Device

Abstract

To provide a device capable of improving accuracy of driving support.SOLUTION: A device 10 disclosed here acquires biological information of a user and determines the degree of driving risk to a user, based on the user's perceived state indicated by biological information related to the user's condition. The disclosed device 10 acquires at least one information selected from the parietal lobe region activity level and the prefrontal cortex region activity level in the user's brain, and determines the degree of driving risk to the user based on the information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a device for determining the degree of risk associated with a user's driving. [Background technology]

[0002] A method for providing driving assistance by determining and estimating the driver's condition from biometric information has been disclosed. In Patent Document 1, a driving control system is developed based on information about the driver's surrounding environment and biological information such as the driver's brain waves. A technology has been disclosed that predicts the driver's operations and provides driving assistance based on the prediction results.

[0003] In Patent Document 2, a system is developed to determine whether a driver feels uncomfortable based on biological information such as brain waves. The technology disclosed determines whether the driver is in a position where the vehicle is in a collision and gives the driver a sense of discomfort depending on the result of the determination. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-15418 [Patent Document 2] Japanese Patent Application Publication No. 2018-82805 Summary of the Invention [Problem to be solved by the invention]

[0005] The degree of risk of driving changes depending on whether the driver is aware of his / her own condition, and the method of driving assistance changes accordingly. However, in Patent Documents 1 and 2, even if the driver's condition can be estimated, whether the driver is aware of it or not cannot be reflected in the judgment of the degree of risk of driving, and there is room for improvement in the accuracy of judging the degree of risk of the user's driving.

[0006] The present disclosure aims to provide a device that can improve the accuracy of determining the degree of risk associated with a user's driving by solving such problems. [Means for solving the problem]

[0007] The device of the present disclosure acquires biometric information of a user and determines the degree of risk to the user's driving based on the subjective state of the user's condition indicated by the biometric information. Alternatively, the device of the present disclosure acquires information on at least one of the activity amount of the parietal lobe region and the activity amount of the prefrontal cortex region of the user's brain and determines the degree of risk to the user's driving based on the information. [Effects of the Invention]

[0008] The present disclosure provides a device that can determine the degree of risk involved in a user's driving. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a configuration of a driving assistance device according to a first embodiment. [Figure 2] 4 is a flowchart showing the operation of the driving assistance device according to the first embodiment. [Figure 3] 4 is a flowchart showing the operation of the driving assistance device according to the first embodiment. [Figure 4] 4 is a table showing an example of a notification to a user of the driving assistance device according to the first embodiment. [Figure 5] FIG. 2 is a block diagram showing the configuration of a computer according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Specific embodiments to which the present disclosure is applied will be described in detail below with reference to the drawings. In each drawing, the same elements are designated by the same reference numerals, and for the sake of clarity, Therefore, redundant explanations will be omitted where necessary.

[0011] (First embodiment) First, the configuration of a driving assistance device 10 according to the first embodiment will be described with reference to FIG. The assistance device 10 includes an acquisition unit 11, a first state estimation unit 12, a second state estimation unit 13, and a driving assistance unit 1 The driving assistance device 10 includes a terminal dedicated to driving assistance, e.g. The driving assistance device 10 is implemented as a terminal such as a smartphone or a tablet. The driving support device 10 may be realized as one of the functions of the device. The functions may be realized by a plurality of devices, such as a terminal and a server.

[0012] The acquisition unit 11 acquires the biometric information of the user who is the target of the driving assistance using a dedicated sensor, a camera, a smartphone, etc. Biometric information is acquired from various sensors and measuring devices such as smartwatches and wearable devices. The information may include, for example, pulse rate, heart rate, eye movement, eye opening, breathing rate, amount of cerebral blood flow, or beta waves. The acquisition unit 11 is configured to include these various sensors and measuring devices. Good too.

[0013] The first state estimation unit 12 is a unit for acquiring biological information such as pulse rate, respiratory rate, and eye movement amount. The first state estimation unit 12 then uses the acquired biometric information to estimate the user's The first state indicates whether the user is in a state that interferes with driving. For example, it indicates whether the user is experiencing stress, tension, fatigue, or other unfavorable conditions. The first state is a physical condition such as fever, or a mental condition such as lack of concentration, anger, or anxiety. For example, the first state estimation unit 12 may detect a pulse rate that is not within the normal range. In this case, the state in which the user is feeling stressed is considered to be a state in which the user is experiencing difficulties in driving. Other known techniques for estimating the user's condition may be used. Whether or not the value is within the normal range is determined by comparing it with values ​​set individually for each user, time period, etc. It is preferable to make a judgment based on this.

[0014] The second state estimation unit 13 estimates, for example, the amount or fluctuation of cerebral blood flow, which is an index of the amount of brain activity of the user. biometric information such as blood volume, hemoglobin levels, brain wave frequency, heart rate and heart rate fluctuations The second state estimation unit 13 uses the acquired biological information to estimate the first state. A second state indicating whether or not the user is aware of the state is estimated from the biological information.

[0015] Specifically, the second state estimation unit 13 estimates the state of the user based on the amount of brain activity, for example, the amount of cerebral blood flow. Specifically, the second state estimation unit estimates whether the user is aware of the first state. 13, when the amount of brain activity reaches a predetermined value or more, the user is aware of the first state. In addition, the second state estimation unit 13 estimates that the amount of brain activity has become smaller than a predetermined value. In this case, the second state estimation unit 13 estimates that the user is not aware of the first state. For example, the parietal lobe of the user's brain is an important area for processing and integrating sensory information such as body sensation, vision, and hearing. It is preferable to estimate this from measurements of the amount of cerebral blood flow and hemoglobin levels in the affected area. The second state estimation unit 13 estimates the state of a part of the brain that plays an important role in cognitive function, such as the prefrontal cortex of the user's brain. It can also be estimated from the amount of cerebral blood flow and hemoglobin levels in the area that plays a role. Any known technique for estimating whether a user is aware of their own condition may be used.

[0016] The driving assistance unit 14 provides the user with driving assistance corresponding to the first state and the second state. Driving assistance methods include audio output using an output device such as a speaker, and display. This is achieved by outputting text using an output device such as a ray.

[0017] Specifically, the first state estimation unit 12 estimates that the user is in a state that interferes with driving. In this case, the driving support unit 14 determines that the second state estimation unit 13 is in a state that interferes with driving. When it is estimated that the user is not aware of the above, the second state estimation unit 13 The user is given a stronger warning than if it were assumed that the user was aware of the condition. For example, if the first state estimation unit 12 estimates that the user is in a stressful state, and the second state estimation unit 12 estimates that the user is in a stressful state, If the state estimation unit 13 estimates that the user is not aware that he or she is in a stressful state, It is more dangerous to drive than if the user is aware that they are under stress. It is determined that this is the case and strongly urges users to be careful.

[0018] On the other hand, the first state estimation unit 12 estimates that the user is not in a state that would impede driving. For example, when the pulse rate is within the normal range, the state estimation unit 12 determines that the user is feeling stressed. Relaxed state without any distractions = It is assumed that the user is not in a state that would impede driving. The estimation unit 12 further determines whether the user is in a relaxed state or not, depending on the pulse rate value, for example. In this case, the driving support unit 14 may estimate whether the vehicle is in a neutral state. The user is not aware that the control unit 13 is not in a state that interferes with driving. For example, if the first state estimation unit 12 estimates that the user The second state estimation unit 13 estimates that the user is in a relaxed state. If it is assumed that the user is not aware that they are in a relaxed state, Users may be overconfident, distracted, or engrossed in their driving than if they were aware of it. The driving support unit 14 determines that there is a possibility of the vehicle being involved in an accident and strongly urges the user to be careful. In this case, the second state estimation unit 13 determines whether the user is aware that the state is not one that will impede driving. If it is estimated that the user is in a normal state, the system will not warn the user. That's fine.

[0019] Next, an example of the operation of the driving assistance device 10 according to the first embodiment will be described with reference to FIGS. 2 and 3. In the example shown in FIGS. 2 and 3, the first state estimation unit 12 determines that the user is feeling stressed. The first state is estimated as whether or not the vehicle is in a state where the driver ...

[0020] First, the acquisition unit 11 acquires the user's biological information from a dedicated sensor or a sensor such as a smart watch. The biological information is acquired from a measuring device or the like (step S101). For example, the acquisition unit 11 may acquire the information such as the degree of opening of the eyes, the respiratory rate, the cerebral blood flow, or the brain wave. The image is input and the measured pulse rate, eye opening, respiratory rate, etc. are acquired. By irradiating near-infrared light using a headband-type sensor and measuring the amount of light that returns, brain tissue can be analyzed. The measured cerebral blood flow information is obtained by detecting changes in blood flow in the tissue. The acquisition unit 11 then transmits the acquired biological information to the first state estimation unit 12 and the second state estimation unit 13. Supply to 13.

[0021] Next, the first state estimation unit 12 estimates the user's Whether the user is in a state where he or she is feeling stressed is estimated as a first state (step S102). For example, the first state estimation unit 12 acquires the pulse rate from the acquisition unit 11. Then, the first state estimation unit 12 acquires the pulse rate from the acquisition unit 11. If the pulse rate is not within the normal range, the measuring unit 12 determines that the user is in a stressed state. If the pulse rate is within the normal range, the first state estimation unit 12 estimates that the user The first state estimation unit 12 estimates that the user is not feeling stress. The first state estimation unit 12 then acquires the respiratory rate from the first state estimation unit 11. If the value is not within the range, it is assumed that the user is experiencing stress. If the respiratory rate is within the normal range, the state estimation unit 12 determines that the user is not feeling stressed. It is estimated that the situation is good.

[0022] Next, when the first state estimation unit 12 determines that the user is feeling stressed (step S103 YES), the second state estimation unit 13 determines that the user himself is feeling stressed. Whether or not the state is conscious is estimated as the second state (step S104).

[0023] For example, the second state estimation unit 13 may estimate the first state as the user's own state based on the amount of brain activity of the user. The brain, especially the parietal lobe, processes sensory information such as body sensation, vision, and hearing. It plays an important role in processing and integration, and measures the amount and fluctuation of cerebral blood flow, such as blood flow in the parietal lobe. By measuring this, we can estimate the amount of activity, which is the amount of sensory information processed by the brain. The second state estimation unit 13 acquires information about the amount of cerebral blood flow from the acquisition unit 11, and estimates the amount of cerebral blood flow. and estimating the amount of brain activity based on the amount of brain activity, and determining the user's own awareness of the first state based on the amount of brain activity. Specifically, the second condition estimation unit 13 estimates the symptoms when the amount of cerebral blood flow reaches or exceeds a predetermined value. In this case, it is assumed that the user is in an active state where the amount of processing of his / her own sensory information is large, and The second state estimation unit 13 estimates that the person is aware that they are feeling a lack of consciousness. If is smaller than a predetermined value, the subject is in a calm and absent-minded state, with less processing of their own sensory information. It is estimated that the user is not aware that he or she is feeling stressed.

[0024] The second state estimation unit 13 acquires the frequency of the brain waves from the acquisition unit 11 and estimates the frequency of the brain waves such as alpha waves and beta waves. The system estimates the amount of brain activity based on the frequency of brain waves, and determines whether the user is experiencing stress. It may be possible to estimate whether or not the person is aware of the above.

[0025] Next, the second state estimation unit 13 estimates a state in which the user is aware that he or she is feeling stressed. If it is estimated that the driver is in a resting state (YES in step S105), the driving support unit 14 prompts the driver to take a rest. The driving support unit 14 notifies the user of the alert (step S106). Output relaxing healing music, suggest a break, and be gentle. The system provides support to users by minimizing route guidance and other tasks.

[0026] On the other hand, the second state estimation unit 13 may detect that the user is not aware that he or she is feeling stressed. If it is determined that the driver has reached the threshold (NO in step S105), the driving support unit 14 issues an alarm that strongly urges the driver to take a rest. By doing so, the user is prevented from lowering his / her attention. Improve what users do to stop driving. For example, The support unit 14 plays music with a good tempo to encourage the user to become aware of his or her own condition. Outputting alerts, and changing the frequency, volume, and wording of alerts to more strongly suggest breaks In order to ensure that users do not miss any route guidance, the volume and frequency of navigation guidance will be increased. Here, in step S107, the driving support unit 14 determines whether the accident is likely to occur more frequently than in step S106. As this is highly likely, users will be strongly warned.

[0027] Furthermore, when the first state estimation unit 12 estimates that the user is not feeling stressed, If the determination is NO in step S103, the second state estimation unit 13 determines that the user himself / herself is feeling stressed. Whether or not the person is aware that they are not using the device is estimated as the second state (step S108). Here, the second state estimation unit 13 estimates the user's The present invention estimates whether the user is aware that he or she is not feeling stressed.

[0028] The second state estimation unit 13 estimates the state in which the user is aware that he or she is not feeling stressed. If it is estimated that there is a problem (YES in step S109), the driving support unit 14 The driving support unit 1 determines that the vehicle is rotating and does not issue an alert (step S110). 4 may notify the user with an alert informing them that they are driving safely.

[0029] On the other hand, if the second state estimation unit 13 detects that the user is not aware that he or she is not feeling stressed, If it is estimated that the vehicle is in a bad condition (NO in step S109), the driving support unit 14 issues a warning. By doing so, the driving support unit 1 4 is a measure of the driver's safety due to the possibility that the user may be overconfident, distracted, or drunk while driving. Prevents accidents caused by excessive speed.

[0030] Next, with reference to FIG. 4, other driving assistance functions of the driving assistance device 10 according to the first embodiment will be described. In the example described above with reference to FIGS. 2 and 3, the first state estimation unit 12 determines whether the user is The first state was estimated as whether or not the subject was feeling stressed.

[0031] In the example shown in FIG. 4, the first state estimation unit 12 determines whether the user is in a tense state based on the first In this case, the driving support unit 14 estimates the second state estimation unit 13 as a tense state. If it is assumed that the user is aware of this, the driver will be able to drive in a relaxed manner. The driving support unit 14 issues an alert to the driver to let's go. If it is estimated that the user is not aware that they are tense, The driving support unit 14 also issues an alert to the driver to take a short rest to The second state estimation unit 13 estimates that the user is aware that he or she is not nervous. If this happens, an alert will be sent to advise you to continue driving safely as there are no particular problems. The driving support unit 14 determines whether the second state estimation unit 13 is aware that the user is not nervous. If you suspect that you are in a state where you are not safe, please concentrate on driving for a while and continue driving safely. Notify alerts.

[0032] The first state estimation unit 12 estimates whether the user is tired as the first state. In this case, the driving support unit 14 determines whether the second state estimation unit 13 is tired or not by the user. If you suspect that you are aware of this condition, take a rest every now and then to relieve your fatigue. The driving support unit 14 notifies the driver of the fact that the second state estimation unit 13 is tired. If we assume that the user is not aware of this, then fatigue is at its maximum. The driving support unit 14 issues an alert to the driver to take a rest immediately. If we assume that the user is aware that they are not tired, then there are no particular problems. The driving support unit 14 issues an alert to the driver to continue driving safely as there is no problem. The state estimation unit 13 estimates that the user is not aware that he or she is not tired. In this case, an alert will be sent to advise you to focus on driving and continue driving safely.

[0033] The driving assistance device 10 of the present disclosure provides a third display that indicates whether the user is in a state that interferes with driving. Whether or not the user is aware of the first state is estimated as the second state. The driving assistance device 10 then notifies the user of the second state by issuing an alert or other driving assistance function. In other words, the driving support device 10 changes the state of the driver when the driver himself is aware of the state of the driver. Being able to estimate whether or not the vehicle is moving can improve the accuracy of driving assistance.

[0034] The present invention is not limited to the above-described embodiment, and may be modified as appropriate within the scope of the invention. It is possible to change it.

[0035] <Hardware configuration> Next, referring to FIG. 5, the hardware of the computer 1000 related to the driving support device 10 will be described. An example of the configuration will be described. In FIG. 5, a computer 1000 includes a processor 1001 and a memory. The processor 1001 is, for example, a microprocessor, an MPU, It may be a Micro Processing Unit (Micro Processing Unit) or a CPU (Central Processing Unit). The processor 1001 may include multiple processors. The memory 1002 is a volatile memory. The memory 1002 is configured by a combination of a processor and a non-volatile memory. 1001. In this case, the processor 100 1 can access the memory 1002 via an I / O interface (not shown). good.

[0036] Furthermore, each device in the above-described embodiment may be implemented as hardware or software, or both. and may be composed of a single piece of hardware or software, The device may be configured from multiple pieces of hardware or software. The functions (processing) of the device may be realized by a computer. The memory 100 stores a program for carrying out the driving support method according to the embodiment. 2 may be implemented by executing a program stored in the processor 1001.

[0037] These programs may be stored on various types of non-transitory computer-readable media. stored on a computer-readable medium and can be supplied to a computer. Non-transitory computer-readable media can include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media ( (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., optical magnetic disk), CD-ROM (Read Only Memory), CD-R, CD-R / W, semiconductor memory (e.g., mask ROM, PROM (Programmable ROM), EPROM) (Erasable PROM), flash ROM, and RAM (random access memory). The program may be stored on various types of transitory computer-readable media. The information may be provided to a computer on a computer-readable medium. Examples of computer-readable media include electrical signals, optical signals, and electromagnetic waves. The program is transmitted to a computer via a wired communication path such as an electric wire or an optical fiber, or via a wireless communication path. can be supplied to the computer. [Explanation of symbols]

[0038] 10 Driving assistance device (device) 11 Acquisition Department 12 First state estimation unit 13 Second state estimation unit 14 Driving Assistance Department 1000 computers 1001 processor 1002 memory

Claims

1. Acquire information on the activity of the parietal lobe of the user's brain, calculating a processing amount of sensory information including at least one of bodily sensation, visual sensation, and auditory sensation of the user based on the information on the activity amount; estimating a subjective state of the user regarding the state of the user from the obtained amount of processing of the sensory information; determining a degree of risk to the user's driving based on the state of awareness; Device.

2. The information on the activity amount of the parietal lobe is at least one of the amount of cerebral blood flow and the hemoglobin value of the parietal lobe.

10. The apparatus of claim 1.

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