Dangerous driving warning device and dangerous driving warning method

Abstract

The travel information sensor (14A) detects travel information of the own vehicle. The living body information sensor (13A) detects living body information of the driver. The camera section (12A) detects an expression of the driver. The communication section (32A) acquires, via the network (4), a degree of involvement indicating a degree to which the own vehicle is being subjected to an involvement behavior by other vehicles. The involved degree calculation section (313A) calculates an involved degree indicating a degree to which the own vehicle is being subjected to an involvement behavior from other vehicles. The danger degree judgment section (312A) calculates a danger degree including whether the own vehicle is being subjected to an involvement behavior from other vehicles, based on the involved degree and the degree of involvement. The prompting section (33A) warns of the danger degree when it is judged that the involvement behavior is being subjected to.

Description

Technical Field

[0001] This disclosure relates to dangerous driving warning devices, dangerous driving warning systems, and dangerous driving warning methods. Background Technology

[0002] The issue of drivers being subjected to dangerous behaviors (hereinafter referred to as "traffic-inducing behaviors") such as sudden overtaking, abrupt deceleration, or abnormal approach from other vehicles while driving their own vehicles has become a social problem. To ensure safe driving, it is desirable to promptly inform drivers of such traffic-inducing behaviors. On the other hand, due to unconsciousness or emotional agitation, drivers may sometimes engage in traffic-inducing behaviors towards other vehicles. In such cases, it is also desirable to warn drivers of their own vehicles.

[0003] As an invention related to this technology, Patent Document 1 discloses a technique for comprehensively judging the driver's mental state and implementing vehicle control based on the judgment result. However, the technology disclosed in Patent Document 1 does not provide a warning when the vehicle itself or other vehicles are engaging in distracting behavior.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2008-70965 Summary of the Invention

[0007] As mentioned above, Patent Document 1 discloses a technology for implementing vehicle control by comprehensively judging the driver's mental state, but does not warn about situations where the vehicle itself or other vehicles are engaging in distracting behavior.

[0008] The purpose of this implementation is to provide a dangerous driving warning device, a dangerous driving warning system, and a dangerous driving warning method, which can notify that the vehicle is being affected by other vehicles, that the vehicle is affecting other vehicles, or that the vehicle and other vehicles are affecting each other.

[0009] As an initial embodiment, a dangerous driving warning device is provided, which warns that a vehicle is being affected by another vehicle. It comprises: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the vehicle, biological information of the driver of the vehicle, and facial expression of the driver of the vehicle; a communication unit that acquires an "affectation degree" indicating the extent to which the other vehicle is affecting the vehicle via wireless communication; an "affected degree calculation unit" that calculates an "affected degree" indicating the extent to which the vehicle is being affected by the other vehicle based on the vehicle information; a "danger degree judgment unit" that determines a danger level, including whether the vehicle is being affected by the other vehicle, based on the affected degree and the affected degree; and a warning unit that warns of the danger level when it is determined that the vehicle is being affected by the other vehicle.

[0010] As another embodiment, a dangerous driving warning device is provided, which warns that a vehicle is being affected by another vehicle. It comprises: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; a communication unit that acquires information about another vehicle, including at least one of the driving information of the other vehicle, biological information of the driver of the other vehicle, and facial expression of the driver of the other vehicle, via wireless communication; an affected-degree calculation unit that calculates an affected-degree, representing the degree to which the vehicle is being affected by the other vehicle, based on the vehicle information; an affected-degree calculation unit that calculates an affected-degree, representing the degree to which the other vehicle is affecting the vehicle itself, based on the other vehicle information; a danger level determination unit that determines a danger level, including whether the vehicle is being affected by the other vehicle, based on the affected-degree and the affected-degree; and a warning unit that warns of the danger level when it is determined that the vehicle is being affected by the other vehicle.

[0011] As another embodiment, a dangerous driving warning system is provided, comprising a vehicle-mounted device for its own vehicle, a vehicle-mounted device for other vehicles, and a server connected to the vehicle-mounted device and the vehicle-mounted devices via a network. The dangerous driving warning system warns that the vehicle is being implicated by other vehicles. The vehicle-mounted device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the vehicle's driving information, the driver's biometric information, and the driver's facial expression; and a vehicle-side communication unit that communicates with the server via the network. At least one of the vehicle-mounted device and the server includes: an implicated degree calculation unit that calculates an implicated degree, representing the extent to which the vehicle is being implicated by other vehicles, based on the vehicle information. The other vehicle device includes: an other vehicle information acquisition unit that acquires other vehicle information including at least one of the other vehicle's driving information, the other vehicle's driver's biometric information, and the other vehicle's driver's facial expression; and an other vehicle-side communication unit that communicates with the self-vehicle device via the network. At least one of the other vehicle device and the server includes: an entanglement degree calculation unit that calculates an entanglement degree based on the other vehicle information, indicating the degree to which the other vehicle is entangled with the self-vehicle. The self-vehicle device also includes: a danger degree judgment unit that determines a danger degree, including whether the self-vehicle is being entangled by the other vehicle, based on the entanglement degree and the entanglement degree; and a warning unit that, when it is determined that the self-vehicle is being entangled, warns of the danger degree through at least one of the self-vehicle and the other vehicle.

[0012] As another embodiment, a dangerous driving warning method is provided, which warns that a vehicle is being affected by another vehicle. The dangerous driving warning method includes the following steps: acquiring vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; calculating a degree of being affected, indicating the extent to which the vehicle is being affected by another vehicle, based on the vehicle information; acquiring information about another vehicle including at least one of the driving information of the other vehicle, biological information of the driver of the other vehicle, and facial expression of the driver of the other vehicle; calculating a degree of influence, indicating the extent to which the other vehicle is affecting the vehicle itself, based on the other vehicle information; determining a danger level, including whether the vehicle is being affected by another vehicle, based on the degree of being affected and the degree of influence; and, when it is determined that the vehicle is being affected by the other vehicle, warning of the danger level through at least one of the vehicle itself and the other vehicle.

[0013] As another embodiment, a dangerous driving warning device is provided, which warns that its own vehicle is causing a disturbance to other vehicles. The dangerous driving warning device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; a communication unit that acquires a disturbance degree, indicating the extent to which other vehicles are being disturbed by the vehicle itself, via wireless communication; a disturbance degree calculation unit that calculates a disturbance degree, indicating the extent to which the vehicle itself is causing a disturbance to other vehicles, based on the vehicle information; a danger level judgment unit that determines a danger level, including whether the vehicle itself is causing a disturbance to other vehicles, based on the disturbance degree and the disturbance degree; and a warning unit that warns of the danger level when it is determined that the disturbance is in progress.

[0014] As another embodiment, a dangerous driving warning device is provided, which warns that its own vehicle is engaging in a distracting behavior towards other vehicles. The dangerous driving warning device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the own vehicle, biological information of the driver of the own vehicle, and facial expression of the driver of the own vehicle; a communication unit that acquires information about other vehicles, including at least one of the driving information of the other vehicle, biological information of the driver of the other vehicle, and facial expression of the driver of the other vehicle, via wireless communication; a distraction degree calculation unit that calculates a distraction degree, representing the degree to which the own vehicle is engaging in the distracting behavior towards other vehicles, based on the vehicle information; a distraction degree calculation unit that calculates a distraction degree, representing the degree to which other vehicles are being distracted by the vehicle, based on the other vehicle information; a danger level judgment unit that determines a danger level, including whether the own vehicle is engaging in the distracting behavior towards other vehicles, based on the distraction degree and the distraction degree; and a warning unit that warns of the danger level when it is determined that the distracting behavior is in progress.

[0015] As another embodiment, a dangerous driving warning system is provided, comprising a vehicle-mounted device for its own vehicle, a vehicle-mounted device for other vehicles, and a server connected to the vehicle-mounted device and the vehicle-mounted device via a network. The dangerous driving warning system warns that the vehicle is causing a disturbance to the other vehicle. The vehicle-mounted device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the vehicle's driving information, the driver's biometric information, and the driver's facial expression; and a vehicle-side communication unit that communicates with the server via the network. At least one of the vehicle-mounted device and the server includes: a disturbance degree calculation unit that calculates a disturbance degree, representing the extent to which the vehicle is causing a disturbance to the other vehicle, based on the vehicle information. The other vehicle device includes: an other vehicle information acquisition unit that acquires other vehicle information including at least one of the other vehicle's driving information, the other vehicle's driver's biometric information, and the other vehicle's driver's facial expression; and an other vehicle-side communication unit that communicates with the self-vehicle device via the network. At least one of the other vehicle device and the server includes: an entanglement degree calculation unit that calculates an entanglement degree based on the other vehicle information, indicating the degree to which the other vehicle is being entangled by the self-vehicle. The self-vehicle device further includes: a danger degree judgment unit that determines a danger degree, including whether the self-vehicle is entangled by the other vehicle, based on the entanglement degree and the entanglement degree; and a warning unit that, when it is determined that the entanglement is in progress, warns of the danger degree through at least one of the self-vehicle and the other vehicle.

[0016] As another embodiment, a dangerous driving warning method is provided, which warns that a vehicle is engaging in a distracting behavior towards another vehicle. The dangerous driving warning method includes the following steps: acquiring vehicle information, including at least one of the following: the vehicle's driving information, the driver's biological information, and the driver's facial expression; calculating a distraction degree based on the vehicle information, indicating the degree to which the vehicle is engaging in a distracting behavior towards the other vehicle; acquiring other vehicle information, including at least one of the other vehicle's driving information, the driver's biological information, and the driver's facial expression; calculating a distraction degree based on the other vehicle information, indicating the degree to which the other vehicle is being distracted by the vehicle; determining a danger level, including whether the vehicle is engaging in a distracting behavior towards the other vehicle, based on the distraction degree and the distraction degree; and, when it is determined that the distracting behavior is in progress, warning of the danger level through at least one of the vehicle and the other vehicle.

[0017] As another embodiment, a dangerous driving warning device is provided, which warns that its own vehicle is engaging in a distracting behavior towards other vehicles. The dangerous driving warning device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; a first distraction degree calculation unit that calculates a first distraction degree based on the vehicle information, indicating the degree to which the vehicle itself is engaging in a distracting behavior towards other vehicles; a communication unit that acquires a second distraction degree, indicating the degree to which other vehicles are engaging in a distracting behavior towards the vehicle itself, via wireless communication; a danger degree judgment unit that determines, based on the first distraction degree and the second distraction degree, a danger degree including whether the vehicle itself is engaging in the distracting behavior towards other vehicles; and a warning unit that warns of the danger degree when it is determined that the distracting behavior is in progress.

[0018] As another embodiment, a dangerous driving warning device is provided, which warns that its own vehicle is engaging in a distracting behavior towards other vehicles. The dangerous driving warning device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; a communication unit that acquires information about other vehicles, including at least one of the driving information of the other vehicles, biological information of the drivers of the other vehicles, and facial expression of the drivers of the other vehicles, via wireless communication; a distraction degree calculation unit that calculates a first distraction degree, representing the degree to which the vehicle itself is engaging in the distracting behavior towards other vehicles, based on the vehicle information, and calculates a second distraction degree, representing the degree to which other vehicles are engaging in the distracting behavior towards the vehicle itself, based on the other vehicle information; a danger degree judgment unit that determines whether the vehicle itself is engaging in the distracting behavior towards other vehicles based on the first distraction degree and the second distraction degree; and a warning unit that warns of the danger degree when it is determined that the distracting behavior is in progress.

[0019] As another embodiment, a dangerous driving warning system is provided, comprising a vehicle-mounted device for its own vehicle, a vehicle-mounted device for other vehicles, and a server connected to the vehicle-mounted device and the vehicle-mounted device via a network. The dangerous driving warning system warns that the vehicle and the other vehicle are engaging in mutually destructive behavior. The vehicle-mounted device includes: a vehicle information acquisition unit that acquires vehicle information including at least one of the vehicle's driving information, the driver's biometric information, and the driver's facial expression; and a vehicle-side communication unit that communicates with the server via the network. At least one of the vehicle-mounted device and the server includes: a first destructive degree calculation unit that calculates a first destructive degree based on the vehicle information, representing the degree to which the vehicle is engaging in destructive behavior towards the other vehicle. The vehicle device includes: an other vehicle information acquisition unit that acquires other vehicle information including at least one of the other vehicle's driving information, the other vehicle's driver's biometric information, and the other vehicle's driver's facial expression; and an other vehicle-side communication unit that communicates with the vehicle device via the network. At least one of the other vehicle device and the server includes: a second entanglement degree calculation unit that calculates a second entanglement degree based on the other vehicle information, indicating the degree to which the other vehicle is entangled with the vehicle. The vehicle device also includes: a danger degree judgment unit that determines a danger degree, including whether the vehicle and the other vehicle are entangled with each other, based on the first entanglement degree and the second entanglement degree; and a warning unit that, when it is determined that the entanglement is in progress, warns of the danger degree through at least one of the vehicle and the other vehicle.

[0020] As another embodiment, a dangerous driving warning method is provided, which warns that a vehicle and other vehicles are engaging in mutually destructive behavior. The dangerous driving warning method includes the following steps: acquiring vehicle information including at least one of the following: driving information of the vehicle itself, biological information of the driver of the vehicle itself, and facial expression of the driver of the vehicle itself; calculating a first destructive degree based on the vehicle information, indicating the degree to which the vehicle itself is engaging in destructive behavior towards the other vehicle; acquiring other vehicle information including at least one of the following: driving information of the other vehicle, biological information of the driver of the other vehicle, and facial expression of the driver of the other vehicle; calculating a second destructive degree based on the other vehicle information, indicating the degree to which the other vehicle is engaging in destructive behavior towards the vehicle itself; determining a danger level based on the first destructive degree and the second destructive degree, indicating whether the vehicle itself and the other vehicle are engaging in mutually destructive behavior; and when it is determined that the destructive behavior is in progress, warning of the danger level by at least one of the vehicle itself and the other vehicle.

[0021] According to the implementation method, it is possible to notify that its own vehicle is being implicated by other vehicles, that its own vehicle is implicated by other vehicles, or that both its own vehicle and other vehicles are implicated by each other. Attached Figure Description

[0022] Figure 1 This is a block diagram illustrating the structure of a dangerous driving warning system implemented in this way.

[0023] Figure 2 This is a block diagram showing the detailed structure of the vehicle device equipped with the dangerous driving warning system implemented in this embodiment.

[0024] Figure 3 This is a block diagram showing the detailed structure of other vehicle devices mounted on the hazardous driving warning system implemented in the embodiment.

[0025] Figure 4 This is a block diagram showing the detailed structure of the server mounted on the dangerous driving warning system implemented in this embodiment.

[0026] Figure 5A This is a diagram representing the organism information table set in the involvement degree correspondence table Tb1.

[0027] Figure 5B This is a diagram representing the vehicle information table set in the affected degree correspondence table Tb1.

[0028] Figure 6A This is a diagram representing the organism information table set in the involvement degree correspondence table Tb2.

[0029] Figure 6B This is a diagram representing the vehicle information table set in the entanglement correspondence table Tb2.

[0030] Figure 7A This is a graph representing the first evaluation value table, which shows the relationship between the degree of involvement and the evaluation value.

[0031] Figure 7B This is a graph representing the second evaluation value table, which shows the relationship between the degree of involvement and the evaluation value.

[0032] Figure 8A This is a diagram showing the correspondence between the warning content and the evaluation value given to vehicles involved in the incident.

[0033] Figure 8B This is a diagram showing the correspondence between the warning content and the evaluation value given to vehicles that are engaging in harmful behavior.

[0034] Figure 9 This is a flowchart illustrating the processing steps of the dangerous driving warning system according to the first embodiment.

[0035] Figure 10 This is a flowchart illustrating the processing steps of a dangerous driving warning system in a second variation of the first embodiment.

[0036] Figure 11 This is a flowchart illustrating the processing steps of the dangerous driving warning system according to the second embodiment.

[0037] Figure 12 This is a flowchart illustrating the processing steps of a dangerous driving warning system in a second variation of the second embodiment.

[0038] Figure 13 This is a flowchart illustrating the processing steps of the dangerous driving warning system according to the third embodiment.

[0039] Figure 14 This is a flowchart illustrating the processing steps of the dangerous driving warning system in the second variation of the third embodiment. Detailed Implementation

[0040] The embodiments will now be described with reference to the accompanying drawings.

[0041] [Description of the First Embodiment]

[0042] Figure 1 This is a block diagram illustrating the structure of a hazardous driving warning system implemented in this way. For example... Figure 1As shown, the hazardous driving warning system 101 of this embodiment is a system that warns a vehicle VA when it is being affected by or causing interference to another vehicle VB. The hazardous driving warning system 101 includes a vehicle-mounted device 1A (hazardous driving warning device) mounted on the vehicle VA, other vehicle devices 1B mounted on one or more other vehicles VB traveling around the vehicle VA in front, behind, to the left, and to the right, and a server 3 connecting the vehicle-mounted device 1A and the other vehicle devices 1B via a network 4. The vehicle-mounted device 1A and the other vehicle devices 1B are connected wirelessly via the server 3.

[0043] Figure 2 This is a block diagram showing the detailed structure of the vehicle's own device 1A. See below for reference. Figure 2 The structure of its own vehicle device 1A will be described. For example... Figure 2 As shown, the vehicle device 1A includes a controller 11A, a camera unit 12A, a biological information sensor 13A, a driving information sensor 14A, and a lidar 15A.

[0044] The camera unit 12A includes an exterior camera 121A for capturing images of the surroundings of the vehicle VA and an interior camera 122A for capturing images of the driver's face of the vehicle VA.

[0045] The exterior camera 121A is installed, for example, at the front, rear, and left and right sides of the vehicle VA, to capture images of the surroundings of the vehicle VA. The exterior camera 121A transmits the captured image data to the controller 11A. The exterior camera 121A can be an optical camera, a CCD (Charge-Coupled Device), a CMOS (Complementary Metal Oxide Semiconductor), or the like. If the vehicle VA is an autonomous vehicle, the exterior camera 121A can also be used as a camera for capturing images of the surroundings during autonomous driving.

[0046] An in-vehicle camera 122A is installed, for example, on the upper part of the windshield or near the dashboard of the vehicle itself (VA), to capture images of the driver's face. The in-vehicle camera 122A sends the captured image data to the controller 11A. As the in-vehicle camera 122A, an optical camera, CCD, CMOS, etc., can be used.

[0047] The bio-information sensor 13A detects various bio-information about the driver of the vehicle. Specific examples of the bio-information sensor 13A include a heart rate sensor that measures the driver's heart rate per unit time, a respiratory rate sensor that measures the number of breaths per unit time, and a blood pressure sensor that measures the driver's blood pressure. The information measured by the bio-information sensor 13A—namely, heart rate, respiratory rate, and blood pressure—is sent to the controller 11A. The measurement of heart rate, respiratory rate, and blood pressure can be achieved, for example, by using sensors embedded in the vehicle seat to detect the driver's body in a non-contact manner.

[0048] The driving information sensor 14A, for example, is a speed sensor and an acceleration sensor mounted on the vehicle VA, which transmits the detected speed and acceleration information to the controller 11A. The driving information sensor 14A also obtains information about the road and lane information of the vehicle VA from the GPS receiver mounted on the vehicle VA. Specifically, when the vehicle VA is traveling on a road with two or more lanes, it obtains information about whether the vehicle VA is traveling in a driving lane or an overtaking lane. The driving information sensor 14A transmits the lane information to the controller 11A.

[0049] The exterior camera 121A, interior camera 122A, biometric sensor 13A, driving information sensor 14A, and lidar 15A are examples of a self-vehicle information acquisition unit that acquires information about its own vehicle.

[0050] The lidar 15A is, for example, a lidar (Laser Imaging Detection and Ranging) installed on the front and rear sides of its own vehicle. The lidar 15A illuminates the front and rear of its own vehicle VA with laser light, receives the laser light reflected from the vehicles in front and behind, and determines the inter-vehicle distance to the vehicle in front and the inter-vehicle distance to the vehicle behind. The lidar 15A sends the detected inter-vehicle distance information to the controller 11A.

[0051] The controller 11A includes an arithmetic processing unit 31A, a communication unit 32A (vehicle-side communication unit), and a prompting unit 33A. The controller 11A can be configured, for example, as an integrated computer consisting of a central processing unit (CPU), RAM, ROM, hard disk, and other storage units.

[0052] The arithmetic processing unit 31A includes an image recognition unit 311A, a risk assessment unit 312A, a susceptibility calculation unit 313A, a susceptibility calculation unit 314A, and a storage unit 315A.

[0053] The image recognition unit 311A ​​identifies the movements of other vehicles VB located in the front, rear, right, and left directions of its own vehicle VA based on images of the surroundings of the vehicle VA captured by the external camera 121A.

[0054] The image recognition unit 311A ​​also acquires a facial image of the driver captured by the in-vehicle camera 122A, and analyzes the acquired facial image to detect the driver's expression in its own vehicle VA. Specifically, the driver's expression is categorized into anger, surprise, fear, sadness, fatigue, enjoyment, disgust, etc. Known techniques can be used to analyze the facial image.

[0055] The storage unit 315A stores the driver's facial expressions, various biological information detected by the biological information sensor 13A, vehicle driving information (speed and acceleration information) detected by the driving information sensor 14A, lane information, and inter-vehicle distance information detected by the lidar 15A. Additionally, the storage unit 315A stores information about the vehicle model VA. "Vehicle model" refers to the distinction between passenger cars and light vehicles, engine displacement, domestically produced and foreign vehicles, specific model name, and specific vehicle number.

[0056] Furthermore, the storage unit 315A stores a table Tb1 that quantifies the degree to which the vehicle VA is being affected by other vehicles VB traveling around it, based on the driver's facial expression, the driver's biological information, the vehicle VA's driving information, the lane information, the inter-vehicle distance information, and the vehicle type information of the vehicle VA.

[0057] The implicated behavior table Tb1 is also used in the following situations: based on the driver's facial expression, driver's biological information, driving information of other vehicles VB, driving lane information, inter-vehicle distance information, and vehicle type information of other vehicles VB, to quantify the degree to which other vehicles VB are being implicated from their own vehicle VA.

[0058] In addition, the storage unit 315A stores the interference degree correspondence table Tb2, which quantifies the degree to which the vehicle VA is interfering with other vehicles VB based on the driver's facial expression, the driver's biological information, the vehicle VA's driving information, the driving lane information, the inter-vehicle distance information, and the vehicle type information of the vehicle VA.

[0059] The interference degree correspondence table Tb2 is also used in the following situations: based on the driver's facial expression, driver's biological information, driving information of other vehicles VB, driving lane information, inter-vehicle distance information, and vehicle type information of other vehicles VB, to quantify the degree to which other vehicles VB are interfering with one's own vehicle VA.

[0060] See below Figure 5A , Figure 5B , Figure 6A , Figure 6B The detailed contents of the implicated degree correspondence table Tb1 and the implicated degree correspondence table Tb2 are described.

[0061] Storage unit 315A also stores a first evaluation value table TB1 and a second evaluation value table TB2. These tables are set to evaluate the degree to which the vehicle VA is being affected by other vehicles VB traveling in the surrounding area (hereinafter referred to as "affected degree") and the degree to which the vehicle VA is affecting other vehicles VB traveling in the surrounding area (hereinafter referred to as "affected degree"), divided into four levels. For details regarding each evaluation value table TB1 and TB2, please refer to [link to relevant documentation]. Figure 7A , Figure 7B To be described later.

[0062] The entanglement degree calculation unit 313A calculates the degree of entanglement of its own vehicle VA from other vehicles VB based on various biological information detected by the biological information sensor 13A of its own vehicle device 1A, various vehicle information detected by the driving information sensor 14A, information of the driving lane, and the inter-vehicle distance detected by the lidar 15A, and with reference to the entanglement degree correspondence table Tb1.

[0063] The entanglement degree calculation unit 313A also calculates the degree of entanglement of other vehicles VB from its own vehicle VA based on various biological information detected by the biological information sensor 13B of other vehicle device 1B, various vehicle information detected by the driving information sensor 14B, information of the driving lane, and the inter-vehicle distance detected by the lidar 15B, referring to the entanglement degree correspondence table Tb1.

[0064] The entanglement calculation unit 314A calculates the degree of entanglement of its own vehicle VA on other vehicles VB based on the biological information of the driver of its own vehicle VA, the vehicle information of its own vehicle VA, the information of the lane it is traveling in, and the information of the distance between vehicles.

[0065] When transmitting biometric information of the driver of another vehicle VB, vehicle information of the other vehicle VB, information about the lane in which it is traveling, and information about the distance between vehicles, the entanglement calculation unit 314A also calculates the degree of entanglement behavior of the other vehicle VB on its own vehicle VA based on this information, namely, the entanglement degree. The entanglement degree, the entanglement degree, and their calculation methods will be described later.

[0066] The risk assessment unit 312A determines, based on the degree of involvement and the degree of being involved, the extent to which its own vehicle VA is being involved by other vehicles VB (first evaluation value Xp, described below) and the extent to which its own vehicle VA is engaging in involvement behavior on other vehicles VB (second evaluation value Yp, described below).

[0067] The communication unit 32A communicates with the server 3 via the network 4. Specifically, it sends to the server 3 information including the driver's facial expression as recognized by the image recognition unit 311A, various biological information detected by the biological information sensor 13A, the driving information of the vehicle VA as detected by the driving information sensor 14A, the inter-vehicle distance information detected by the lidar 15A, and the lane information. Additionally, it receives data sent from the communication unit 22 (described later) of the server 3.

[0068] The communications unit 32A also obtains the degree of interference, indicating the extent to which other vehicles VB are interfering with its own vehicle VA, via server 3 and network 4.

[0069] The warning unit 33A, for example, is a display installed inside the vehicle. When the hazard judgment unit 312A determines that its own vehicle VA is being affected by the actions of another vehicle VB, or determines that its own vehicle VA is affecting the actions of another vehicle VB, or determines that its own vehicle VA and the other vehicle VB are mutually affecting each other, it displays a warning image. In addition to displaying images, warnings can also be given through sound, light, vibration, etc.

[0070] That is, when it is determined that its own vehicle VA is being affected by another vehicle VB, the notification unit 33A warns its own vehicle VA of the level of danger caused by the affected behavior. For example, it may indicate the dangerous situation and warn the driver through sound or text, such as "being affected by a vehicle behind". Alternatively, it may divide the level of danger into 10 levels, indicate the current level of danger and warn the driver. Or, it may warn the driver of the danger by continuously changing the display color from "blue" to "red".

[0071] Figure 3 This is a block diagram showing the detailed structure of other vehicle device 1B. For example... Figure 3As shown, the other vehicle device 1B includes a controller 11B, a camera unit 12B with an external camera 121B and an internal camera 122B, a biometric sensor 13B, a driving information sensor 14B, and a lidar 15B. Additionally, the controller 11B includes a processing unit 31B, a communication unit 32B (other vehicle-side communication unit), and a prompting unit 33B.

[0072] The exterior camera 121B, the interior camera 122B, the biometric sensor 13B, the driving information sensor 14B, and the lidar 15B are examples of other vehicle information acquisition units that acquire information about other vehicles.

[0073] Other vehicle device 1B has the same structure as the aforementioned vehicle device 1A. Therefore, the suffix "A" of each component will be represented as "B", and detailed descriptions will be omitted.

[0074] Figure 4 It means Figure 1 A block diagram of the structure of server 3. (See diagram below.) Figure 3 As shown, server 3 includes a control unit 21, a communication unit 22, and a storage unit 23.

[0075] The communication unit 22 is connected to its own vehicle device 1A and at least one other vehicle device 1B via network 4. The communication unit 22 communicates with its own vehicle device 1A and at least one other vehicle device 1B via network 4.

[0076] Storage unit 23 stores various data transmitted by the communication unit 32A of its own vehicle device 1A and the communication unit 32B of other vehicle devices 1B. Specifically, it stores driver facial expressions, various biological information, driving information of its own vehicle VA, inter-vehicle distance information, and vehicle model information of its own vehicle VA, transmitted from its own vehicle device 1A. Furthermore, it stores driver facial expressions, various biological information, driving information of other vehicles VB, inter-vehicle distance information, and vehicle model information of other vehicles VB, transmitted from other vehicle devices 1B.

[0077] The control unit 21 receives various information sent from its own vehicle device 1A and other vehicle devices 1B. It also performs control to send the received information to its own vehicle device 1A and other vehicle devices 1B. Furthermore, in the second variation of the first embodiment, the second variation of the second embodiment, and the second variation of the third embodiment described later, the same processing as the risk assessment unit 312A, the involvement calculation unit 313A, and the involvement calculation unit 314A described above are performed.

[0078] The control unit 21 can be configured as an integrated computer consisting of a central processing unit (CPU), RAM, ROM, hard disk and other storage units.

[0079] [Explanation of the Involvement Degree Correspondence Table Tb1]

[0080] Next, refer to Figure 5A , Figure 5B The table Tb1, which corresponds to the degree of involvement stored in the storage unit 315A, will be explained.

[0081] Figure 5A , Figure 5B This is a graph representing the drag-in table Tb1 used to quantify the degree to which a vehicle VA (or another vehicle VB) is being dragged in by other vehicles VB (or its own vehicle VA), i.e., the drag-in degree. Figure 5A This indicates a biometric table that sets values ​​based on the driver's biometric information. Figure 5B This represents a vehicle information table that sets values ​​based on its own vehicle's VA information.

[0082] like Figure 5A As shown, the biometric information table includes settings for heart rate (beats / minute), respiratory rate (breaths / minute), blood pressure (upper part) (mmHg), and the reliability (%) of facial expression (anger, sadness, fatigue), along with points and coefficients for each value. For example, if the driver's heart rate is in the range of 75-80 beats / minute, the point is "1" and the coefficient is "2". Conversely, if the respiratory rate is 40 breaths / minute or higher, the point is "5" and the coefficient is "1.5".

[0083] On the other hand, such as Figure 5B As shown in (a), the vehicle information table is set with the vehicle's speed [km / h], the number of rapid accelerations per unit time (10 seconds in this case), the degree of rapid acceleration (the change in speed within 1 second [%)), the frequency of side-view driving (the percentage of time within 1 minute when the line of sight deviates more than 30 degrees forward, backward, left, or right from the front [%)), the number of sharp turns (the number within 1 minute [times / minute]), and the points and coefficients for each value. For example, when the speed is 40-60 [km / h], it is considered to be driving at a standard speed, so the possibility of being affected by other vehicles' VB behavior is low, and the point is "0".

[0084] Additionally, at speeds of 30–40 km / h, the point value is set to “4” because the lower speed makes the user more susceptible to distraction. At speeds below 30 km / h, the point value is set to “5” because the user is more easily distracted. Furthermore, the coefficient is set to “1.5”.

[0085] Furthermore, the more frequent the rapid accelerations within a unit of time (e.g., 10 seconds), the higher the score will be. For example, if there are more than 5 rapid accelerations within 10 seconds, the score will be set to "5". Additionally, the coefficient will be "2". Furthermore, the more frequently you look to the side, the higher the score will be, as will the more frequent the sharp turns.

[0086] like Figure 5B As shown in (b), in the vehicle information table, the multiplication factor corresponding to the vehicle type information (VA) is set to a range of "1" to "1.5". Furthermore, the multiplication factor corresponding to the lane information is set to either "1" or "2.25". These multiplication factors, referred to later as "α" and "γ", are coefficients used when calculating the degree of entanglement. Therefore, the larger the multiplication factor, the greater the degree of entanglement.

[0087] As for the vehicle model information (VA), as mentioned above, it can be categorized by engine displacement and whether it is a domestically produced or foreign vehicle. For example, foreign vehicles with an engine displacement of 2000 cc or more are designated as "A", and those with a displacement of less than 2000 cc are designated as "B". Similarly, domestically produced vehicles with an engine displacement of 3000 cc or more are designated as "C", those with a displacement between 2000 and 3000 cc are designated as "D", those with a displacement between 1000 and 2000 cc are designated as "E", and those with a displacement of less than 1000 cc are designated as "F".

[0088] That is, it is believed that vehicles with small engine displacement, such as light vehicles, are more susceptible to being affected by the behavior, so the multiplication factor is set high; while it is believed that foreign vehicles with large engine displacement are less susceptible to being affected by the behavior, so the multiplication factor is set low.

[0089] Lane information indicates whether vehicle VA is traveling in the driving lane or the overtaking lane. If vehicle VA is continuously traveling in the driving lane, the multiplication factor is set to "1". For example, if vehicle VA has been traveling in the overtaking lane for more than 30 seconds within a 5-minute timeframe, the multiplication factor is set to "2.25". That is, when vehicle VA is traveling in the overtaking lane, the likelihood of being affected by other vehicles VB increases, hence the higher multiplication factor is set. If vehicle VA is traveling in a single-lane road on one side, the multiplication factor is set to "1".

[0090] The points, coefficients, and multiplication coefficients mentioned above are used in the formulas for calculating the degree of involvement and the degree of involvement (described later).

[0091] Furthermore, in the above description, the implicatedness correspondence table Tb1 stored in the storage unit 315A mounted on its own vehicle device 1A was described, but the same implicatedness correspondence table Tb1 is also stored in the storage unit 315B mounted on other vehicle devices 1B.

[0092] [Explanation of the drag coefficient table Tb2]

[0093] Next, refer to Figure 6A , Figure 6B The table Tb2, which corresponds to the degree of involvement stored in the storage unit 315A, will be explained.

[0094] Figure 6A , Figure 6B This is a graph representing the degree of involvement of a vehicle (VA) (or another vehicle (VB)) in relation to other vehicles (VB) (or itself), which is used to quantify the degree of involvement. Figure 6A This indicates a biometric table that sets values ​​based on the driver's biometric information. Figure 6B This indicates a vehicle information table that sets values ​​based on the vehicle's own information.

[0095] like Figure 6A As shown, the biometrics table includes settings for heart rate (beats / minute), respiratory rate (breaths / minute), blood pressure (upper side) (mmHg), and the reliability (%) of facial expression (anger), along with points and coefficients for each value. For example, when the heart rate is in the range of 75-80 (beats / minute), the point is "1" and the coefficient is "2". Conversely, when the respiratory rate is above 40 (breaths / minute), the point is "5" and the coefficient is "1.5".

[0096] On the other hand, such as Figure 6B As shown in (a), the vehicle information table is set with the vehicle's speed [km / h], the number of rapid accelerations per unit time (10 seconds in this case), the degree of rapid acceleration (the change in speed within 1 second [%)), the frequency of side-view driving (the percentage of time within 1 minute when the line of sight deviates more than 30 degrees forward, backward, left, or right from the front [%)), the number of sharp turns (the number within 1 minute [times / minute]), and the points and coefficients for each value. For example, when the speed is 60-70 km / h, the point is "1" and the coefficient is "1.5". Furthermore, when the number of rapid accelerations is 5 or more within 10 seconds, the point is set to "5" and the coefficient is "2".

[0097] In addition, such as Figure 6BAs shown in (b), in the vehicle information table, multiplication coefficients corresponding to the vehicle type information and body information of the vehicle's own VA are set in the range of "1" to "1.5". The vehicle type information of the own vehicle VA can be divided by the size of the vehicle's engine displacement and whether it is a domestic or foreign vehicle. As an example, domestic vehicles with an engine displacement of less than 1000 cc are set as "A", those with an engine displacement of 1000 to 2000 cc are set as "B", those with an engine displacement of 2000 to 3000 cc are set as "C", and those with an engine displacement of more than 3000 cc are set as "D". In addition, foreign vehicles with an engine displacement of less than 2000 cc are set as "E", and those with an engine displacement of more than 2000 cc are set as "F".

[0098] That is, it is believed that vehicles with large engine displacement or foreign vehicles are more likely to cause entanglement behavior, so the multiplication factor is set high; while it is believed that domestically produced vehicles with small engine displacement, such as light vehicles, are less likely to cause entanglement behavior, so the multiplication factor is set low.

[0099] The vehicle information includes its past traffic violation and accident history. A record of zero violations or accidents is designated as "A," with higher records designated as "B," "C," "D," "E," and "F." This is because vehicles with a high number of violations and accidents are considered more likely to implicate other vehicles; therefore, the higher the number of violations and accidents, the higher the multiplication factor. For example, vehicle models with a high number of violations and accidents have a higher multiplication factor. Additionally, vehicles with license plate numbers that have a high number of violations and accidents also have a higher multiplication factor.

[0100] Lane information indicates whether the vehicle VA is traveling in the driving lane or the overtaking lane. If the vehicle VA is continuously traveling in the driving lane, the multiplication factor is set to "1". For example, if the vehicle VA is traveling in the overtaking lane for more than 30 seconds within a 5-minute timeframe, the multiplication factor is set to "1.5". That is, when the vehicle VA is traveling in the overtaking lane, the likelihood of it hindering other vehicles VB increases, hence the higher multiplication factor is set. If the vehicle VA is traveling in a single lane on one side of the road, the multiplication factor is set to "1". Additionally, the more other vehicles present in the surrounding area, the higher the multiplication factor can be set.

[0101] The points, coefficients, and multiplication coefficients mentioned above are used in the formulas for calculating the degree of involvement and the degree of involvement (described later).

[0102] Furthermore, in the above description, the involvement degree correspondence table Tb2 stored in the storage unit 315A mounted on its own vehicle device 1A was described, but the same involvement degree correspondence table Tb2 is also stored in the storage unit 315B mounted on other vehicle devices 1B.

[0103] Moreover, in the first embodiment, using Figure 5A , Figure 5B The table Tb1 shows the degree of involvement. Calculate the degree of involvement (set as "Q1") when vehicle VA is being involved in the actions of other vehicles VB. Furthermore, using... Figure 6A , Figure 6B The table Tb2 shows the drag-in degree. The drag-in degree (Q2) is calculated when another vehicle (VB) is dragging your vehicle (VA) in a drag-in manner. Based on the calculated drag-in degree Q1 and drag-in degree Q2, the evaluation value of the danger caused by the drag-in behavior of other vehicles (VB) on your vehicle (VA) and the evaluation value of the danger caused by the drag-in behavior of other vehicles (VB) on your vehicle (VA) are calculated. Warnings corresponding to each evaluation value are then sent to the driver of your vehicle (VA). Details regarding the "evaluation value" are described later.

[0104] Furthermore, in the second embodiment, the degree of involvement (Q1) when another vehicle (VB) is being involved from the vehicle (VA) is using the involvement degree correspondence table Tb1 is calculated. And, the degree of involvement (Q2) when the vehicle (VA) is being involved in the actions of another vehicle (VB) is using the involvement degree correspondence table Tb2 is calculated. Based on the calculated involvement degree (Q1) and involvement degree (Q2), an evaluation value of the danger caused by the involvement of another vehicle (VB) from the vehicle (VA) and an evaluation value of the danger caused by the involvement of the vehicle (VA) in the actions of another vehicle (VB) is being carried out are calculated, and a warning corresponding to each evaluation value is issued to the driver of the vehicle (VA).

[0105] Furthermore, in the third embodiment, using the entanglement degree correspondence table Tb2, the entanglement degree Q2 (first entanglement degree) when the vehicle VA is engaging in entanglement behavior towards other vehicles VB, and the entanglement degree Q2' (second entanglement degree) when other vehicles VB are engaging in entanglement behavior towards the vehicle VA, are calculated. Based on the calculated first entanglement degree Q2 and second entanglement degree Q2', the evaluation value of the danger caused by the entanglement behavior of other vehicles VB towards the vehicle VA, and the evaluation value of the danger caused by the entanglement behavior of the vehicle VA towards other vehicles VB, are calculated, and a warning corresponding to each evaluation value is issued to the driver of the vehicle VA.

[0106] [Explanation of the calculation method for draggage]

[0107] Next, the method by which the entanglement degree calculation unit 313A calculates the entanglement degree Q1 of its own vehicle VA when it is being implicated by other vehicles VB, with reference to the aforementioned entanglement degree correspondence table Tb1, will be explained.

[0108] The involvement calculation unit 313A calculates the involvement degree based on various information detected by its own vehicle device 1A. Figure 5A , Figure 5B The points and coefficients of each item shown are used to calculate the parameter q1 using the following formula (1).

[0109] q1 = (Heart rate) * 2 + (Respiratory rate) * 1.5 + (Blood pressure) * 2

[0110] +(Emotional points)*1.5+(Speed ​​points)*1.5+(Number of rapid accelerations)*2+(Number of rapid accelerations)*1.5+(Frequency of glancing at the road)*1.5+(Number of sharp turns)*2…(1)

[0111] In equation (1), when all points are at their maximum value of "5", q1 = 77.5.

[0112] Furthermore, by multiplying parameter q1 by a multiplication factor corresponding to the vehicle type information of its own vehicle VA (set as "α") and the number of points corresponding to the lane information (set as "γ"), the degree of entanglement Q1 is calculated using the following formula (2). α is a value of 1 to 1.5, and γ is a value of 1 or 2.25.

[0113] Q1=α*γ*q1…(2)

[0114] In equation (2), the degree of involvement Q1 is a value between 0 and 261.6.

[0115] [Explanation of the calculation method for drag]

[0116] Next, the entanglement calculation unit 314A is referenced. Figure 6A , Figure 6B The following explains how to calculate the drag degree Q2 when a vehicle VA is dragging other vehicles VB, based on the drag degree correspondence table Tb2 shown.

[0117] The involvement calculation unit 314A calculates the involvement based on the driver's facial expression, the driver's biometric information, and the driving information of the vehicle VA. Figure 6A , Figure 6B The points and coefficients shown are used to calculate the parameter q2 using the following equation (3).

[0118] q2 = (heart rate points) * 2 + (breath count points) * 1.5 + (blood pressure points) * 2 + (facial expression points) * 1.5 + (speed points) * 1.5 + (number of rapid accelerations points) * 2 + (degree of rapid acceleration points) * 1.5 + (frequency of glancing at the side while driving) * 1.5 + (number of sharp turns) * 2…(3)

[0119] In equation (3), when all the points are the maximum value, i.e., “5”, q2 = 77.5.

[0120] Furthermore, by multiplying the parameter q2 by the number of points corresponding to the vehicle type information of the vehicle VA (set as "α"), the number of points corresponding to the vehicle body information (set as "β"), and the number of points corresponding to the lane information (set as "γ"), the entanglement degree Q2 is calculated using the following formula (4). α and β are values ​​from 1 to 1.5, and γ is a value of 1 or 1.5.

[0121] Q2=α*β*γ*q2…(4)

[0122] In equation (4), the degree of involvement Q2 is a value between 0 and 261.6.

[0123] [Explanation of the evaluation value table]

[0124] Next, the evaluation value table will be explained. Figure 2 The storage unit 315A shown stores a first evaluation value table and a second evaluation value table. The first evaluation value table represents the evaluation value set by dividing the degree of influence Q1 of the vehicle VA from other vehicles VB into 4 levels. The second evaluation value table represents the evaluation value set by dividing the degree of influence Q2 of the vehicle VA's influence on other vehicles VB into 4 levels.

[0125] Figure 7A This is a graph representing the first evaluation value table TB1. (Example) Figure 7A As shown, the first evaluation value table TB1 sets the relationship between the degree of involvement Q1, which varies within the range of "0 to 202.5", and the evaluation value Xp. Specifically, the degree of involvement Q1 is divided into 4 levels, which are used as the evaluation values ​​Xp, and are set from X0 to X3. For example, if the degree of involvement Q1 is in the range of 0 to 50, then the evaluation value Xp is X0.

[0126] Figure 7B This is a graph representing the second evaluation value table TB2. (Example) Figure 7BAs shown, the second evaluation value table TB2 sets the relationship between the drag level Q2, which varies within the range of "0 to 202.5", and the evaluation value Yp. Specifically, the drag level Q2 is divided into four levels, which are used as the evaluation values ​​Yp, and are set to Y0 to Y3. For example, if the drag level Q2 is above 150, then the evaluation value Yp is Y3.

[0127] The risk assessment unit 312A determines the assessment value Xp (set as the first assessment value Xp) based on the degree of involvement Q1 and with reference to the first assessment value table TB1. Additionally, it determines the assessment value Yp (set as the second assessment value Yp) based on the degree of involvement Q2 and with reference to the second assessment value table TB2.

[0128] The hazard assessment unit 312A also determines the content of the warning to the driver of its own vehicle VA based on the determined first evaluation value Xp and second evaluation value Yp (see reference). Figure 8A , Figure 8B It then outputs to the prompt unit 33A.

[0129] [Explanation of the function of the first embodiment]

[0130] Next, refer to Figure 9 The flowchart shown illustrates the function of the first embodiment. Figure 9 This is a flowchart illustrating the processing steps of the dangerous driving warning system 101 according to the first embodiment. First, in Figure 9 In step S11, the controller 11A acquires information detected by the camera unit 12A, the bio-information sensor 13A, the driving information sensor 14A, and the lidar 15A mounted on its own vehicle device 1A.

[0131] In step S12, the controller 11A stores the information obtained in the processing of step S11 in the storage unit 315A.

[0132] In step S13, the entanglement degree calculation unit 313A calculates the values ​​of the aforementioned information by referring to the entanglement degree correspondence table Tb1. For example, as Figure 5B As shown, when the vehicle VA is traveling at a speed of 60-70 km / h, the point number is "1".

[0133] In step S14, the entanglement degree calculation unit 313A calculates the entanglement degree Q1. Specifically, the entanglement degree calculation unit 313A calculates the degree to which its own vehicle VA is being implicated by other vehicles VB, i.e., the entanglement degree Q1, using the aforementioned formula (2). The entanglement degree Q1 can be calculated using the value at the determination time (instantaneous). Alternatively, for example, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q1 during this time period can be used. Alternatively, the peak value in the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q1.

[0134] In step S15, the communication unit 32A acquires the data of the entanglement degree Q2 calculated by the other vehicle device 1B. As described above, in the other vehicle device 1B, the entanglement degree Q2 when the other vehicle VB is entangled with its own vehicle VA is calculated by the entanglement degree calculation unit 314B. Specifically, the entanglement degree Q2 is calculated using the aforementioned formula (4). The data of the entanglement degree Q2 is sent to the server 3 via the network 4 and to the own vehicle device 1A. The communication unit 32A receives the data of the entanglement degree Q2. The entanglement degree Q2, like the aforementioned entanglement degree Q1, can be calculated using the value at the determination time (instantaneous). In addition, a time period (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q2 during the time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q2.

[0135] In step S16, the risk assessment unit 312A sets a first evaluation value Xp based on the degree of involvement Q1 calculated in step S14. Specifically, it sets the first evaluation value Xp. Figure 7A Any one of X0 to X3 shown is set as the first evaluation value Xp.

[0136] In step S17, the risk assessment unit 312A sets a second evaluation value Yp based on the involvement degree Q2 obtained in the processing of step S15. Specifically, it sets the second evaluation value Yp. Figure 7B Any one of Y0 to Y3 shown is set as the second evaluation value Yp.

[0137] In step S18, the hazard assessment unit 312A determines whether the distance between its own vehicle VA and other vehicles VB is below a preset threshold distance Lth based on the distance between the vehicle VA and other vehicles VB. If the distance is below the threshold distance Lth (S18: Yes), the process proceeds to step S21; otherwise (S18: No), the process proceeds to step S19.

[0138] In step S19, the hazard assessment unit 312A sets a warning message for the driver of its own vehicle VA based on a first evaluation value Xp and a second evaluation value Yp. For example, the warning message is determined based on the first evaluation value Xp, provided that the second evaluation value Yp is Y1 or higher.

[0139] Figure 8A This is a diagram indicating a warning message when vehicle VA is being affected by actions from other vehicles VB. For example... Figure 8A As shown, when the first evaluation value Xp is X0, it is set to "No warning". Additionally, when the first evaluation value Xp is X1 to X3, the warning content corresponding to each evaluation value is set.

[0140] In step S20, the hazard assessment unit 312A determines whether to warn the driver of its own vehicle VA about a situation where the vehicle is being implicated. If the second evaluation value Yp is Y0, or the first evaluation value Xp is X0, no warning is issued (S20: No), and the process ends.

[0141] On the other hand, if the second evaluation value Yp is Y1 or higher and the first evaluation value Xp is X1 or higher, in step S21, the danger judgment unit 312A issues a warning corresponding to the first evaluation value Xp through the prompting unit 33A.

[0142] Taking the case where the prompt unit 33A is a display as an example, when the first evaluation value Xp is X1, text such as "Please drive safely" is displayed. When the first evaluation value Xp is X2, text such as "Please drive carefully" is displayed. When the first evaluation value Xp is X3, text such as "Dangerous driving detected, please pay attention to surrounding vehicles" is displayed. That is, the higher the first evaluation value Xp, the stronger the attention reminder.

[0143] Furthermore, if the distance between vehicles is below the threshold distance (S18: Yes), a warning will be displayed, such as text or sound, indicating "An abnormally close vehicle is approaching, please be careful." That is, if the distance between vehicles is short and other vehicles (VB) are abnormally close to the vehicle (VA), a warning will be displayed regardless of the magnitude of the impact factor (Q1). Then, this process will end.

[0144] In this way, based on various information related to its own vehicle (VA) and other vehicles (VB), it can determine whether its own vehicle (VA) is being implicated by other vehicles (VB), and provide a warning to the driver of its own vehicle (VA) with the content corresponding to the degree of implicatedness (Q1).

[0145] [Explanation of the effects of the first embodiment]

[0146] Thus, the dangerous driving warning system 101 of the first embodiment can achieve the following effects.

[0147] (1) Calculate the degree of involvement Q1, which indicates the extent to which vehicle VA is being affected by the actions of other vehicle VB, and calculate the degree of involvement Q2, which indicates the extent to which other vehicle VB is causing involvement to vehicle VA. Based on the degree of involvement Q1 and the degree of involvement Q2, warn the driver of vehicle VA that they have been affected by the actions. Therefore, the driver of vehicle VA can immediately recognize that their vehicle VA is being affected and can quickly take measures to avoid danger.

[0148] (2) When other vehicles VB are causing interference to their own vehicle VA, the interference degree Q2 is calculated by the interference degree calculation unit 314B of the other vehicles VB and obtained by the own vehicle device 1A through communication via network 4, thus reducing the computational load in the own vehicle device 1A.

[0149] (3) The information for the vehicle VA includes vehicle type information and lane information. For example, if the vehicle VA is a light vehicle and is traveling in the overtaking lane, it is considered to be susceptible to being implicated. Therefore, the multiplication factor when calculating the implicatedness Q1 is set to a higher value. Thus, the implicatedness Q1 can be calculated with higher accuracy.

[0150] (4) Information about other vehicles (VB) includes vehicle type, vehicle body, and lane information. For example, if another vehicle (VB) is a large vehicle and is traveling in the overtaking lane, it is considered likely to cause entanglement, so the multiplication factor used to calculate entanglement degree Q2 is set to a higher value. Furthermore, if another vehicle (VB) has a history of numerous accidents or violations, it is considered likely to cause entanglement, so the multiplication factor used to calculate entanglement degree Q2 is set to a higher value. Therefore, entanglement degree Q2 can be calculated with higher accuracy.

[0151] (5) As vehicle information, the vehicle's speed, number of rapid accelerations, degree of acceleration, frequency of side-view driving, and number of sharp turns are used to calculate the degree of involvement Q1 and the degree of involvement Q2. When the vehicle speed is high, the number of rapid accelerations is high, or the degree of acceleration is large, the likelihood of being involved or engaging in such behavior is high. Therefore, the points used to calculate the degree of involvement Q1 and the degree of involvement Q2 are set high. Thus, the degree of involvement Q1 and the degree of involvement Q2 can be calculated with high accuracy.

[0152] (6) As biological information, the driver's heart rate, respiratory rate, and blood pressure are used to calculate the degree of involvement Q1 and the degree of involvement Q2. When the driver's heart rate, respiratory rate, and blood pressure are high, the probability of being involved in or engaging in such behavior is high. Therefore, the points used to calculate the degree of involvement Q1 and the degree of involvement Q2 are set high. Thus, the degree of involvement Q1 and the degree of involvement Q2 can be calculated with high accuracy.

[0153] [Description of a first variation of the first embodiment]

[0154] Next, a first variation of the first embodiment described above will be described. In the first variation, the configuration is as follows: Figure 4 The control unit 21 of the server 3 shown has the same risk assessment unit as the risk assessment unit 312A. In addition, the storage unit 23 of the server 3 stores the implicated degree correspondence table Tb1, the implicated degree correspondence table Tb2, the first evaluation value table TB1, and the second evaluation value table TB2, which is different from the first embodiment described above.

[0155] In the first variation, various information detected in the vehicle's own device 1A (biological information, driving information, images, lidar information, etc.) is transmitted from the communication unit 32A to the server 3 via the network 4, and the degree of involvement Q1 is calculated in the control unit 21 of the server 3. Additionally, various information detected in other vehicle devices 1B is transmitted from the communication unit 32B to the server 3 via the network 4, and the degree of involvement Q2 is calculated in the control unit 21 of the server 3. The structure otherwise is the same as the first embodiment described above.

[0156] Thus, in the dangerous driving warning system 101 of the first modification, a susceptibility correspondence table Tb1 and a susceptibility correspondence table Tb2 are set in the server 3, and susceptibility Q1 and susceptibility Q2 are calculated in the server 3. Therefore, the storage capacity and computational load of the storage units 315A and 315B installed in the vehicle device 1A and other vehicle devices 1B can be reduced.

[0157] [Description of a second variation of the first embodiment]

[0158] Next, a second variation of the first embodiment described above will be described. The device structure is the same as described above. Figures 1-4 Since they are the same, the structural description is omitted.

[0159] In the first embodiment described above, the influence degree Q2 of the other vehicle VB on the vehicle VA is calculated by the influence degree calculation unit 314B mounted on the other vehicle VB, and the calculated influence degree Q2 is sent to the vehicle VA via the network 4. In contrast, in the second variation, various information detected in the other vehicle device 1B is sent to the vehicle device 1A via the network 4. The difference is that the influence degree Q2 of the other vehicle VB is calculated by the influence degree calculation unit 314A mounted on the vehicle device 1A.

[0160] The following is for reference Figure 10 The flowchart shown illustrates the processing steps of the dangerous driving warning system 101 in the second variation.

[0161] First of all, Figure 10 In step S31, the controller 11A mounted on its own vehicle device 1A acquires the information detected by the camera unit 12A, the biological information sensor 13A, the driving information sensor 14A, and the lidar 15A.

[0162] In step S32, the controller 11A stores the information obtained in step S31 in the storage unit 315A.

[0163] In step S33, the entanglement degree calculation unit 313A quantifies the above information by referring to the entanglement degree correspondence table Tb1.

[0164] In step S34, the controller 11A of the vehicle device 1A acquires information detected by the camera unit 12B, biometric sensor 13B, driving information sensor 14B, and lidar 15B mounted on the other vehicle device 1B. Specifically, the controller 11A receives various data transmitted from the communication unit 32B of the other vehicle device 1B via the communication unit 32A of the vehicle device 1A.

[0165] In step S35, the controller 11A stores the information obtained in step S34 in the storage unit 315A.

[0166] In step S36, the entanglement calculation unit 314A refers to the entanglement correspondence table Tb2 and quantifies the above information.

[0167] In step S37, the entanglement degree calculation unit 313A calculates the entanglement degree Q1. Specifically, the entanglement degree calculation unit 313A calculates the degree to which its own vehicle VA is being implicated by other vehicles VB, i.e., the entanglement degree Q1, using the aforementioned formula (2). The entanglement degree Q1 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q1 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q1.

[0168] In step S38, the entanglement calculation unit 314A calculates the entanglement degree Q2. Specifically, the entanglement calculation unit 314A calculates the entanglement degree Q2 when another vehicle VB is engaging in entanglement behavior against its own vehicle VA using the aforementioned formula (4). The entanglement degree Q2 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q2 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q2.

[0169] In step S39, the risk assessment unit 312A sets a first evaluation value Xp based on the degree of involvement Q1 calculated in step S37. Specifically, it sets the first evaluation value Xp. Figure 7A Any one of the evaluation values ​​X0 to X3 shown is set as the first evaluation value Xp.

[0170] In step S40, the risk assessment unit 312A sets a second evaluation value Yp based on the involvement degree Q2 obtained in step S38. Specifically, it sets the second evaluation value Yp. Figure 7B Any one of the evaluation values ​​Y0 to Y3 shown is set as the second evaluation value Yp.

[0171] Steps S41 to S44 and Figure 9 The processes in steps S18 to S21 are the same, so the explanation is omitted.

[0172] Thus, in the dangerous driving warning system 101 of the second variation of the first embodiment, the degree of involvement Q1 when the vehicle VA is being involved by another vehicle VB is calculated by the involvement degree calculation unit 313A of the vehicle device 1A. In addition, various information about the other vehicle VB is obtained from the other vehicle VB, and the involvement degree calculation unit 314A uses the obtained information to calculate the degree of involvement Q2 when the other vehicle VB is causing involvement to the vehicle VA.

[0173] Therefore, the degree of involvement Q1 and degree of involvement Q2 can be calculated through the vehicle's own device 1A, thus reducing the computational load in other vehicles' VB.

[0174] [Description of the Second Embodiment]

[0175] Next, the second embodiment will be described. The dangerous driving warning system 101 of the second embodiment is similar to the one described above. Figures 1-4 The dangerous driving warning system 101 shown is identical, therefore its structural description is omitted. In the second embodiment, when a vehicle VA is engaging in a distracting behavior towards another vehicle VB, the driver of the vehicle VA is warned of the degree of danger caused by the distracting behavior.

[0176] The following is for reference Figure 11 The flowchart shown illustrates the processing steps of the dangerous driving warning system 101 according to the second embodiment. First, in Figure 11 In step S51, the controller 11A acquires information detected by the camera unit 12A, the bio-information sensor 13A, the driving information sensor 14A, and the lidar 15A mounted on its own vehicle device 1A.

[0177] In step S52, the controller 11A stores the information obtained in step S51 in the storage unit 315A.

[0178] In step S53, the entanglement calculation unit 314A refers to the entanglement correspondence table Tb2 and quantifies the above information. For example, as Figure 6B As shown, if the acceleration of the vehicle's VA occurs more than 5 times within 10 seconds, the score is "5".

[0179] In step S54, the entanglement calculation unit 314A calculates the entanglement degree Q2. Specifically, the entanglement calculation unit 314A calculates the entanglement degree Q2 when its own vehicle VA is engaging in entanglement behavior on other vehicles VB using the aforementioned formula (4). The entanglement degree Q2 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q2 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q2.

[0180] In step S55, the communication unit 32A acquires the data of the degree of involvement Q1 calculated by the other vehicle device 1B. As described above, in the other vehicle device 1B, the degree of involvement Q1 when the other vehicle VB is being involved by its own vehicle VA is calculated by the degree of involvement calculation unit 313B. Specifically, the degree of involvement Q1 is calculated using the aforementioned formula (2). The data of the degree of involvement Q1 is sent to the server 3 via the network 4 and also to the own vehicle device 1A. The communication unit 32A receives the data of the degree of involvement Q1. The degree of involvement Q1 can be calculated using the value at the determination time (instantaneous). In addition, a time period of backtracking from the determination time (e.g., 10 seconds) can be set, and representative values ​​such as the average value, peak value, and median value of the degree of involvement Q1 during the time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the degree of involvement Q1.

[0181] In step S56, the risk assessment unit 312A sets a first evaluation value Xp based on the degree of involvement Q1 obtained in the processing of step S55. Specifically, it sets the first evaluation value Xp. Figure 7A Any one of the evaluation values ​​X0 to X3 shown is set as the first evaluation value Xp.

[0182] In step S57, the risk assessment unit 312A sets a second evaluation value Yp based on the entanglement degree Q2 calculated in step S54. Specifically, it sets the second evaluation value Yp. Figure 7B Any one of the evaluation values ​​Y0 to Y3 shown is set as the second evaluation value Yp.

[0183] In step S58, the hazard assessment unit 312A determines whether the distance between its own vehicle VA and other vehicles VB is below a preset threshold distance Lth based on the distance between the vehicle VA and other vehicles VB. If the distance is below the threshold distance Lth (S58: Yes), the process proceeds to step S61; otherwise (S58: No), the process proceeds to step S59.

[0184] In step S59, the hazard assessment unit 312A sets a warning message for the driver of its own vehicle VA based on a first evaluation value Xp and a second evaluation value Yp. For example, the warning message is determined based on the second evaluation value Yp, provided that the first evaluation value Xp is X1 or higher.

[0185] Figure 8B This is a diagram indicating a warning message when vehicle VA is causing problems for other vehicles VB. For example... Figure 8B As shown, when the second evaluation value Yp is Y0, it is set to "No warning". Additionally, when the second evaluation value Yp is Y1 to Y3, the warning content corresponding to each evaluation value is set.

[0186] In step S60, the hazard assessment unit 312A determines whether to issue a warning to the driver of its own vehicle VA regarding ongoing entanglement behavior. If the first evaluation value Xp is X0, or the second evaluation value Yp is Y0, no warning is issued (S60: No), and the process ends.

[0187] On the other hand, if the first evaluation value Xp is X1 or higher (i.e., the driver of another vehicle VB is identified as being implicated), and the second evaluation value Yp is Y1 or higher, in step S61, the danger judgment unit 312A issues a warning corresponding to the second evaluation value Yp through the prompting unit 33A.

[0188] If we take the case where the prompt unit 33A is a display as an example, then as follows Figure 8B As shown, when the second evaluation value Yp is Y1, for example, the text "Please drive safely" is displayed. When the second evaluation value Yp is Y2, for example, the text "Please drive carefully" is displayed. When the second evaluation value Yp is Y3, for example, the text "Dangerous driving detected, please maintain a safe distance" is displayed.

[0189] Furthermore, if the inter-vehicle distance becomes below the threshold distance Lth (S58: Yes), a warning will be displayed, such as "Approaching the vehicle ahead abnormally. Please maintain sufficient inter-vehicle distance while driving," or an audio warning will be given. That is, if the inter-vehicle distance is short and the vehicle VA is abnormally approaching another vehicle VB, a warning will be displayed regardless of the magnitude of the interference degree Q2. Then, this process will end.

[0190] In this way, based on various information related to its own vehicle (VA) and other vehicles (VB), it can determine whether its own vehicle (VA) is engaging in any behavior that affects other vehicles (VB), and will send warning messages to the driver of its own vehicle (VA) with content corresponding to the degree of the behavior.

[0191] Thus, the dangerous driving warning system 101 of the second embodiment can achieve the following effects.

[0192] (1) Calculate the degree of interference Q2, which indicates the extent to which vehicle VA is interfering with other vehicle VB, and calculate the degree of being interfered with Q1, which indicates the extent to which other vehicle VB is being interfered with by vehicle VA. Then, based on the degree of interference Q2 and the degree of being interfered with Q1, issue a warning when vehicle VA is engaging in interfering behavior. Therefore, in situations where the driver of vehicle VA is unknowingly interfering with other vehicle VB, interfering behavior can be immediately identified, and measures for safe driving can be taken quickly.

[0193] (2) Since the degree of involvement Q1 of other vehicles VB is being involved from the vehicle VA is calculated by the degree of involvement calculation unit 313B of other vehicles VB and obtained by the vehicle device 1A through communication via network 4, the computational load in the vehicle device 1A can be reduced.

[0194] (3) The information for the vehicle VA includes vehicle type information, number of traffic violations and accidents, and lane information. For example, if the vehicle VA is a large vehicle or is driving in the overtaking lane, it is considered likely to cause entanglement, so the multiplication factor for calculating the entanglement degree Q2 is set to a higher value. Furthermore, if the vehicle VA has a history of numerous accidents and traffic violations, it is considered likely to cause entanglement, so the multiplication factor for calculating the entanglement degree Q2 is set to a higher value. Moreover, if the vehicle VA is driving in the overtaking lane, it is considered likely to cause entanglement to other vehicles VB, so the multiplication factor for calculating the entanglement degree Q2 is set to a high value. Therefore, the entanglement degree Q2 can be calculated with higher accuracy.

[0195] (4) Information for other vehicles VB includes vehicle type information and lane information. For example, if other vehicles VB are light vehicles or are traveling in the overtaking lane, they are considered to be prone to being implicated, so the multiplication factor when calculating the implicated degree Q1 is set to a higher value. Therefore, the implicated degree Q1 can be calculated with higher accuracy.

[0196] (5) As vehicle information, the vehicle's speed, number of rapid accelerations, degree of acceleration, frequency of side-view driving, and number of sharp turns are used to calculate the degree of involvement (Q2) and the degree of being involved (Q1). When the vehicle speed is high, the number of rapid accelerations is high, or the degree of acceleration is large, the likelihood of engaging in or being involved in such behavior is high. Therefore, the points used to calculate the degree of involvement (Q2) and the degree of being involved (Q1) are set high. Thus, the degree of involvement (Q2) and the degree of being involved (Q1) can be calculated with high accuracy.

[0197] (6) As biological information, the driver's heart rate, respiratory rate, and blood pressure are used to calculate the degree of involvement (Q2) and the degree of involvement (Q1). When the driver's heart rate, respiratory rate, and blood pressure are high, the probability of engaging in or being involved in a behavior is high. Therefore, the points used to calculate the degree of involvement (Q2) and the degree of involvement (Q1) are set high. Thus, the degree of involvement (Q2) and the degree of involvement (Q1) can be calculated with high accuracy.

[0198] [Description of the first variation of the second embodiment]

[0199] Next, a first variation of the second embodiment described above will be described. In the first variation, the configuration is as follows: Figure 4 The control unit 21 of the server 3 shown includes a risk assessment unit. Furthermore, the storage unit 23 of the server 3 stores an implicatedness correspondence table Tb1, an implicatedness correspondence table Tb2, a first evaluation value table TB1, and a second evaluation value table TB2, which differs from the second embodiment described above.

[0200] In the first variation, various information detected in the vehicle's own device 1A (biological information, driving information, images, lidar information, etc.) is transmitted from the communication unit 32A to the server 3 via the network 4, and the involvement degree Q2 is calculated in the control unit 21 of the server 3. Additionally, various information detected in other vehicle devices 1B is transmitted from the communication unit 32B to the server 3 via the network 4, and the involvement degree Q1 is calculated in the control unit 21 of the server 3. The structure other than this is the same as in the second embodiment described above.

[0201] Thus, in the dangerous driving warning system 101 of the first modification, a susceptibility correspondence table Tb1 and a susceptibility correspondence table Tb2 are set in the server 3, and susceptibility Q2 and susceptibility Q1 are calculated in the server 3. Therefore, the storage capacity and computational load of the storage units 315A and 315B installed in the vehicle device 1A and other vehicle devices 1B can be reduced.

[0202] [Description of a second variation of the second embodiment]

[0203] Next, a second variation of the second embodiment described above will be described. The device structure is the same as described above. Figures 1-4 Since they are the same, the structural description is omitted.

[0204] In the second embodiment described above, the influence degree Q1 of the other vehicle VB from its own vehicle VA is calculated by the influence degree calculation unit 313B mounted on the other vehicle VB, and the calculated influence degree Q1 is sent to the own vehicle VA via the network 4. In contrast, in the second variation, various information detected in the other vehicle device 1B is sent to the own vehicle device 1A via the network 4. Moreover, the difference is that the influence degree Q1 of the other vehicle VB is calculated by the arithmetic processing unit 31A mounted on the own vehicle device 1A.

[0205] The following is for reference Figure 12 The flowchart shown illustrates the processing steps of the dangerous driving warning system 101 in the second variation. First, in Figure 12In step S71, the controller 11A acquires information detected by the camera unit 12A, the bio-information sensor 13A, the driving information sensor 14A, and the lidar 15A mounted on its own vehicle device 1A.

[0206] In step S72, the controller 11A stores the information obtained in step S71 in the storage unit 315A.

[0207] In step S73, the entanglement degree calculation unit 313A quantifies the above information by referring to the entanglement degree correspondence table Tb1.

[0208] In step S74, the controller 11A acquires information detected by the camera unit 12B, biometric sensor 13B, driving information sensor 14B, and lidar 15B mounted on the other vehicle device 1B. Specifically, it receives various data transmitted from the communication unit 32B of the other vehicle device 1B through its own vehicle device 1A's communication unit 32A.

[0209] In step S75, the controller 11A stores the information obtained in step S74 in the storage unit 315A.

[0210] In step S76, the entanglement calculation unit 314A refers to the entanglement correspondence table Tb2 and quantifies the above information.

[0211] In step S77, the entanglement calculation unit 314A calculates the entanglement degree Q2. Specifically, the entanglement calculation unit 314A calculates the entanglement degree Q2 when its own vehicle VA is engaging in entanglement behavior on other vehicles VB using the aforementioned formula (4). The entanglement degree Q2 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the entanglement degree Q2 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q2.

[0212] In step S78, the entanglement calculation unit 313A calculates the entanglement degree Q1. Specifically, the entanglement calculation unit 313A calculates the entanglement degree Q1 when another vehicle VB is being implicated by its own vehicle VA using the aforementioned formula (2). The entanglement degree Q1 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average, peak, and median value of the entanglement degree Q1 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the entanglement degree Q1.

[0213] In step S79, the risk assessment unit 312A sets a first evaluation value Xp based on the degree of involvement Q1 obtained in the processing of step S78. Specifically, it sets the first evaluation value Xp. Figure 7A Any one of the evaluation values ​​X0 to X3 shown is set as the first evaluation value Xp.

[0214] In step S80, the risk assessment unit 312A sets a second evaluation value Yp based on the entanglement degree Q2 calculated in step S77. Specifically, it sets the second evaluation value Yp. Figure 7B Any one of the evaluation values ​​Y0 to Y3 shown is set as the second evaluation value Yp.

[0215] Processing in steps S81 to S84 Figure 11 The processes in steps S58 to S61 are the same, so the explanation is omitted.

[0216] Thus, in the dangerous driving warning system 101 of the second embodiment, the degree of interference Q2 when the vehicle VA is causing interference to another vehicle VB is calculated. Furthermore, various information about the other vehicle VB is obtained from the other vehicle VB, and the obtained information is used to calculate the degree of interference Q1 when the other vehicle VB is being affected by the vehicle VA.

[0217] Therefore, the involvement degree Q2 and the degree of being involved Q1 can be calculated through its own vehicle device 1A, thus reducing the computational load on other vehicle devices 1B.

[0218] [Description of the Third Embodiment]

[0219] Next, the third embodiment will be described. The dangerous driving warning system 101 of the third embodiment is similar to the one described above. Figures 1-4 The dangerous driving warning system 101 shown is the same, so the structural description is omitted.

[0220] but, Figure 2 The drag-in calculation unit 314A shown is a first drag-in calculation unit that calculates the drag-in degree Q2 (first drag-in degree), which indicates the degree to which the vehicle VA is dragging other vehicles VB. Furthermore, Figure 3 The drag-in calculation unit 314B shown is a second drag-in calculation unit that calculates the drag-in degree Q2' (second drag-in degree), which represents the degree to which another vehicle VB is dragging in one's own vehicle VA. In the third embodiment, when both one's own vehicle VA and another vehicle VB are dragging in each other, the driver of at least one of the two vehicles VA is warned of the danger caused by the drag-in behavior.

[0221] That is, in the first embodiment described above, a response was shown when the vehicle VA was being implicated by another vehicle VB, and in the second embodiment, a response was shown when the vehicle VA was implicated by another vehicle VB. In contrast, in the third embodiment, when the vehicle VA and the other vehicle VB are implicated by each other, a warning is issued to the driver of at least one of the vehicles VA and VB.

[0222] The following is for reference Figure 13 The flowchart shown illustrates the processing steps of the dangerous driving warning system 101 according to the third embodiment. First, in Figure 13 In step S91, the controller 11A acquires information detected by the camera unit 12A, the bio-information sensor 13A, the driving information sensor 14A, and the lidar 15A mounted on its own vehicle device 1A.

[0223] In step S92, the controller 11A stores the information obtained in step S91 in the storage unit 315A.

[0224] In step S93, the entanglement calculation unit 314A (first entanglement calculation unit) quantifies the aforementioned information by referring to the entanglement correspondence table Tb2. For example, as Figure 6B As shown, if the acceleration of the vehicle's VA occurs more than 5 times within 10 seconds, the score is "5".

[0225] In step S94, the entanglement calculation unit 314A calculates the entanglement degree Q2 (first entanglement degree). Specifically, the entanglement calculation unit 314A calculates the degree to which its own vehicle VA is entangled with other vehicles VB, i.e., the first entanglement degree Q2, using the aforementioned formula (4). The first entanglement degree Q2 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the first entanglement degree Q2 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the first entanglement degree Q2.

[0226] In step S95, the communication unit 32A acquires data of the entanglement degree (second entanglement degree; referred to as "Q2'") calculated by the entanglement degree calculation unit 314B (second entanglement degree calculation unit) of the other vehicle device 1B. As described above, in the other vehicle device 1B, the entanglement degree calculation unit 314B calculates the second entanglement degree Q2' when the other vehicle VB is engaging in entanglement behavior against its own vehicle VA. Specifically, the second entanglement degree Q2' is calculated using the aforementioned formula (4). The data of the second entanglement degree Q2' is sent to the server 3 via the network 4 and also to the own vehicle device 1A. The communication unit 32A receives the data of the second entanglement degree Q2'. The second entanglement degree Q2' can be calculated using the value at the determination time (instantaneous). In addition, a time period of rewinding a predetermined time (e.g., 10 seconds) from the determination time can be set, and representative values ​​such as the average value, peak value, and median value of the second entanglement degree Q2' during the time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the second entrapment level Q2'.

[0227] In step S96, the hazard assessment unit 312A sets a second evaluation value (set to "Yp1") for its own vehicle VA based on the first entanglement degree Q2 calculated in step S94. Specifically, it sets the second evaluation value (set to "Yp1") for its own vehicle VA. Figure 7B Any one of Y0 to Y3 shown is set as the second evaluation value Yp1 of the vehicle's VA.

[0228] In step S97, the hazard assessment unit 312A sets a second evaluation value (set to "Yp2") for other vehicles VB based on the second entanglement degree Q2' obtained in step S95. Specifically, it sets the second evaluation value (set to "Yp2") for other vehicles VB. Figure 7B Any one of Y0 to Y3 shown is set as the second evaluation value Yp2 of other vehicles' VB.

[0229] In step S98, the hazard assessment unit 312A determines whether the distance between its own vehicle VA and other vehicles VB is below a preset threshold distance Lth based on the distance between the vehicle VA and other vehicles VB. If the distance is below the threshold distance Lth (S98: Yes), the process proceeds to step S101; otherwise (S98: No), the process proceeds to step S99.

[0230] In step S99, the hazard assessment unit 312A sets a warning message for the driver of its own vehicle VA based on the second evaluation value Yp1 of its own vehicle VA and the second evaluation values ​​Yp2 of other vehicles VB. For example, the warning message is determined based on the second evaluation value Yp1 of its own vehicle VA, provided that the second evaluation value Yp2 of other vehicles VB is Y1 or higher.

[0231] like Figure 8BAs shown, when the second evaluation value Yp1 of the vehicle's VA is Y0, it is set to "No warning". Additionally, when the second evaluation value Yp1 of the vehicle's VA is Y1 to Y3, the warning content corresponding to each evaluation value is set.

[0232] In step S100, the hazard assessment unit 312A determines whether to issue a warning to the driver of its own vehicle VA regarding ongoing entanglement behavior. If the second evaluation value Yp2 of another vehicle VB is Y0, or if the second evaluation value Yp1 of its own vehicle VA is Y0, no warning is issued (S100: No), and the process ends.

[0233] On the other hand, if the second evaluation value Yp2 of other vehicles VB is Y1 or higher (i.e., the driver of other vehicles VB is engaging in distracting driving) and the second evaluation value Yp1 of the vehicle itself VA is Y1 or higher, in step S101, the danger judgment unit 312A issues a warning corresponding to the second evaluation value Yp1 of the vehicle itself VA through the prompting unit 33A.

[0234] If we take the case where the prompt unit 33A is a display as an example, then as follows Figure 8B As shown, when the second evaluation value Yp1 of the vehicle's own VA is Y1, for example, the text "Please drive safely" is displayed. When the second evaluation value Yp1 is Y2, for example, the text "Please drive carefully" is displayed. When the second evaluation value Yp1 is Y3, for example, the text "Dangerous driving detected, please maintain a safe distance" is displayed.

[0235] Furthermore, if the inter-vehicle distance becomes below the threshold distance Lth (S98: Yes), a message such as "Approaching an oncoming vehicle abnormally. Please maintain sufficient inter-vehicle distance while driving" or an audio prompt will be displayed. Then, this process will end.

[0236] In this way, based on various information related to its own vehicle (VA) and other vehicles (VB), it can determine whether its own vehicle (VA) and other vehicles (VB) are engaging in mutually destructive behaviors, and provide warning prompts to the driver of its own vehicle (VA) with content corresponding to the degree of destructive behavior.

[0237] Thus, the dangerous driving warning system 101 of the third embodiment can achieve the following effects.

[0238] (1) A first interference degree Q2, representing the extent to which vehicle VA is interfering with other vehicle VB, is calculated, and a second interference degree Q2', representing the extent to which other vehicle VB is interfering with vehicle VA, is calculated. Furthermore, based on each interference degree Q2 and Q2', a warning is issued that vehicle VA and other vehicle VB are interfering with each other. Therefore, the driver of vehicle VA can immediately recognize that vehicle VA and other vehicle VB are interfering with each other and can quickly take evasive action to avoid danger.

[0239] (2) When other vehicles VB are dragging their own vehicles VA, the second drag degree Q2' is calculated by the drag degree calculation unit 314B of the other vehicles VB and obtained by the own vehicle device 1A through communication, thus reducing the computational load of the own vehicle VA.

[0240] (3) The information for the vehicle itself (VA) and other vehicles (VB) includes vehicle type information, vehicle body information, and lane information. For example, if the vehicle itself (VA) or other vehicles (VB) are large vehicles, or if they are traveling in the overtaking lane, it is determined that they are prone to causing entanglement, so the multiplication coefficients for calculating each entanglement degree Q2 and Q2' are set to higher values. Moreover, if the vehicle itself (VA) or other vehicles (VB) are vehicles that have had a lot of violations or accidents in the past, it is determined that they are prone to causing entanglement, so the multiplication coefficients for calculating each entanglement degree Q2 and Q2' are set to higher values. Therefore, each entanglement degree Q2 and Q2' can be calculated with higher accuracy.

[0241] (4) As vehicle information, the vehicle's speed, number of rapid accelerations, degree of acceleration, frequency of side-view driving, and number of sharp turns are used to calculate each level of entanglement, Q2 and Q2'. When the speed is high, the number of rapid accelerations is high, or the degree of acceleration is large, the likelihood of entanglement behavior is high; therefore, the points used to calculate each level of entanglement, Q2 and Q2' are set high. Thus, each level of entanglement, Q2 and Q2' can be calculated with high accuracy.

[0242] (5) As biological information, the driver's heart rate, respiratory rate, and blood pressure are used to calculate each degree of involvement, Q2 and Q2'. When the driver's heart rate, respiratory rate, and blood pressure are high, the likelihood of involvement is high, so the points used to calculate each degree of involvement, Q2 and Q2' are set high. Therefore, each degree of involvement, Q2 and Q2', can be calculated with high accuracy.

[0243] [Description of the first variation of the third embodiment]

[0244] Next, a first variation of the third embodiment described above will be described. In the first variation, the configuration is as follows: Figure 4 The control unit 21 of the server 3 shown includes a risk assessment unit. Furthermore, the storage unit 23 of the server 3 stores an implicatedness correspondence table Tb1, an implicatedness correspondence table Tb2, a first evaluation value table TB1, and a second evaluation value table TB2, which differs from the third embodiment described above.

[0245] In the first variation, various information detected in the vehicle's own device 1A (biological information, driving information, images, lidar information, etc.) is transmitted from the communication unit 32A to the server 3 via the network 4. The control unit 21 of the server 3 calculates the first drag level Q2 of the vehicle VA. Additionally, various information detected in other vehicle devices 1B is transmitted from the communication unit 32B to the server 3 via the network 4. The control unit 21 of the server 3 calculates the second drag level Q2' of the other vehicle VB. The structure, apart from this, is the same as in the second embodiment described above.

[0246] Thus, in the dangerous driving warning system 101 of the first modification, a drag-related degree correspondence table Tb1 and a drag-related degree correspondence table Tb2 are set in the server 3, and the first drag-related degree Q2 of the vehicle itself VA and the second drag-related degree Q2' of the other vehicles VB are calculated in the server 3. Therefore, the storage capacity and computing load of the storage units 315A and 315B installed in the vehicle itself device 1A and the other vehicle devices 1B can be reduced.

[0247] [Description of the second variation of the third embodiment]

[0248] Next, a second variation of the third embodiment described above will be described. The device structure is the same as described above. Figures 1-4 Since they are the same, the structural description is omitted.

[0249] In the third embodiment described above, the second degree of interference Q2' of the interference behavior that the other vehicle VB is performing on its own vehicle VA is calculated using the interference degree calculation unit 314B mounted on the other vehicle VB, and the calculated second degree of interference Q2' is sent to the own vehicle VA via the network 4. In contrast, in the second variation, various information detected in the other vehicle device 1B is sent to the own vehicle device 1A via the network 4. The difference is that the second degree of interference Q2' of the interference behavior that the other vehicle VB is performing on its own vehicle VA is calculated by the arithmetic processing unit 31A mounted on the own vehicle device 1A.

[0250] The following is for reference Figure 14 The flowchart shown illustrates the processing steps of the dangerous driving warning system 101 in the second variation. First, in Figure 14In step S111, the controller 11A acquires information detected by the camera unit 12A, the bio-information sensor 13A, the driving information sensor 14A, and the lidar 15A mounted on its own vehicle device 1A.

[0251] In step S112, the controller 11A stores the information obtained in step S111 in the storage unit 315A.

[0252] In step S113, the entanglement calculation unit 314A refers to the entanglement correspondence table Tb2 and quantifies the above information.

[0253] In step S114, information detected by the camera unit 12B, biometric sensor 13B, driving information sensor 14B, and lidar 15B mounted on the other vehicle device 1B is acquired. Specifically, the vehicle device 1A receives various data transmitted from the communication unit 32B of the other vehicle device 1B through its own communication unit 32A.

[0254] In step S115, the controller 11A stores the information obtained in step S114 in the storage unit 315A.

[0255] In step S116, the entanglement calculation unit 314A refers to the entanglement correspondence table Tb2 and quantifies the above information.

[0256] In step S117, the entanglement calculation unit 314A calculates the first entanglement degree Q2 of its own vehicle VA. Specifically, the entanglement calculation unit 314A calculates the degree to which its own vehicle VA is entangled with other vehicles VB, i.e., the first entanglement degree Q2, using the aforementioned formula (4). The first entanglement degree Q2 can be calculated using the value at the determination time (instantaneous). Alternatively, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the first entanglement degree Q2 during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the first entanglement degree Q2.

[0257] In step S118, the entanglement calculation unit 314A calculates the second entanglement degree Q2' of the other vehicle VB. Specifically, the entanglement calculation unit 314A calculates the degree of entanglement behavior that the other vehicle VB is performing on its own vehicle VA, i.e., the second entanglement degree Q2', using the aforementioned formula (4). The second entanglement degree Q2' can be calculated using the value at the determination time (instantaneous). Alternatively, a time period of predetermined time (e.g., 10 seconds) can be set back from the determination time, and representative values ​​such as the average value, peak value, and median value of the second entanglement degree Q2' during this time period can be used. Alternatively, the peak value of the above time period can be maintained for a predetermined time (e.g., 10 seconds) and set as the second entanglement degree Q2'.

[0258] In step S119, the hazard assessment unit 312A sets a second evaluation value Yp1 for its own vehicle VA based on the first entanglement degree Q2 calculated in step S117. Specifically, it sets the second evaluation value Yp1 for its own vehicle VA. Figure 7A Any one of Y0 to Y3 shown is set as the second evaluation value Yp1 of the vehicle's VA.

[0259] In step S120, the hazard assessment unit 312A sets a second evaluation value Yp2 for other vehicles based on the second involvement degree Q2' of other vehicles VB calculated in step S118. Specifically, it sets a second evaluation value Yp2 for other vehicles. Figure 7B Any one of Y0 to Y3 shown is set as the second evaluation value Yp2 of other vehicles' VB.

[0260] Processing in steps S121 to S124 Figure 13 The processes in steps S98 to S101 are the same, so the explanation is omitted.

[0261] Thus, in the dangerous driving warning system 101 of the second variation of the third embodiment, a first degree of interference Q2 is calculated when the vehicle VA is causing interference to another vehicle VB. Furthermore, various information about the other vehicle VB is obtained from the other vehicle VB, and the obtained information is used to calculate a second degree of interference Q2' when the other vehicle VB is causing interference to the vehicle VA.

[0262] Therefore, it is possible to calculate each degree of involvement Q2 and Q2' through its own vehicle device 1A, thereby reducing the computational load of other vehicle devices 1B.

[0263] The foregoing has described embodiments of the present invention, but the descriptions and drawings that form part of this disclosure should not be construed as limiting the invention. Based on this disclosure, those skilled in the art will be able to identify various alternative embodiments, examples, and techniques.

[0264] This application claims priority based on Japanese Patent Application Nos. 2020-052863, 2020-052864 and 2020-052866, filed on March 24, 2020, the entire disclosure of which is incorporated herein by reference.

Claims

1. A dangerous driving warning device that warns a vehicle that is being implicated by other vehicles in the vicinity of the vehicle, characterized in that, The dangerous driving warning device includes: The vehicle information acquisition unit acquires vehicle information including at least one of the following: driving information of the vehicle, biological information of the driver of the vehicle, and facial expression of the driver of the vehicle. The communications department acquires, via wireless communication, a degree of interference indicating the likelihood that other vehicles are engaging in interference behavior towards its own vehicle. The susceptibility calculation unit calculates, based on its own vehicle information, a susceptibility degree, which represents the degree to which its own vehicle is likely to be affected by the actions of other vehicles. The danger assessment unit calculates the danger level, including whether the vehicle itself is being affected by the other vehicles, based on the degree of involvement and the degree of involvement. as well as The warning unit provides a warning corresponding to the first evaluation value of the danger level that classifies the affected degree into a predetermined level when the second evaluation value exceeds a predetermined value.

2. The dangerous driving warning device according to claim 1, characterized in that, The vehicle information also includes at least one of the vehicle model information and the lane information of the vehicle.

3. The dangerous driving warning device according to claim 1 or 2, characterized in that, The driving information includes at least one of the following: vehicle speed, number of rapid accelerations per unit time, degree of rapid acceleration, frequency of side-view driving, and number of sharp turns per unit time. The biological information includes at least one of the following: driver's heart rate, respiratory rate, and blood pressure.

4. A method for warning of dangerous driving, which warns a vehicle that is being implicated by other vehicles in the vicinity of the vehicle, characterized in that, The dangerous driving warning method includes the following steps: Obtain vehicle information that includes at least one of the following: the vehicle's driving information, the driver's biometric information, and the driver's facial expression. Based on the vehicle information, calculate the degree of involvement, which indicates the likelihood that the vehicle is being affected by the actions of the other vehicles. Acquire information about other vehicles that includes at least one of the following: driving information of the other vehicles, biometric information of the drivers of the other vehicles, and facial expressions of the drivers of the other vehicles. Based on the information about other vehicles, calculate the degree of interference, which indicates the likelihood that the other vehicles are engaging in interference behavior towards the vehicle itself. Based on the degree of involvement and the degree of involvement, a risk level is determined that includes whether the vehicle itself is being involved in the actions of other vehicles. as well as When the second evaluation value of the danger level, which classifies the entanglement degree into a predetermined level, is above a predetermined value, the vehicle itself issues a warning corresponding to the first evaluation value of the danger level, which classifies the entanglement degree into a predetermined level.

Images