Driver anti-drowsiness awakening system based on artificial intelligence

Abstract

The invention provides a driver anti-drowsiness wake-up system based on artificial intelligence, and the system collects the face video of a driver according to the collection frequency, carries out the preliminary recognition of the face video, obtains a face image which may be fatigue driving, achieves the obtaining and preliminary recognition of the face condition of the driver during driving, and improves the driving safety. The method comprises the steps of providing a data basis for recognition of fatigue driving, performing facial expression and facial behavior recognition on a facial image based on artificial intelligence, judging whether a driver is in fatigue driving or not according to a recognition result, performing recognition through artificial intelligence to improve recognition accuracy, and determining a coordinate area of a mouth and nose area of the driver after determining that the driver is in fatigue driving. And the refreshing liquid is sprayed to the coordinate area, so that the operation of accurately spraying the refreshing liquid to the driver is realized, and the driver is effectively awakened.

Description

Technical Field

[0001] The present invention relates to the field of vehicle driving technology, and in particular to an artificial intelligence-based driver anti-drowsiness awakening system. Background Art

[0002] After long-term continuous driving, drivers will experience physiological and psychological disorders, objectively leading to a decline in driving skills. Drivers also have poor or insufficient sleep quality and are prone to fatigue after long hours of driving. The hazards of fatigue driving are as follows:

[0003] Reduced judgment ability: When a driver is fatigued, his judgment ability will be significantly reduced, making him prone to operational errors.

[0004] Slow reaction: The reaction time is prolonged, and it is difficult to respond quickly and correctly to emergencies.

[0005] Increased operational errors: When slightly fatigued, gear shifting is untimely and inaccurate; when moderately fatigued, operating movements are sluggish or even forgotten; when severely fatigued, subconscious operations or short-term sleep may occur.

[0006] The existing fatigue driving recognition device has the following technical problems: the recognition accuracy is not high, and the driver cannot be effectively awakened after fatigue driving is recognized. Summary of the invention

[0007] The present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, which is used to solve the problems raised in the background technology.

[0008] An artificial intelligence-based driver anti-drowsiness awakening system, comprising:

[0009] A video acquisition module is used to acquire facial videos of the driver at an acquisition frequency, and to perform preliminary recognition on the facial videos to obtain facial images that may indicate fatigue driving;

[0010] An image recognition module, used to recognize facial expressions and facial behaviors of the facial image based on artificial intelligence, and determine whether the driver is driving fatigued according to the recognition result;

[0011] The servo-controlled injection module is used to determine the coordinate area of ​​the driver's mouth and nose area and inject refreshing liquid to the coordinate area after determining that the driver is driving fatigued.

[0012] Preferably, it also includes: a trigger module, which is used to trigger the servo control injection module to work when it is determined that the driver is driving fatigued;

[0013] The trigger module comprises:

[0014] A determination unit, used to use the determination of driver fatigue driving as a start instruction of the trigger mechanism, and use the control instruction of the injection action as a trigger action of the trigger mechanism;

[0015] A synchronization unit, used to establish a synchronization execution strategy between the start instruction and the trigger action, and to establish a trigger strategy based on the synchronization execution strategy and the trigger mechanism;

[0016] The execution unit realizes the immediate execution of the injection action of the servo-controlled injection module based on the trigger strategy after determining that the driver is driving fatigued.

[0017] Preferably, the video acquisition module includes:

[0018] A frequency determination unit, configured to set a collection frequency determination strategy based on a correspondence between a driver's historical fatigue curve and an injection action, obtain a latest fatigue curve of the driver within a preset time period from the historical fatigue curve, and determine a collection frequency based on the latest fatigue curve in combination with the collection frequency determination strategy;

[0019] A collection unit, used for collecting the driver's facial video at the collection frequency by using a camera aimed at the driving area;

[0020] The preliminary processing unit is used to perform preliminary recognition on the facial video based on an image comparison method, and to capture facial images that may be fatigue driving from the facial video.

[0021] Preferably, the preliminary processing unit comprises:

[0022] An image acquisition unit is used to acquire an image frame set in a facial video based on a preset sampling frequency, perform image comparison on adjacent image frames in the image frame set, obtain image differences, use image frames whose image differences are greater than a preset difference as target image frames, and obtain a target image frame set with a preset number of frames centered on the target image frame from the image frame set;

[0023] The image selection unit is used to perform a preliminary comparison between the target image frame set and a pre-acquired standard image of a driver driving fatigue to obtain image similarity, and obtain an image frame with an image similarity greater than a preset similarity as a facial image that may be of a driver driving fatigue.

[0024] Preferably, the image recognition module includes:

[0025] A model building unit, used to train an artificial intelligence model based on a large number of facial images with annotated expressions, and based on a large number of facial images with standard facial features during fatigue driving, and to build a facial recognition model based on the expression and facial features training results;

[0026] An image recognition unit, used to input the facial image into a facial recognition model, recognize each facial feature area, determine whether the expression feature of each facial feature is a fatigue feature, and determine whether the facial image is a fatigue driving image based on the facial recognition model;

[0027] A probability determination unit, used to determine a probability value of the driver being fatigued driving based on the fatigue characteristics and the fatigue driving image;

[0028] The fatigue judgment unit is used to determine whether the driver is driving fatigued based on the probability value in combination with the vehicle state information and the driving environment information.

[0029] Preferably, the probability determination unit includes:

[0030] An evaluation unit, used to obtain the similarity between each facial feature in the fatigue driving image and the corresponding fatigue feature, and determine the weight of each facial feature based on the influence of the facial features on fatigue driving, and obtain a fatigue evaluation value for the fatigue driving image based on the facial feature weight of the facial feature and the similarity with the corresponding fatigue feature;

[0031] The conversion unit is used to convert the fatigue evaluation value of the fatigue driving image into a probability value of the driver being fatigued driving.

[0032] Preferably, the fatigue judgment unit includes:

[0033] An information acquisition unit, used to acquire vehicle status information and driving environment information within a time period corresponding to the fatigue driving image;

[0034] A feature acquisition unit, used to determine the vehicle speed change feature and the vehicle driving direction feature from the vehicle state information, and to acquire the driving road condition feature and other vehicle features from the driving environment information;

[0035] a feature analysis unit, configured to obtain a first matching degree between the vehicle speed change feature and the driving road condition feature and other vehicle features, obtain a second matching degree between the vehicle driving direction feature and the driving road condition feature and other vehicle features, and obtain a first change value of the vehicle speed change feature and obtain a second change value of the vehicle driving direction feature;

[0036] a consciousness evaluation unit, configured to determine a comprehensive matching degree between the vehicle state and the driving environment based on the first matching degree and the second matching degree, and determine a consciousness evaluation value of the driver based on the first change value and the second change value in combination with driving environment information;

[0037] a standard determination unit, for determining the degree of standardization of the driver's operation based on the comprehensive matching degree and the awareness evaluation value;

[0038] a habit analysis unit, configured to obtain historical driving data of the driver, obtain relevant driving data related to the driving environment information from the historical driving habits, determine the driving habits of the driver based on the relevant driving data, and determine a habit matching degree between the driver's current driving operation and the driving habits based on the vehicle state information;

[0039] The fatigue judgment unit is used to determine the driving safety level of the driver based on the degree of operation specifications and the degree of habit matching, and to weight the probability value based on the driving safety level to obtain a target probability value. When the target probability value is greater than a preset probability value, it is determined that the driver is driving fatigued. Otherwise, it is determined that the driver may be driving fatigued and further monitoring is required.

[0040] Preferably, the servo-controlled injection module comprises:

[0041] An area determination unit, for determining a coordinate area of ​​a driver's mouth and nose area based on a facial image of the driver after determining that the driver is driving fatigued;

[0042] The spraying unit is used for, after receiving the control spray start, determining the spray action instruction based on the coordinate area, and spraying the refreshing liquid to the coordinate area according to the spray action instruction.

[0043] Preferably, the area determination unit includes:

[0044] A marking unit, used for retrieving a standard driving area after determining that the driver is driving fatigued, and marking the coordinates of the standard driving area to obtain a coordinate driving area;

[0045] A matching unit, used to obtain edge environment features from the driver's facial image, match the edge environment features with the coordinate driving area, determine the coordinate value of the edge environment in the coordinate driving area, and obtain a matching result;

[0046] A mapping unit is used to map the facial image to a coordinate driving area based on the matching result, and determine the coordinate area of ​​the driver's mouth and nose area according to the mapping result.

[0047] Preferably, the injection unit comprises:

[0048] a position determination unit, for determining the relative position of the driver's mouth and nose and the injection port based on the coordinate area and the position information of the injection port after receiving the control injection start;

[0049] The instruction generating unit is used to determine the injection direction of the injection port based on the relative position, and generate an injection action instruction in combination with a preset injection amount and injection speed.

[0050] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0051] By collecting the driver's facial video at a collection frequency and performing preliminary recognition on the facial video, a facial image that may be of fatigue driving is obtained, so as to obtain and preliminarily recognize the driver's facial condition while driving, provide a data basis for the recognition of fatigue driving, perform facial expression and facial behavior recognition on the facial image based on artificial intelligence, judge whether the driver is fatigued according to the recognition result, improve the recognition accuracy through artificial intelligence recognition, and when it is determined that the driver is fatigued, determine the coordinate area of ​​the driver's mouth and nose area, and spray refreshing liquid to the coordinate area, so as to achieve the operation of accurately spraying refreshing liquid on the driver and effectively make the driver sober.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 This is a structural diagram of a driver anti-drowsiness awakening system based on artificial intelligence in an embodiment of the present invention;

[0056] Figure 2 is a structural diagram of the trigger module described in an embodiment of the present invention;

[0057] Figure 3 4 is a structural diagram of the image recognition module described in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] Embodiment 1:

[0060] The embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, such as Figure 1 As shown, including:

[0061] A video acquisition module is used to acquire facial videos of the driver at an acquisition frequency, and to perform preliminary recognition on the facial videos to obtain facial images that may indicate fatigue driving;

[0062] An image recognition module, used to recognize facial expressions and facial behaviors of the facial image based on artificial intelligence, and determine whether the driver is driving fatigued according to the recognition result;

[0063] The servo-controlled injection module is used to determine the coordinate area of ​​the driver's mouth and nose area and inject refreshing liquid to the coordinate area after determining that the driver is driving fatigued.

[0064] In this embodiment, the preliminary recognition of the facial video is specifically to capture the video with large facial expressions or head movement changes as facial images that may indicate fatigue driving.

[0065] In this embodiment, the image recognition module uses embedded artificial intelligence technology to realize the recognition of strolling images.

[0066] In this embodiment, facial expressions include opening the mouth, closing the eyes, etc., and facial behaviors include touching the face, rubbing the eyes, etc.

[0067] The beneficial effects of the above design scheme are: by collecting the driver's facial video at the collection frequency, and performing preliminary recognition on the facial video, a facial image that may be driving fatigued is obtained, the facial condition of the driver while driving is obtained and preliminary recognition is achieved, providing a data basis for the recognition of fatigue driving, facial expressions and facial behaviors of the facial image are recognized based on artificial intelligence, and whether the driver is driving fatigued is determined according to the recognition result. The recognition accuracy is improved through artificial intelligence recognition, and when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined, and refreshing liquid is sprayed to the coordinate area, so as to achieve the operation of accurately spraying refreshing liquid on the driver and effectively make the driver sober.

[0068] Embodiment 2:

[0069] Based on Example 1, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, such as Figure 2 As shown, it also includes: a trigger module, which is used to trigger the servo control injection module to work when it is determined that the driver is driving fatigued;

[0070] The trigger module comprises:

[0071] A determination unit, used to use the determination of driver fatigue driving as a start instruction of the trigger mechanism, and use the control instruction of the injection action as a trigger action of the trigger mechanism;

[0072] A synchronization unit, used to establish a synchronization execution strategy between the start instruction and the trigger action, and to establish a trigger strategy based on the synchronization execution strategy and the trigger mechanism;

[0073] The execution unit realizes the immediate execution of the injection action of the servo-controlled injection module based on the trigger strategy after determining that the driver is driving fatigued.

[0074] The beneficial effects of the above design scheme are: by using the determination of driver fatigue driving as the starting instruction of the trigger mechanism, and the control instruction of the injection action as the trigger action of the trigger mechanism, a synchronous execution strategy between the starting instruction and the trigger action is established, and a trigger strategy is established based on the synchronous execution strategy and the trigger mechanism. Based on the trigger strategy, the injection action of the servo-controlled injection module is immediately executed after it is determined that the driver is driving fatigued, thereby ensuring real-time synchronization between the injection action and the driver's fatigue state, and ensuring that the driver can wake up quickly.

[0075] Embodiment 3:

[0076] Based on Example 1, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, and the video acquisition module includes:

[0077] A frequency determination unit, configured to set a collection frequency determination strategy based on a correspondence between a driver's historical fatigue curve and an injection action, obtain a latest fatigue curve of the driver within a preset time period from the historical fatigue curve, and determine a collection frequency based on the latest fatigue curve in combination with the collection frequency determination strategy;

[0078] A collection unit, used for collecting the driver's facial video at the collection frequency by using a camera aimed at the driving area;

[0079] The preliminary processing unit is used to perform preliminary recognition on the facial video based on an image comparison method, and to capture facial images that may be fatigue driving from the facial video.

[0080] In this embodiment, the acquisition frequency determination strategy is that the more curve segments with injection action in the historical fatigue curve, the higher the corresponding acquisition frequency, and the specific setting value can be reasonably set according to the actual situation.

[0081] In this embodiment, the latest fatigue curve is the latest one at the current time.

[0082] In this embodiment, the image comparison method specifically compares the image of the facial video with a preset image of the driver when fatigued, and makes a judgment based on the similarity.

[0083] The beneficial effects of the above design scheme are: by setting the acquisition frequency determination strategy based on the correspondence between the driver's historical fatigue curve and the injection action, the driver's latest fatigue curve within a preset time period is obtained from the historical fatigue curve, and the acquisition frequency is determined based on the latest fatigue curve in combination with the acquisition frequency determination strategy; the camera aimed at the driving area is used to collect the driver's facial video at the acquisition frequency; based on the image comparison method, the facial video is preliminarily identified, and facial images that may be fatigue driving are intercepted from the facial video, providing a data basis for the identification of fatigue driving.

[0084] Embodiment 4:

[0085] Based on Example 3, an embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the preliminary processing unit includes:

[0086] An image acquisition unit is used to acquire an image frame set in a facial video based on a preset sampling frequency, perform image comparison on adjacent image frames in the image frame set, obtain image differences, use image frames whose image differences are greater than a preset difference as target image frames, and obtain a target image frame set with a preset number of frames centered on the target image frame from the image frame set;

[0087] The image selection unit is used to perform a preliminary comparison between the target image frame set and a pre-acquired standard image of a driver driving fatigue to obtain image similarity, and obtain an image frame with an image similarity greater than a preset similarity as a facial image that may be of a driver driving fatigue.

[0088] In this embodiment, the standard image of driver fatigue driving is acquired in advance from the historical data.

[0089] The beneficial effect of the above design scheme is: by collecting a set of image frames in a facial video based on a preset sampling frequency, performing image comparison on adjacent image frames in the image frame set to obtain image difference, taking image frames with image difference greater than the preset difference as target image frames, obtaining a target image frame set with a preset number of frames centered on the target image frame from the image frame set, performing a preliminary comparison between the target image frame set and a standard image of the driver's fatigue driving obtained in advance to obtain image similarity, obtaining image frames with image similarity greater than the preset similarity as facial images that may be of fatigue driving, ensuring the correctness of screening of facial images that may be of fatigue driving, and providing a data basis for the identification of fatigue driving.

[0090] Embodiment 5:

[0091] Based on Example 1, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, such as Figure 3 As shown, the image recognition module includes:

[0092] A model building unit, used to train an artificial intelligence model based on a large number of facial images with annotated expressions, and based on a large number of facial images with standard facial features during fatigue driving, and to build a facial recognition model based on the expression and facial features training results;

[0093] An image recognition unit, used to input the facial image into a facial recognition model, recognize each facial feature area, determine whether the expression feature of each facial feature is a fatigue feature, and determine whether the facial image is a fatigue driving image based on the facial recognition model;

[0094] A probability determination unit, used to determine a probability value of the driver being fatigued driving based on the fatigue characteristics and the fatigue driving image;

[0095] The fatigue judgment unit is used to determine whether the driver is driving fatigued based on the probability value in combination with the vehicle state information and the driving environment information.

[0096] In this embodiment, the facial expression features include eye features, mouth features, eyebrow features, ear features and nose features.

[0097] In this embodiment, the facial recognition model is used for the recognition of each facial feature and the comprehensive recognition of fatigue driving.

[0098] The beneficial effect of the above design scheme is: by training the artificial intelligence model based on a large number of face images with annotated expressions, and training the artificial intelligence model based on a large number of face images with standard facial features when driving with fatigue, a facial recognition model is established according to the expression and facial features training results, the facial recognition model is established, the facial image is input into the facial recognition model, each facial feature area is identified, it is determined whether the expression feature of each facial feature is a fatigue feature, and whether the facial image is a fatigue driving image is determined based on the facial recognition model, and based on the fatigue feature and the fatigue driving image, the probability value of the driver driving with fatigue is determined, and the recognition of the image from the local to the whole based on artificial intelligence is realized, so as to ensure the accuracy of the probability value of the driver driving with fatigue, and based on the probability value, combined with the vehicle status information and the driving environment information, it is determined whether the driver is driving with fatigue, and further judgment is made in combination with the actual driving situation to ensure the accuracy of the determination of whether the driver is driving with fatigue.

[0099] Embodiment 6:

[0100] Based on Example 5, an embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the probability determination unit includes:

[0101] An evaluation unit, used to obtain the similarity between each facial feature in the fatigue driving image and the corresponding fatigue feature, and determine the weight of each facial feature based on the influence of the facial features on fatigue driving, and obtain a fatigue evaluation value for the fatigue driving image based on the facial feature weight of the facial feature and the similarity with the corresponding fatigue feature;

[0102] The conversion unit is used to convert the fatigue evaluation value of the fatigue driving image into a probability value of the driver being fatigued driving.

[0103] In this embodiment, the larger the fatigue evaluation value is, the larger the probability value that the driver is driving in fatigue.

[0104] In this embodiment, the weight of each facial feature is determined based on the influence of the facial features on fatigue driving, for example, the weight of the mouth is 0.9, the weight of the eyes is 0.9, and the weight of the ears is 0.3.

[0105] The beneficial effect of the above design scheme is: by obtaining the similarity between each facial feature in the fatigue driving image and the corresponding fatigue feature, and determining the weight of each facial feature based on the influence of the facial features on fatigue driving, a fatigue evaluation value for the fatigue driving image is obtained based on the facial feature weights of the facial features and the similarity with the corresponding fatigue feature, and the fatigue evaluation value of the fatigue driving image is converted into a probability value of the driver driving due to fatigue, thereby realizing image recognition from local to overall based on artificial intelligence, and ensuring the accuracy of the probability value of the driver driving due to fatigue.

[0106] Embodiment 7:

[0107] Based on Example 5, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the fatigue judgment unit includes:

[0108] An information acquisition unit, used to acquire vehicle status information and driving environment information within a time period corresponding to the fatigue driving image;

[0109] A feature acquisition unit, used to determine the vehicle speed change feature and the vehicle driving direction feature from the vehicle state information, and to acquire the driving road condition feature and other vehicle features from the driving environment information;

[0110] a feature analysis unit, configured to obtain a first matching degree between the vehicle speed change feature and the driving road condition feature and other vehicle features, obtain a second matching degree between the vehicle driving direction feature and the driving road condition feature and other vehicle features, and obtain a first change value of the vehicle speed change feature and obtain a second change value of the vehicle driving direction feature;

[0111] a consciousness evaluation unit, configured to determine a comprehensive matching degree between the vehicle state and the driving environment based on the first matching degree and the second matching degree, and determine a consciousness evaluation value of the driver based on the first change value and the second change value in combination with driving environment information;

[0112] a standard determination unit, for determining the degree of standardization of the driver's operation based on the comprehensive matching degree and the awareness evaluation value;

[0113] a habit analysis unit, configured to obtain historical driving data of the driver, obtain relevant driving data related to the driving environment information from the historical driving habits, determine the driving habits of the driver based on the relevant driving data, and determine a habit matching degree between the driver's current driving operation and the driving habits based on the vehicle state information;

[0114] The fatigue judgment unit is used to determine the driving safety level of the driver based on the degree of operation specifications and the degree of habit matching, and to weight the probability value based on the driving safety level to obtain a target probability value. When the target probability value is greater than a preset probability value, it is determined that the driver is driving fatigued. Otherwise, it is determined that the driver may be driving fatigued and further monitoring is required.

[0115] In this embodiment, the higher the driving safety level, the smaller the weighting process is applied to the probability value.

[0116] In this embodiment, based on the first change value and the second change value, combined with the driving environment information, the driver's awareness evaluation value is determined. Specifically, when the driving environment information needs to change more, the more the first change value and the second change value match the driving environment information needs to change more, the corresponding awareness evaluation value is higher.

[0117] The beneficial effect of the above design scheme is: based on the probability value, combined with the vehicle status information and driving environment information, it is determined whether the driver is driving fatigued, and further judgment is made in combination with the actual driving situation to ensure the accuracy of the determination of whether the driver is driving fatigued.

[0118] Embodiment 8:

[0119] Based on Example 1, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the servo control injection module comprises:

[0120] An area determination unit, for determining a coordinate area of ​​a driver's mouth and nose area based on a facial image of the driver after determining that the driver is driving fatigued;

[0121] The spraying unit is used for, after receiving the control spray start, determining the spray action instruction based on the coordinate area, and spraying the refreshing liquid to the coordinate area according to the spray action instruction.

[0122] The beneficial effects of the above design scheme are: when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined based on the driver's facial image, when the control injection start is received, the injection action instruction is determined based on the coordinate area, and refreshing liquid is sprayed to the coordinate area according to the injection action instruction; when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined, and refreshing liquid is sprayed to the coordinate area, thereby realizing the operation of accurately spraying refreshing liquid on the driver and effectively making the driver awake.

[0123] Embodiment 9:

[0124] Based on Example 8, the embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the area determination unit includes:

[0125] A marking unit, used for retrieving a standard driving area after determining that the driver is driving fatigued, and marking the coordinates of the standard driving area to obtain a coordinate driving area;

[0126] A matching unit, used to obtain edge environment features from the driver's facial image, match the edge environment features with the coordinate driving area, determine the coordinate value of the edge environment in the coordinate driving area, and obtain a matching result;

[0127] A mapping unit is used to map the facial image to a coordinate driving area based on the matching result, and determine the coordinate area of ​​the driver's mouth and nose area according to the mapping result.

[0128] The beneficial effects of the above design scheme are: when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined based on the driver's facial image, when the control injection start is received, the injection action instruction is determined based on the coordinate area, and refreshing liquid is sprayed to the coordinate area according to the injection action instruction; when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined, and refreshing liquid is sprayed to the coordinate area, thereby realizing the operation of accurately spraying refreshing liquid on the driver and effectively making the driver awake.

[0129] Embodiment 10:

[0130] Based on Example 8, an embodiment of the present invention provides a driver anti-drowsiness awakening system based on artificial intelligence, wherein the injection unit comprises:

[0131] a position determination unit, for determining the relative position of the driver's mouth and nose and the injection port based on the coordinate area and the position information of the injection port after receiving the control injection start;

[0132] The instruction generating unit is used to determine the injection direction of the injection port based on the relative position, and generate an injection action instruction in combination with a preset injection amount and injection speed.

[0133] The beneficial effects of the above design scheme are: when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined based on the driver's facial image, when the control injection start is received, the injection action instruction is determined based on the coordinate area, and refreshing liquid is sprayed to the coordinate area according to the injection action instruction; when it is determined that the driver is driving fatigued, the coordinate area of ​​the driver's mouth and nose area is determined, and refreshing liquid is sprayed to the coordinate area, thereby realizing the operation of accurately spraying refreshing liquid on the driver and effectively making the driver awake.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of this application document and its equivalent technology, the present invention is also intended to include these changes and variations.

Claims

1. A driver anti-drowsiness wake-up system based on artificial intelligence, characterized in that: include: A video acquisition module is used to acquire facial videos of the driver at an acquisition frequency, and to perform preliminary recognition on the facial videos to obtain facial images that may indicate fatigue driving; An image recognition module, used to recognize facial expressions and facial behaviors of the facial image based on artificial intelligence, and determine whether the driver is driving fatigued according to the recognition result; The servo-controlled injection module is used to determine the coordinate area of ​​the driver's mouth and nose area and inject refreshing liquid to the coordinate area after determining that the driver is driving fatigued.

2. The artificial intelligence-based driver anti-drowsiness awakening system according to claim 1, characterized in that: Also includes: A trigger module, used to trigger the servo control injection module to work when it is determined that the driver is driving fatigued; The trigger module comprises: A determination unit, used to use the determination of driver fatigue driving as a start instruction of the trigger mechanism, and use the control instruction of the injection action as a trigger action of the trigger mechanism; A synchronization unit, used to establish a synchronization execution strategy between the start instruction and the trigger action, and to establish a trigger strategy based on the synchronization execution strategy and the trigger mechanism; The execution unit realizes the immediate execution of the injection action of the servo-controlled injection module based on the trigger strategy after determining that the driver is driving fatigued.

3. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 1 is characterized in that: The video acquisition module comprises: A frequency determination unit, configured to set a collection frequency determination strategy based on a correspondence between a driver's historical fatigue curve and an injection action, obtain a latest fatigue curve of the driver within a preset time period from the historical fatigue curve, and determine a collection frequency based on the latest fatigue curve in combination with the collection frequency determination strategy; A collection unit, used for collecting the driver's facial video at the collection frequency by using a camera aimed at the driving area; The preliminary processing unit is used to perform preliminary recognition on the facial video based on an image comparison method, and to capture facial images that may be fatigue driving from the facial video.

4. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 3 is characterized in that: The preliminary processing unit comprises: An image acquisition unit is used to acquire an image frame set in a facial video based on a preset sampling frequency, perform image comparison on adjacent image frames in the image frame set, obtain image differences, use image frames whose image differences are greater than a preset difference as target image frames, and obtain a target image frame set with a preset number of frames centered on the target image frame from the image frame set; The image selection unit is used to perform a preliminary comparison between the target image frame set and a pre-acquired standard image of a driver driving fatigue to obtain image similarity, and obtain an image frame with an image similarity greater than a preset similarity as a facial image that may be of a driver driving fatigue.

5. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 1 is characterized in that: The image recognition module comprises: A model building unit, used to train an artificial intelligence model based on a large number of facial images with annotated expressions, and based on a large number of facial images with standard facial features during fatigue driving, and to build a facial recognition model based on the expression and facial features training results; An image recognition unit, used to input the facial image into a facial recognition model, recognize each facial feature area, determine whether the expression feature of each facial feature is a fatigue feature, and determine whether the facial image is a fatigue driving image based on the facial recognition model; A probability determination unit, used to determine a probability value of the driver being fatigued driving based on the fatigue characteristics and the fatigue driving image; The fatigue judgment unit is used to determine whether the driver is driving fatigued based on the probability value in combination with the vehicle state information and the driving environment information.

6. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 5 is characterized in that: The probability determination unit comprises: An evaluation unit, used to obtain the similarity between each facial feature in the fatigue driving image and the corresponding fatigue feature, and determine the weight of each facial feature based on the influence of the facial features on fatigue driving, and obtain a fatigue evaluation value for the fatigue driving image based on the facial feature weight of the facial feature and the similarity with the corresponding fatigue feature; The conversion unit is used to convert the fatigue evaluation value of the fatigue driving image into a probability value of the driver being fatigued driving.

7. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 5 is characterized in that: The fatigue judgment unit comprises: An information acquisition unit, used to acquire vehicle status information and driving environment information within a time period corresponding to the fatigue driving image; A feature acquisition unit, used to determine the vehicle speed change feature and the vehicle driving direction feature from the vehicle state information, and to acquire the driving road condition feature and other vehicle features from the driving environment information; a feature analysis unit, configured to obtain a first matching degree between the vehicle speed change feature and the driving road condition feature and other vehicle features, obtain a second matching degree between the vehicle driving direction feature and the driving road condition feature and other vehicle features, and obtain a first change value of the vehicle speed change feature and obtain a second change value of the vehicle driving direction feature; a consciousness evaluation unit, configured to determine a comprehensive matching degree between the vehicle state and the driving environment based on the first matching degree and the second matching degree, and determine a consciousness evaluation value of the driver based on the first change value and the second change value in combination with driving environment information; A standard determination unit, used to determine the driver's operation standard degree based on the comprehensive matching degree and the awareness evaluation value; a habit analysis unit, configured to obtain historical driving data of the driver, obtain relevant driving data related to the driving environment information from the historical driving habits, determine the driving habits of the driver based on the relevant driving data, and determine a habit matching degree between the driver's current driving operation and the driving habits based on the vehicle state information; The fatigue judgment unit is used to determine the driving safety level of the driver based on the degree of operation specifications and the degree of habit matching, and to weight the probability value based on the driving safety level to obtain a target probability value. When the target probability value is greater than a preset probability value, it is determined that the driver is driving fatigued. Otherwise, it is determined that the driver may be driving fatigued and further monitoring is required.

8. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 1 is characterized in that: The servo-controlled injection module comprises: An area determination unit, for determining a coordinate area of ​​a driver's mouth and nose area based on a facial image of the driver after determining that the driver is driving fatigued; The spraying unit is used for, after receiving the control spray start, determining the spray action instruction based on the coordinate area, and spraying the refreshing liquid to the coordinate area according to the spray action instruction.

9. The artificial intelligence-based driver anti-drowsiness awakening system according to claim 8, characterized in that: The area determination unit comprises: A marking unit, used for retrieving a standard driving area after determining that the driver is driving fatigued, and marking the coordinates of the standard driving area to obtain a coordinate driving area; A matching unit, used to obtain edge environment features from the driver's facial image, match the edge environment features with the coordinate driving area, determine the coordinate value of the edge environment in the coordinate driving area, and obtain a matching result; A mapping unit is used to map the facial image to a coordinate driving area based on the matching result, and determine the coordinate area of ​​the driver's mouth and nose area according to the mapping result.

10. The driver anti-drowsiness awakening system based on artificial intelligence according to claim 8, characterized in that: The injection unit comprises: a position determination unit, for determining the relative position of the driver's mouth and nose and the injection port based on the coordinate area and the position information of the injection port after receiving the control injection start; The instruction generating unit is used to determine the injection direction of the injection port based on the relative position, and generate an injection action instruction in combination with a preset injection amount and injection speed.

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