following S indicate the step numbers of each process. Here, the processing flow in which the mobile terminal 200 predicts the risk level is described, but the control unit 101 of the vehicle 100 may also be configured to perform similar processing. The processing flow in that case is similar to the processing flow described below, so description thereof will be omitted.

[0056] First, in S101, the control unit 201 acquires environmental information (first environmental information) around the vehicle 100 while it is traveling using the information acquisition unit 211. More specifically, the control unit 201 acquires, for example, time and GNSS information (location information) from various devices of the mobile terminal 200, and acquires information about the MAP, illuminance, and weather from the server group 300. Note that the devices and servers from which the information is acquired are not limited to those mentioned above, and the information can be acquired from any device or server. For example, the location information may be acquired from a server via the wireless communication device 207, or may be acquired from the vehicle 100 via the short-range wireless device 206. Furthermore, if the vehicle 100 has a sensor for detecting illuminance, the illuminance may also be acquired from the vehicle 100 via the short-range wireless device 206. Furthermore, the illuminance can also be calculated from the acquired weather information.

[0057] Next, in S102, the control unit 201 acquires accident data (second environmental information) from the accident data accumulation server 301 via the wireless communication device 207. At this time, as described above, the control unit 201 may request the accident data accumulation server 301 to provide accident data using at least one of the time, position, MAP, illuminance, and weather information acquired in S101 as a parameter. For example, it is desirable to request accident data by adding parameters that do not change immediately, such as time (time zone) and weather information. The accident data accumulation server 301 narrows down the target accident data according to the acquired parameters and provides it to the mobile terminal 200.

[0058] Next, in S103, the control unit 201 compares the surrounding environment information acquired in S101 with the environmental information at the time of the accident, which is included in the accident data acquired in S102. The comparison method has already been explained using Fig. 5, so a detailed description is omitted. Here, the control unit 201 acquires the compatibility rate of each parameter of the surrounding environment information for each piece of acquired accident data.

[0059] Next, in S104, the control unit 201 predicts the risk of the traveling vehicle 100 using a model indicating the driver's driving characteristics acquired from the driving characteristic server 302 and the matching rate acquired in S103. Typically, if the matching rate is 60% or higher, i.e., if there are five parameters to be compared and three or more parameters are similar, the risk is predicted as high; if fewer than three parameters are similar, the risk is predicted as medium; and if there are no similar parameters, the risk is predicted as zero. On the other hand, for example, if the driving characteristics indicate frequent use of sudden braking and sudden braking is related to the cause of an accident at the current driving location, the control unit 201 can adjust the risk according to the driving characteristics. For example, if the driving characteristics indicate that the driver frequently brakes suddenly, and if sudden braking is related to the cause of an accident at the current driving location, the control unit 201 can adjust the risk according to two or more similarities. Note that, while an example has been described in which a model of driving characteristics is used to predict the risk from similar parameters, this is not limiting. For example, the conditions for determining similarity when comparing parameters in S103 may be adjusted according to the model of driving characteristics. Alternatively, both may be used.

[0060] When the risk level is predicted, in S105, the control unit 201 controls the vehicle 100 via the short-range wireless device 206 to notify the driver of the risk level according to the predicted risk level, controls the vehicle 100 to restrict the operation of the vehicle 100 as necessary, and ends the process. Details of the process of S105 will be described later with reference to FIG. 7.

[0061] <Notification control / vehicle control> 7 is a flowchart showing the detailed processing procedure of the notification control and vehicle control in S105 according to this embodiment. The processing described below is realized, for example, by the control unit 201 of the mobile terminal 200 reading a program stored in the storage unit 202 into RAM and executing it. The numbers following S indicate the step numbers of each process. Here, a processing flow in which the mobile terminal 200 predicts the risk level will be described, but the control unit 101 of the vehicle 100 may also be configured to perform similar processing. The processing flow in that case is similar to the processing flow described below, and therefore description thereof will be omitted.

[0062] Here, for example, the following process is performed on the accident data with the highest matching rate from the process results of S103 and S104. First, in S201, the control unit 201 determines whether there are any similar items in the parameters of the environmental information and surrounding environment information of the accident data. If there are any similar items, the process proceeds to S202; if not, the process proceeds to S206. In S206, since there are no similar items, the control unit 201 determines that the risk level is zero, does not notify the driver of the risk level, and ends the process. Alternatively, the control unit 201 may notify the driver that the risk level is zero. For example, the control unit 201 may light up a lamp included in the meter display 152 in green. This allows the driver to recognize that they are not currently traveling in an area where there is a risk of an accident.

[0063] Meanwhile, in S202, the control unit 201 determines whether there are three or more similar items. If there are three or more similar items, the process proceeds to S203; if not, the process proceeds to S205. In S205, the control unit 201 notifies the driver that the risk level is "medium" and ends the process. For example, the control unit 201 may turn on a lamp included in the meter display 152 of the vehicle 100 in red, or may display a character string or an icon indicating the risk level is "medium."

[0064] On the other hand, in S203, the control unit 201 determines that the risk level is "high" because there are three or more similar items, and notifies the driver of the risk level. For example, the control unit 201 may cause a lamp included in the meter display 152 of the vehicle 100 to flash red, or may display a character string or an icon indicating the risk level is "high." Next, in S204, the control unit 201 controls the operation of the vehicle 100 as necessary and ends the process. More specifically, if deceleration is necessary, the control unit 201 requests the vehicle 100 to slow down the speed of the traveling vehicle 100, and ends the process. The vehicle 100 controls the vehicle speed, for example, by applying the brakes, in response to a request from the mobile terminal 200. Note that the vehicle speed is not limited to being limited here, and other operations, such as turning on the lights or switching on the high beams, may also be controlled. Also, while an example of controlling the vehicle to be restricted only when the risk level is "high" is described here, the vehicle may also be restricted even when the risk level is "medium," or the vehicle may be restricted depending on the type of risk. Here, the type of risk indicates the cause or type of the accident, and includes various causes such as a fall, a rear-end collision, being caught in a vehicle, falling asleep at the wheel, and drinking alcohol.

[0065] <Model generation and update> 8 is a flowchart showing the processing flow when acquiring a model according to this embodiment. The processing described below is realized by, for example, the control unit 201 of the mobile terminal 200 reading a program stored in the storage unit 202 into RAM and executing it. Note that the numbers following S indicate the step numbers of each process. Here, the processing flow in which the mobile terminal 200 predicts the risk level will be described, but the control unit 101 of the vehicle 100 may also be configured to perform similar processing. The processing flow in that case is similar to the processing flow described below, so description thereof will be omitted.

[0066] In S301, the control unit 201 acquires information from various sensors from the vehicle 100 via the short-range wireless device 206. Subsequently, in S302, the control unit 201 transmits the acquired information from the various sensors to the driving characteristic server 302 and requests the identification of the driving characteristics of the corresponding driver. Here, the control unit 201 preferably adds the driver's identification information when making the request. This makes it possible to identify the driving characteristics of each driver even when the vehicle 100 is used by multiple people. The driving characteristic server 302 identifies the driving characteristics from the accumulated data 321 related to the driver and generates a model that represents the identified driving characteristics. The identified driving characteristics include, for example, swaying during driving, frequency and degree of corrective steering, instability of vehicle speed, frequency of sudden braking, etc. Of course, other driving characteristics may also be included.

[0067] In S303, the control unit 201 acquires the generated model from the driving characteristic server 302, stores it in the storage unit 202, and ends the process. The model stored in the storage unit 202 is used for weighting when predicting the risk level while the driver is driving the vehicle 100.

[0068] <Second embodiment> A second embodiment of the present invention will be described below. In the first embodiment, an example was described in which the vehicle 100 or the mobile terminal 200 checks the surrounding environment information against the accident data. In this case, a certain amount of accident data is stored in the vehicle 100 or the mobile terminal 200, and while it is possible to dynamically predict the risk level based on changes in parameters while driving, the amount of communication required to acquire the accident data increases. Therefore, in this embodiment, an example will be described in which the check is performed in the server group 300. In this case, there is no need to transmit the accident data from the server to the vehicle or the mobile terminal, and therefore the amount of communication can be reduced.

[0069] <System control configuration> 9 is a block diagram showing the control configuration of a risk prediction system according to this embodiment. Here, only the parts that differ from the configuration of the first embodiment will be mainly described. Therefore, the same reference numerals will be used for the same configurations, and the description will be omitted.

[0070] The server group 300 includes an accident data accumulation server 901 instead of the accident data accumulation server 301 of Fig. 3. In addition to the accident data 311 to 315, the accident data accumulation server 901 further includes a comparison unit 911 and a matching result transmission unit 912. The comparison unit 911 executes the process of S103 shown in Fig. 6, that is, compares and collates the surrounding environment information with the corresponding accident data. The matching result transmission unit 912 transmits the matching result by the comparison unit 911 to the vehicle 100 or the mobile terminal 200.

[0071] <Procedure for predicting risk> Fig. 10 shows the procedure for predicting the risk level according to this embodiment. In this embodiment, an example will be described in which the accident data accumulation server 901 performs the collation and the mobile terminal 200 performs risk level prediction control. However, this is not intended to limit the present invention, and for example, the processing of the mobile terminal 200 may be performed in the vehicle 100, or even risk level prediction may be performed on the server side. Note that explanations that overlap with those of Fig. 4 will be omitted.

[0072] In this embodiment, surrounding environment information about the traveling vehicle 100 acquired by the mobile terminal 200 is transmitted to the accident data accumulation server 901. Because the accident data accumulation server 901 has already accumulated accident data, the acquired surrounding environment information is compared with the accumulated accident data. For example, in S103, the mobile terminal 200 transmits the surrounding environment information acquired in S101 to the accident data accumulation server 901 and requests a comparison. The accident data accumulation server 901 performs the comparison using a comparison unit 911, and transmits the comparison result to the mobile terminal 200 using a comparison result transmission unit 912. The mobile terminal 200 executes the processes from S104 onwards based on the acquired comparison result.

[0073] Although an example in which only the matching process is performed by the accident data accumulation server 901 has been described here, the accident data accumulation server 901 may also perform the risk prediction process. In this case, the accident data accumulation server 901 acquires a model indicating the driver's characteristics from the driving characteristic server 302, the vehicle 100, or the mobile terminal 200. The accident data accumulation server 901 predicts the risk and transmits the prediction result to the mobile terminal 200. The mobile terminal 200 performs the process of S105 based on the risk prediction result.

[0074] <Summary of the embodiment> The above embodiments disclose at least the following systems, vehicles, and mobile terminals.

[0075] 1. According to the above embodiment, the system comprises an acquisition means (111, 211) for acquiring first environmental information relating to the environment around a vehicle currently in motion, a prediction means (112, 212) for predicting the risk of an accident occurring by comparing the first environmental information acquired by the acquisition means with second environmental information, which is environmental information at the time of a past accident, and a notification means (113, 213) for notifying the driver of the vehicle of the prediction result by the prediction means.

[0076] According to this embodiment, it is possible to appropriately predict and notify the risk of an accident based on the environment around the vehicle while it is traveling. Furthermore, it is possible to appropriately predict the risk in accordance with the driving characteristics of the vehicle driver. That is, it is possible to appropriately predict the risk of an accident at the current location.

[0077] 2. In the above embodiment, the system further comprises a storage means (301, 901) for storing accident data relating to past accidents, and the second environmental information is stored by the storage means as part of the accident data.

[0078] According to this embodiment, the amount of information to be handled can be reduced by comparing only environmental information from the accumulated past accident data, thereby reducing the calculation cost and communication volume and enabling prediction of the risk of an accident appropriate to the driving conditions.

[0079] 3. In the above embodiment, the first environmental information includes at least information about the surrounding area during travel (MAP information), and the prediction means compares the second environmental information corresponding to the information about the surrounding area during travel with the first environmental information.

[0080] According to this embodiment, by making a judgment based on information about the area in which the vehicle is traveling, such as suburban areas or urban areas, it is possible to predict the risk of an accident that is more suited to the vehicle in motion.

[0081] 4. In the above embodiment, the first environmental information includes at least information regarding the road surface position during driving, and the prediction means compares the second environmental information corresponding to the information regarding the road surface position during driving with the first environmental information.

[0082] According to this embodiment, by determining road surface position information during travel, such as road shoulders, asphalt, unpaved roads, bridges, etc., it is possible to predict the risk of an accident that is more suited to the vehicle in travel.

[0083] 5. In the above embodiment, the first environmental information includes at least weather information during travel, and the prediction means compares the second environmental information corresponding to the weather information during travel with the first environmental information.

[0084] According to this embodiment, by taking into account weather information during driving, such as rain or sunshine, it is possible to predict the risk of an accident that is more suited to the vehicle in motion.

[0085] 6. In the above embodiment, the first environmental information includes at least information regarding the time period during which the vehicle is traveling, and the prediction means compares the second environmental information corresponding to the information regarding the time period during which the vehicle is traveling with the first environmental information.

[0086] According to this embodiment, by making a judgment based on information about the time period during which the vehicle is traveling, such as morning, early morning, or evening, it is possible to predict the risk of an accident that is more suited to the vehicle being traveled.

[0087] 7. In the above embodiment, the first environmental information includes at least information regarding illuminance during driving, and the prediction means compares the second environmental information corresponding to the information regarding illuminance during driving with the first environmental information.

[0088] According to this embodiment, by making a judgment based on information about the time period during which the vehicle is traveling, such as morning, early morning, or evening, it is possible to predict the risk of an accident that is more suited to the vehicle being traveled.

[0089] 8. In the above embodiment, the acquisition means acquires the first environmental information by a device (103, 104, 106) provided in the vehicle or a mobile terminal (203, 204) owned by the driver.

[0090] According to this embodiment, it is possible to conveniently obtain environmental information about a vehicle while it is in motion. Furthermore, by utilizing the equipment in the vehicle or a mobile terminal, it is not necessary to add new equipment to predict the risk of an accident, and this can be realized at low cost.

[0091] 9. In the above embodiment, the notification means displays the degree of danger on an indicator provided in the vehicle (152).

[0092] According to this embodiment, the driver of the vehicle can be informed of the degree of danger at a glance, and since no additional equipment is required, it can be realized at low cost.

[0093] 10. In the above embodiment, the vehicle further includes a sensor (107) that acquires the state of the vehicle, and an identification means (112, 212, 302) that identifies the driving characteristics of the driver based on the vehicle state information acquired by the sensor, and the notification means notifies the driver of a dangerous state corresponding to the driver based on the prediction result by the prediction means and the driving characteristics of the driver identified by the identification means.

[0094] According to this embodiment, by predicting the risk of an accident taking into account the vehicle state (driving characteristics), it is possible to make a prediction that is more suited to the driver.

[0095] 11. In the above embodiment, the identifying means accumulates information acquired by the sensors and models the driving characteristics of the driver.

[0096] According to this embodiment, by modeling the driving characteristics of the driver in advance, it is possible to predict a risk that is more suited to the driver.

[0097] 12. In the above embodiment, the sensors include at least one of a sensor that detects brake pressure, a sensor that detects left and right sway, a sensor that detects acceleration, and a sensor that detects vehicle speed.

[0098] According to this embodiment, by taking into account the brake pressure of the vehicle, it is possible to make a judgment more in line with the driving characteristics of the driver, such as the frequency of sudden braking.

[0099] 13. In the above embodiment, the mobile terminal (200) includes an acquisition means (211) for acquiring first environmental information relating to the environment around a vehicle currently in motion, a prediction means (212) for predicting the risk of an accident occurring by comparing the first environmental information acquired by the acquisition means with second environmental information, which is environmental information at the time of a past accident, and a notification means for notifying the driver of the vehicle of the prediction result by the prediction means.

[0100] According to this embodiment, it is possible to appropriately predict and notify the risk of an accident based on the environment around the vehicle while it is traveling. Furthermore, it is possible to appropriately predict the risk in accordance with the driving characteristics of the vehicle driver. That is, it is possible to appropriately predict the risk of an accident at the current location.

[0101] 14. In the above embodiment, the vehicle is provided with an acquisition means (111) for acquiring first environmental information relating to the environment around the vehicle currently traveling, a prediction means (112) for comparing the first environmental information acquired by the acquisition means with second environmental information, which is environmental information at the time of a past accident, to predict the risk of an accident occurring, and a notification means (113) for notifying the driver of the vehicle of the prediction result by the prediction means.

[0102] According to this embodiment, it is possible to appropriately predict and notify the risk of an accident based on the environment around the vehicle while it is traveling. Furthermore, it is possible to appropriately predict the risk in accordance with the driving characteristics of the vehicle driver. That is, it is possible to appropriately predict the risk of an accident at the current location.

[0103] Although the embodiments of the invention have been described above, the invention is not limited to the above-described embodiments, and various modifications and variations are possible within the scope of the gist of the invention. [Explanation of symbols]

[0104] 100: Vehicle, 101: Control unit, 102: Memory unit, 103: Clock, 104: External monitoring device, 105: Notification device, 106: External communication device, 107: In-vehicle sensor, 200: Mobile terminal, 201: Control unit, 202: Memory unit, 203: External communication device, 204: Imaging unit, 300: Server group, 301: Accident data accumulation server, 302: Driving characteristic server, 303: Environmental information server

Claims

1. 1. A system comprising: A sensor for acquiring the state of the vehicle; an identification means for identifying a driving characteristic of a driver relating to a behavior of the vehicle based on state information of the vehicle during travel that is acquired and accumulated by a sensor that acquires the state of the vehicle; an acquisition means for acquiring first environmental information relating to an environment around a currently traveling vehicle; a prediction means for predicting a risk of an accident from a matching rate indicating a rate at which corresponding parameters match when a plurality of parameters included in the first environmental information acquired by the acquisition means are compared with a plurality of parameters included in second environmental information, which is environmental information included in accident data at the time of a past accident, and for adjusting the risk of an accident when the driving characteristics of the driver identified by the identification means are related to the cause of the past accident; a notification means for notifying a driver of the vehicle of a prediction result by the prediction means; A system comprising:

2. further comprising a storage means for storing accident data relating to past accidents; 2. The system according to claim 1, wherein the second environmental information is stored by the storage means as part of the accident data.

3. The first environmental information includes, as the plurality of parameters, information about at least a surrounding area during travel; 3. The system according to claim 1, wherein the prediction means compares the second environmental information corresponding to information about the surrounding area during the traveling with the first environmental information.

4. the first environmental information includes, as the plurality of parameters, information on at least a road surface position during travel; 4. The system according to claim 1, wherein the prediction means compares the second environmental information corresponding to information about the road surface position during travel with the first environmental information.

5. The first environmental information includes at least weather information during traveling as the plurality of parameters, 5. The system according to claim 1, wherein the prediction means compares the second environmental information corresponding to the weather information during the traveling with the first environmental information.

6. The first environmental information includes, as the plurality of parameters, information about at least a time period during which the vehicle is traveling; 6. The system according to claim 1, wherein the prediction means compares the second environmental information corresponding to information relating to the time period during which the vehicle is traveling with the first environmental information.

7. The first environmental information includes, as the plurality of parameters, information on at least illuminance during driving, 6. The system according to claim 1, wherein the prediction means compares the second environmental information corresponding to information on illuminance during driving with the first environmental information.

8. 8. The system according to claim 1, wherein the acquisition means acquires the first environmental information by a device provided in the vehicle or a mobile terminal carried by the driver.

9. 9. The system according to claim 1, wherein the notification means displays the degree of danger on an indicator provided in the vehicle.

10. 10. The system according to claim 1, wherein the predicting means weights the compatibility rate in accordance with the driving characteristics of the driver to predict the risk of an accident occurring.

11. 11. The system according to claim 10, wherein the identifying means accumulates information acquired by the sensors and models the driving characteristics of the driver.

12. 12. The system according to claim 10 or 11, wherein the sensors include at least one of a sensor for detecting brake pressure, a sensor for detecting left / right sway, a sensor for detecting acceleration, and a sensor for detecting vehicle speed.

13. A mobile terminal, a first acquisition means for acquiring, from a server, driving characteristics of a driver relating to a behavior of the vehicle identified based on state information of the vehicle during travel acquired and accumulated by a sensor for acquiring a state of the vehicle; a second acquisition means for acquiring first environmental information relating to an environment around a currently traveling vehicle; a prediction means for predicting a risk of an accident from a compatibility rate indicating a rate at which corresponding parameters match when a plurality of parameters included in the first environmental information acquired by the second acquisition means are compared with a plurality of parameters included in second environmental information, which is environmental information included in accident data at the time of a past accident, and for adjusting the risk of an accident when the driving characteristics of the driver acquired by the first acquisition means are related to the cause of the past accident; a notification means for notifying a driver of the vehicle of a prediction result by the prediction means; A mobile terminal comprising:

14. A vehicle, a sensor for acquiring a state of the vehicle; an identification means for identifying a driving characteristic of a driver relating to a behavior of the vehicle based on state information of the vehicle during travel that is acquired and accumulated by a sensor that acquires the state of the vehicle; an acquisition means for acquiring first environmental information relating to an environment around the vehicle currently traveling; a prediction means for predicting a risk of an accident from a matching rate indicating a rate at which corresponding parameters match when a plurality of parameters included in the first environmental information acquired by the acquisition means are compared with a plurality of parameters included in second environmental information, which is environmental information included in accident data at the time of a past accident, and for adjusting the risk of an accident when the driving characteristics of the driver identified by the identification means are related to the cause of the past accident; a notification means for notifying a driver of the vehicle of a prediction result by the prediction means; A vehicle characterized by comprising:

15. A method for controlling a mobile terminal, comprising: a first acquisition step of acquiring, from a server, driving characteristics of a driver related to a behavior of the vehicle, the driving characteristics being identified based on state information of the vehicle during travel that is acquired and accumulated by a sensor that acquires the state of the vehicle; a second acquisition step of acquiring first environmental information relating to an environment surrounding the currently traveling vehicle; a prediction step of predicting a risk of an accident from a matching rate indicating a rate at which corresponding parameters match when a plurality of parameters included in the first environmental information acquired in the second acquisition step are compared with a plurality of parameters included in second environmental information, which is environmental information included in accident data at the time of a past accident, and capable of adjusting the risk of an accident when the driving characteristics of the driver acquired in the first acquisition step are related to the cause of the past accident; a notification step of notifying a driver of the vehicle of the prediction result in the prediction step; A method for controlling a mobile terminal, comprising:

16. A method for controlling a vehicle equipped with a sensor for acquiring a state of the vehicle, comprising: an identifying step of identifying a driving characteristic of a driver related to a behavior of the vehicle based on state information of the vehicle during travel that is acquired and accumulated by a sensor that acquires the state of the vehicle; an acquisition step of acquiring first environmental information relating to an environment around the vehicle currently traveling; a prediction step of predicting the risk of an accident from a matching rate indicating the rate at which corresponding parameters match when a plurality of parameters included in the first environmental information acquired in the acquisition step are compared with a plurality of parameters included in second environmental information, which is environmental information included in accident data at the time of a past accident, and capable of adjusting the risk of an accident when the driving characteristics of the driver identified in the identification step are related to the cause of the past accident; a notification step of notifying a driver of the vehicle of the prediction result in the prediction step; A vehicle control method comprising:

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