Driving fatigue relieving system based on brain-computer interface

Abstract

The invention discloses a driving fatigue relieving system based on a brain-computer interface, and the system comprises an electroencephalogram signal collection module which is used for collecting the electroencephalogram data of a driver in real time; the electroencephalogram signal analysis module is connected to the electroencephalogram signal acquisition module and is used for processing and analyzing the electroencephalogram data received from the electroencephalogram signal acquisition module so as to judge the mental state of the driver; and the execution application module is connected to the electroencephalogram signal analysis module and is used for executing a corresponding auxiliary strategy based on an analysis result of the electroencephalogram signal analysis module. According to the driving fatigue relieving system based on the brain-computer interface, a direct interaction control channel between the brain of the driver and the external equipment is constructed, so that the mental state of the driver is monitored and intervened in real time, and a new technical means is provided for improving driving safety and reducing traffic accident risks.

Description

Technical Field

[0001] The present invention belongs to the technical field of assisted driving, and in particular relates to a driving fatigue relief system based on a brain-computer interface. Background Art

[0002] With the acceleration of the pace of modern society, driving has become an indispensable part of people's daily life. However, long-term continuous driving can easily lead to driving fatigue, which not only affects the physical and mental health of the driver, but also greatly increases the risk of traffic accidents. Traditional methods of relieving driving fatigue, such as regular rest, drinking coffee or listening to music, are effective, but often lack specificity and are difficult to respond to the driver's fatigue state in real time. Therefore, it is particularly important to develop a technology that can monitor the driver's mental state in real time and effectively relieve driving fatigue. Summary of the invention

[0003] The present invention provides a driving fatigue relief system based on a brain-computer interface to solve the above-mentioned technical problems, and specifically adopts the following technical solutions:

[0004] A driving fatigue relief system based on brain-computer interface, comprising:

[0005] EEG signal acquisition module, used to collect the driver's EEG data in real time;

[0006] an EEG signal analysis module, connected to the EEG signal acquisition module, for processing and analyzing the EEG data received from the EEG signal acquisition module to determine the mental state of the driver;

[0007] An execution application module is connected to the EEG analysis module and is used to execute a corresponding auxiliary strategy based on the analysis result of the EEG signal analysis module.

[0008] Furthermore, the EEG signal analysis module comprises:

[0009] A data preprocessing submodule is used to preprocess the received EEG data;

[0010] A feature extraction submodule is used to extract EEG features reflecting the driver's mental state from the preprocessed EEG data;

[0011] The state monitoring submodule is used to output classification results based on the extracted EEG features through the trained classification model.

[0012] Furthermore, the results output by the EEG signal analysis module include: normal wakefulness state, mild fatigue state, moderate fatigue state and severe fatigue state;

[0013] When the result output by the EEG signal analysis module is a normal awake state, the execution module does not perform any operation;

[0014] When the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module executes a first auxiliary strategy;

[0015] When the result output by the electroencephalogram signal analysis module is a severe fatigue state, the execution module executes a second auxiliary strategy.

[0016] Furthermore, the execution module includes a voice playing submodule and a display submodule;

[0017] When the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module starts a mindfulness meditation guidance program, plays a guidance voice through the voice playing submodule, and plays a guidance video through the display submodule to help the driver relieve fatigue;

[0018] The driving fatigue relief system based on brain-computer interface includes:

[0019] A vibration reminder module is used to generate a vibration reminder for the driver;

[0020] When the result output by the EEG signal analysis module is a severe fatigue state, the execution module starts a vibration prompting program, and generates a vibration prompt through the vibration prompting module to remind the driver.

[0021] Further, when the result output by the EEG signal analysis module is a mild fatigue state, the execution module starts a first mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a first volume, and the voice playback submodule and the display submodule perform the guidance task for a first preset time;

[0022] When the result output by the EEG signal analysis module is a moderate fatigue state, the execution module starts a second mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a second volume, the voice playback submodule and the display submodule perform the guidance task for a second preset time, the second volume is greater than the first volume, and the second preset time is greater than the first preset time.

[0023] Further, the execution module includes a driving assistance submodule, and the driving assistance submodule is connected to the vehicle control center;

[0024] When the result output by the EEG signal analysis module is a severe fatigue state and lasts for a preset time, the driving assistance sub-module sends a deceleration command, a warning flash command and a warning command to the vehicle control center. After receiving the deceleration command, the vehicle control center slows down the vehicle, turns on the double flashes and sounds the horn to warn based on the vehicle intelligent driving system.

[0025] Furthermore, the execution module further comprises a lane determination submodule, and the lane determination module is used to determine whether the road on which the vehicle is currently traveling is a highway;

[0026] After the driving assistance submodule sends the deceleration instruction, the warning flash instruction and the warning instruction to the vehicle control center:

[0027] If the lane determination module is used to determine that the road the vehicle is currently traveling on is not a highway, the driving assistance submodule further sends a pull-over parking instruction to the vehicle control center, and the vehicle control center operates the vehicle to pull over and park according to the current road conditions based on the intelligent driving system;

[0028] If the lane determination module is used to determine that the road the vehicle is currently traveling on is a highway, the driving assistance submodule also sends a temporary parking instruction to the vehicle control center. The vehicle control center, based on the intelligent driving system, operates the vehicle to drive into the nearest service area and park according to the current road conditions.

[0029] Furthermore, the execution application module also includes:

[0030] The setting submodule is used for the driver to set the relevant parameters of the mindfulness meditation guidance program and the vibration reminder program.

[0031] Furthermore, the driving fatigue relief system based on brain-computer interface also includes:

[0032] A feedback module, used for providing feedback to the driver on whether the execution strategy of the execution application module is correct or not;

[0033] The EEG signal analysis module reversely adjusts the classification model based on the driver's feedback to improve its classification accuracy.

[0034] Furthermore, the driving fatigue relief system based on brain-computer interface comprises: a headband and a smart mobile device, wherein the headband is worn by a user and is wirelessly connected to the smart mobile device;

[0035] The EEG signal acquisition module is arranged in the headband;

[0036] The intelligent mobile device comprises the EEG signal analysis module and the application execution module.

[0037] The benefit of the present invention lies in the fact that the driving fatigue relief system based on brain-computer interface provided realizes real-time monitoring and intervention of the driver's mental state by constructing a direct interactive control channel between the driver's brain and external equipment, providing a new technical means for improving driving safety and reducing the risk of traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 It is a schematic diagram of the driving fatigue relief system based on brain-computer interface of the present application. DETAILED DESCRIPTION

[0040] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0041] like Figure 1 The figure shows a driving fatigue relief system based on brain-computer interface of the present application, which includes: an EEG signal acquisition module, an EEG signal analysis module and an execution application module.

[0042] The EEG signal acquisition module is used to collect the driver's EEG data in real time. The EEG signal analysis module is connected to the EEG signal acquisition module and is used to process and analyze the EEG data received from the EEG signal acquisition module to determine the driver's mental state. The execution application module is connected to the EEG signal analysis module and is used to execute the corresponding auxiliary strategy based on the analysis results of the EEG signal analysis module.

[0043] Specifically, the driving fatigue relief system based on brain-computer interface includes: a headband and a smart mobile device. The headband is worn by the user and is wirelessly connected to the smart mobile device. The headband is provided with an EEG signal acquisition module. The smart mobile device includes an EEG signal analysis module and an execution application module.

[0044] The EEG signal acquisition module adopts a wearable design, specifically a headband with electrodes. The electrodes are attached to the driver's forehead skin to collect brain waves in a non-invasive way. The headband has a built-in signal amplifier that can amplify weak brain wave signals to a detectable level, and transmit the amplified brain wave data to smart mobile devices such as mobile phones through wireless communication technologies such as Bluetooth or Wi-Fi.

[0045] In order to improve the accuracy and stability of EEG acquisition, the EEG signal acquisition module of this application adopts the following technical improvements.

[0046] Adaptive electrode design: The electrodes are made of soft, skin-friendly material and can adapt to the head shapes of different drivers, reducing signal interference caused by loose or displaced electrodes.

[0047] Noise reduction processing: During the signal amplification process, advanced digital filtering technology is used to effectively filter out environmental noise and electromyographic interference, and improve the signal-to-noise ratio of brain wave signals.

[0048] Real-time calibration: The system has a built-in real-time calibration mechanism that can regularly detect electrode contact and signal quality to ensure that the collected EEG data is accurate.

[0049] The EEG signal analysis module is responsible for processing and analyzing the data received from the EEG signal acquisition module. This module uses advanced signal processing algorithms and machine learning models to extract and classify brain waves to determine the driver's mental state.

[0050] Specifically, the EEG signal analysis module includes: a data preprocessing submodule, a feature extraction submodule and a state monitoring submodule.

[0051] The data preprocessing submodule is used to preprocess the received EEG data, such as artifact removal, re-referencing, segmentation and baseline correction. The feature extraction submodule is used to extract EEG features that reflect the driver's mental state from the preprocessed EEG data, such as the power spectral density of α waves, β waves, θ waves and δ waves. The state monitoring submodule is used to output classification results based on the extracted EEG features through a trained classification model. It is understandable that a machine learning model (such as support vector machine, random forest or deep learning network, etc.) can be trained based on a large amount of EEG data under driving fatigue (different levels) and awake state to achieve accurate classification of the driver's mental state.

[0052] In an embodiment of the present application, the results output by the EEG signal analysis module include: normal wakefulness state, mild fatigue state, moderate fatigue state and severe fatigue state. When the result output by the EEG signal analysis module is a normal wakefulness state, the execution module does not perform any operation. When the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module executes the first auxiliary strategy. When the result output by the EEG signal analysis module is a severe fatigue state, the execution module executes the second auxiliary strategy.

[0053] In an implementation manner of the present application, the execution module includes a voice playing submodule and a display submodule.

[0054] Specifically, when the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module starts the mindfulness meditation guidance program, plays the guidance voice through the voice playback submodule, and plays the guidance video through the display submodule to help the driver to practice mindfulness meditation such as deep breathing, relaxing the body, and concentrating attention, so as to relieve fatigue and improve attention. Mindfulness meditation is a widely recognized psychological adjustment method that can effectively reduce stress, improve concentration and emotional management ability.

[0055] As a preferred embodiment, when the result output by the EEG signal analysis module is a mild fatigue state, the execution module starts the first mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a first volume, and the voice playback submodule and the display submodule perform the guidance task for a first preset time. When the result output by the EEG signal analysis module is a moderate fatigue state, the execution module starts the second mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a second volume, and the voice playback submodule and the display submodule perform the guidance task for a second preset time, the second volume is greater than the first volume, and the second preset time is greater than the first preset time. In this way, for mild fatigue and moderate fatigue states, differentiated coping strategies are adopted.

[0056] In an embodiment of the present application, a driving fatigue relief system based on a brain-computer interface includes a vibration reminder module. The vibration reminder module is used to generate a vibration reminder for the driver. When the result output by the EEG signal analysis module is a severe fatigue state, the execution module starts the vibration reminder program, and generates a vibration reminder through the vibration reminder module to remind the driver. Preferably, the vibration reminder module is a vibration sensor provided on the headband, and when the system detects that the driver is in a severe fatigue state, it reminds the driver by vibration.

[0057] In the implementation of the present application, the execution module includes a driving assistance submodule, and the driving assistance submodule is connected to the vehicle control center. When the result output by the EEG signal analysis module is a severe fatigue state and lasts for a preset time, the driving assistance submodule sends a deceleration instruction, a warning flash instruction, and a warning instruction to the vehicle control center. After receiving the deceleration instruction, the warning flash instruction, and the warning instruction, the vehicle control center decelerates the vehicle, turns on the double flashes, and sounds the horn for warning based on the vehicle intelligent driving system.

[0058] It is understandable that when the driver is in a state of severe fatigue and the vibration prompt cannot make the driver out of this state for a period of time, the driving assistance submodule is used to intervene in the driving state of the vehicle. Specifically, the driving speed is reduced to avoid major accidents. The double flash and horn warning are turned on to remind other vehicles on the road to pay attention to avoid.

[0059] In an embodiment of the present application, the execution module further includes a lane determination submodule, and the lane determination module is used to determine whether the road on which the vehicle is currently traveling is a highway.

[0060] After the driving assistance submodule sends the deceleration command, warning flash command and warning command to the vehicle control center:

[0061] If the lane determination module is used to determine that the road the vehicle is currently traveling on is not a highway, the driving assistance submodule also sends a pull-over instruction to the vehicle control center. The vehicle control center operates the vehicle to pull over according to the current road conditions based on the intelligent driving system.

[0062] If the lane determination module is used to determine that the road the vehicle is currently traveling on is a highway, the driving assistance submodule also sends a temporary parking instruction to the vehicle control center. The vehicle control center, based on the intelligent driving system, operates the vehicle to drive into the nearest service area and park according to the current road conditions.

[0063] It is understandable that when the driver is unable to get out of the state of severe fatigue, simply slowing down the vehicle cannot completely eliminate the safety hazard. The best way is to stop the vehicle. However, in some cases, such as when driving at high speed, it is not possible to stop the vehicle in a short time.

[0064] Therefore, in this application, after the driving assistance submodule sends the deceleration command, warning flash command and warning command to the vehicle control center, if the vehicle is not traveling on the highway, a control command can be sent, and the vehicle's intelligent driving system can automatically park the vehicle gradually on the roadside according to the command. If the vehicle is traveling on the highway at this time, a control command can be sent, and the vehicle's intelligent driving system can automatically drive the vehicle into the nearest service area and stop according to the command.

[0065] Preferably, after the vehicle stops, the windows are automatically lowered to a certain height to prevent harm to people caused by air pollution inside the vehicle.

[0066] In the implementation mode of the present application, the execution application module further includes: a setting submodule. The setting submodule is used for the driver to set the relevant parameters of the mindfulness meditation guidance program and the vibration prompt program, such as the guidance voice, guidance video, background music, duration, vibration degree, vibration duration, etc.

[0067] In an embodiment of the present application, the driving fatigue relief system based on the brain-computer interface further comprises: a feedback module. The feedback module is used for providing feedback to the driver on whether the execution strategy of the execution application module is correct or not. The EEG signal analysis module reversely adjusts the classification model based on the driver's feedback to improve its classification accuracy.

[0068] It is understandable that, for each driver, the state actually reflected by their EEG signals is different. In the present application, in order to make the analysis results of the EEG signal analysis module more suitable for the current driver, the driver can provide feedback on the analysis results of the EEG signal analysis module. For example, the analysis results of the current EEG signal analysis module show that the driver is in a state of mild fatigue, but the driver judges that he is in a state of severe fatigue. The driver can then provide feedback on his current true state and correct the classification results of the EEG signal analysis module. In this way, the EEG signal analysis module can reversely adjust the classification model based on the driver's feedback, so that the classification results of the classification model are more suitable for the current driver.

[0069] As a preferred embodiment, the brain-computer interface-based driving fatigue relief system of the present application can display the driver's mental state and the progress of the mindfulness meditation guidance program, as well as the triggering status and duration of the driving assistance function on the smart device. It can also report the operating status and intervention effect of the system to the driver through voice prompts, such as "You are currently in a state of mild fatigue, please practice mindfulness meditation".

[0070] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A driving fatigue relief system based on brain-computer interface, characterized in that: Include: EEG signal acquisition module, used to collect the driver's EEG data in real time; an EEG signal analysis module, connected to the EEG signal acquisition module, for processing and analyzing the EEG data received from the EEG signal acquisition module to determine the mental state of the driver; An execution application module is connected to the EEG signal analysis module and is used to execute a corresponding auxiliary strategy based on the analysis result of the EEG signal analysis module.

2. The driving fatigue relief system based on brain-computer interface according to claim 1 is characterized in that: The EEG signal analysis module comprises: A data preprocessing submodule is used to preprocess the received EEG data; A feature extraction submodule is used to extract EEG features reflecting the driver's mental state from the preprocessed EEG data; The state monitoring submodule is used to output classification results based on the extracted EEG features through the trained classification model.

3. The driving fatigue relief system based on brain-computer interface according to claim 2 is characterized in that: The results output by the EEG signal analysis module include: normal awake state, mild fatigue state, moderate fatigue state and severe fatigue state; When the result output by the EEG signal analysis module is a normal awake state, the execution module does not perform any operation; When the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module executes a first auxiliary strategy; When the result output by the electroencephalogram signal analysis module is a severe fatigue state, the execution module executes a second auxiliary strategy.

4. The driving fatigue relief system based on brain-computer interface according to claim 3 is characterized in that: The execution module includes a voice playing submodule and a display submodule; When the result output by the EEG signal analysis module is a mild fatigue state or a moderate fatigue state, the execution module starts a mindfulness meditation guidance program, plays a guidance voice through the voice playing submodule, and plays a guidance video through the display submodule to help the driver relieve fatigue; The driving fatigue relief system based on brain-computer interface includes: A vibration reminder module is used to generate a vibration reminder for the driver; When the result output by the EEG signal analysis module is a severe fatigue state, the execution module starts a vibration prompting program, and generates a vibration prompt through the vibration prompting module to remind the driver.

5. The driving fatigue relief system based on brain-computer interface according to claim 4 is characterized in that: When the result output by the EEG signal analysis module is a mild fatigue state, the execution module starts a first mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a first volume, and the voice playback submodule and the display submodule perform the guidance task for a first preset time; When the result output by the EEG signal analysis module is a moderate fatigue state, the execution module starts a second mindfulness meditation guidance program, the voice playback submodule performs voice guidance at a second volume, the voice playback submodule and the display submodule perform the guidance task for a second preset time, the second volume is greater than the first volume, and the second preset time is greater than the first preset time.

6. The driving fatigue relief system based on brain-computer interface according to claim 5 is characterized in that: The execution module includes a driving assistance submodule, and the driving assistance submodule is connected to the vehicle control center; When the result output by the EEG signal analysis module is a severe fatigue state and lasts for a preset time, the driving assistance sub-module sends a deceleration command, a warning flash command and a warning command to the vehicle control center. After receiving the deceleration command, the vehicle control center slows down the vehicle, turns on the double flashes and sounds the horn to warn based on the vehicle intelligent driving system.

7. The driving fatigue relief system based on brain-computer interface according to claim 6 is characterized in that: The execution module further comprises a lane determination submodule, wherein the lane determination submodule is used to determine whether the road on which the vehicle is currently traveling is a highway; After the driving assistance submodule sends the deceleration instruction, the warning flash instruction and the warning instruction to the vehicle control center: If the lane determination module is used to determine that the road the vehicle is currently traveling on is not a highway, the driving assistance submodule further sends a pull-over parking instruction to the vehicle control center, and the vehicle control center operates the vehicle to pull over and park according to the current road conditions based on the intelligent driving system; If the lane determination module is used to determine that the road the vehicle is currently traveling on is a highway, the driving assistance submodule also sends a temporary parking instruction to the vehicle control center. The vehicle control center, based on the intelligent driving system, operates the vehicle to drive into the nearest service area and park according to the current road conditions.

8. The driving fatigue relief system based on brain-computer interface according to claim 4 is characterized in that: The execution application module also includes: The setting submodule is used for the driver to set the relevant parameters of the mindfulness meditation guidance program and the vibration reminder program.

9. The driving fatigue relief system based on brain-computer interface according to claim 1 is characterized in that: The driving fatigue relief system based on brain-computer interface also includes: A feedback module, used for providing feedback to the driver on whether the execution strategy of the execution application module is correct or not; The EEG signal analysis module reversely adjusts the classification model based on the driver's feedback to improve its classification accuracy.

10. The driving fatigue relief system based on brain-computer interface according to claim 1 is characterized in that: The driving fatigue relief system based on brain-computer interface comprises: a headband and a smart mobile device, wherein the headband is worn by a user and is wirelessly connected to the smart mobile device; The EEG signal acquisition module is arranged in the headband; The intelligent mobile device comprises the EEG signal analysis module and the application execution module.

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