A method and system for taking over autonomous driving in vehicles

Abstract

This invention provides a method and system for taking over autonomous driving in vehicles. The method includes the following steps: identifying the driver's fatigue level based on real-time acquired facial images; outputting corresponding reminders based on the driver's fatigue level, and selecting between maintaining vehicle operation through autonomous driving or controlling the vehicle to pull over through autonomous driving. This invention can provide safety reminders while automatically activating different levels of autonomous driving functions, effectively reducing vehicle safety accidents caused by sudden health issues of the driver.

Description

Technical Field

[0001] This invention belongs to the field of automotive safe driving technology, specifically relating to a method and system for autonomous driving takeover in vehicles. Background Technology

[0002] A significant portion of car accidents are caused by driver fatigue, dizziness, or other physical factors. As people's lives become increasingly sophisticated and social engagements rise, the occurrence of collisions due to driver physical limitations has also increased dramatically. Active safety, a crucial component of vehicle safety, has always been a key focus for automakers. Currently, methods for improving active safety are constantly evolving, and major automakers are continuously optimizing their solutions to enhance active safety while significantly reducing the frequency of accidents, particularly those involving drivers who are physically unable to drive normally.

[0003] The current strategy involves using a driver health monitoring system to measure drivers' blood pressure, heart rate, and other physical parameters. This system analyzes the parameters to alert drivers if they are unwell and unable to drive. While this method does alert drivers, it only applies when the driver is conscious and capable of pulling the vehicle to a safe location, thus preventing accidents to some extent. If a driver loses consciousness due to fatigue or sudden fainting while driving, even with alerts, a collision cannot be avoided if the driver remains unconscious. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a method and system for taking over autonomous driving in vehicles. This system can provide safety reminders while automatically activating autonomous driving functions at different levels, effectively reducing vehicle safety accidents caused by sudden health conditions of the driver.

[0005] The technical solution adopted in this invention is: a method for taking over autonomous driving in a vehicle, comprising the following steps:

[0006] The driver's fatigue level is identified based on real-time collected facial images.

[0007] Based on the driver's level of fatigue, corresponding reminders are issued, and the driver can choose to maintain vehicle operation through autonomous driving or control the vehicle to pull over to the side of the road through autonomous driving.

[0008] In the above technical solution, the process of recognizing the driver's fatigue level based on the real-time collected driver's facial image includes: locating the lips in the driver's facial image in real time, and determining the driver's fatigue level based on the frequency of lip changes exceeding a set value.

[0009] In the above technical solution, the process of recognizing the driver's fatigue level based on the real-time collected driver's facial image includes: locating the eyes in the driver's facial image in real time, and determining the driver's fatigue level based on the frequency of changes in the eyes within a set range.

[0010] In the above technical solution, the lip change amplitude is the change in distance between the upper and lower edges of the mouth at two adjacent sampling times; the set value is 1.5 times the distance between the upper and lower edges of the mouth at the previous sampling time in two adjacent sampling times; if the frequency of the lip change amplitude being greater than the set value is greater than the corresponding set standard, the driver's physical condition is marked as low and a corresponding prompt sound is output.

[0011] In the above technical solution, the eye variation amplitude is defined as the change in distance between the upper and lower edges of the eye at two adjacent sampling times; the set range includes a first set range and a second set range.

[0012] The minimum value of the first set range is 0.25 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; the maximum value of the first set range is 0.75 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the first set range is greater than the corresponding set standard, the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and automatic driving will be activated to maintain vehicle driving.

[0013] The minimum value of the second set range is 0; the maximum value of the first set range is 0.25 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the second set range is greater than the corresponding set standard, the driver's physical condition will be marked as very low, a corresponding prompt sound will be output, and the automatic driving control will be activated to pull the vehicle to the side of the road.

[0014] In the above technical solution, if the driver's physical condition is marked as low, and the frequency of lip changes exceeding the set value within a set time period exceeds the corresponding set standard, then the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and automatic driving will be activated to maintain vehicle operation.

[0015] In the above technical solution, if the driver's physical condition is marked as low, and the frequency of eye changes within a first set range is greater than the corresponding set standard within a set time period, then the driver's physical condition is marked as very low, a corresponding prompt sound is output, and the autonomous driving control system is activated to pull the vehicle to the side of the road.

[0016] In the above technical solution, by locating key points in the driver's face image, several key points located at the upper and lower edges of the mouth are determined respectively; the distance between the corresponding symmetrical key points at the upper and lower edges of the mouth is calculated; and the average distance between the upper and lower edges of the mouth is obtained by averaging the calculated distances between all symmetrical key points.

[0017] In the above technical solution, by locating key points in the driver's face image, several key points located at the upper and lower edges of the eyes are determined; the distance between the corresponding symmetrical key points at the upper and lower edges of the glasses is calculated; and the average distance between the upper and lower edges of the eyes is obtained by averaging the calculated distances between all symmetrical key points.

[0018] This invention provides a vehicle autonomous driving takeover system for implementing the vehicle autonomous driving takeover method described in the above technical solution, comprising: a fatigue level recognition module and an autonomous driving takeover module;

[0019] The fatigue level recognition module is used to identify the driver's fatigue level based on real-time captured driver facial images;

[0020] The autonomous driving takeover module is used to output corresponding reminders based on the driver's fatigue level, and to choose between maintaining vehicle driving through autonomous driving or controlling the vehicle to pull over to the side of the road through autonomous driving.

[0021] This invention provides a vehicle, the vehicle comprising:

[0022] One or more processors;

[0023] Memory, used to store one or more programs;

[0024] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle autonomous driving takeover method described in the above technical solution.

[0025] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle autonomous driving takeover method described above.

[0026] The beneficial effects of this invention are as follows: This invention classifies and determines the driver's physical condition and takes corresponding countermeasures based on different levels. With the assistance of this enhanced active safety solution, on the one hand, it effectively avoids the risk of immediate activation of autonomous driving due to driver physical problems, even when the driver is capable of normal driving. In such cases, the autonomous driving function may interfere with and affect the driver's normal driving, causing misjudgment of the vehicle's condition and leading to unnecessary car accidents. On the other hand, it also addresses situations where the driver's condition is extremely poor and unable to drive normally, posing a significant safety hazard. By prioritizing the autonomous driving function over the driver, it promptly takes over driving control, avoiding the risk of car accidents caused by driver errors due to unconsciousness, fainting, or confusion. This method dynamically monitors the driver's physical condition by real-time acquisition of facial images and identification of fatigue levels, thereby taking corresponding measures in real time. This not only improves driver safety but also effectively avoids traffic accidents caused by fatigued driving, enhancing the intelligence and reliability of the autonomous driving system.

[0027] Furthermore, this invention identifies fatigue levels by the frequency of lip movements exceeding a set value. This method, combined with subtle changes in the driver's lips, can accurately determine the driver's fatigue level, especially in the face of subtle changes, thereby improving the accuracy of fatigue detection.

[0028] Furthermore, this invention identifies fatigue by monitoring changes in the driver's eye movements, thus increasing the dimensions of the assessment. Eye changes are directly related to the driver's attention and alertness; therefore, combining this with changes in lip movements provides a more comprehensive fatigue assessment and reduces misjudgment.

[0029] Furthermore, by calculating the range of lip changes and setting a reasonable threshold for these changes, this invention can accurately reflect the driver's level of fatigue. The set threshold can be adjusted based on multiple factors (such as the distance between the upper and lower edges of the mouth at the sampling time) to ensure the system's sensitivity and accuracy. This helps to issue timely warnings before fatigue driving occurs, ensuring driving safety.

[0030] Furthermore, this invention provides a more detailed identification of fatigued driving by comparing the amplitude of eye changes with a set range. By setting multiple threshold ranges, the system can determine the degree of fatigue in different levels and trigger different response measures, such as maintaining vehicle movement or pulling over, thereby responding more flexibly to different driver conditions.

[0031] Furthermore, when the driver's physical condition is marked as low, this invention can promptly detect further deterioration of the driver's physical condition by monitoring the frequency of lip movements. This dynamic adjustment method enables stricter safety measures to be taken when the driver is continuously fatigued, reducing safety risks.

[0032] Furthermore, if the driver's physical condition deteriorates further, this invention can further confirm the driver's level of fatigue and take emergency measures, such as automatically controlling the vehicle to pull over, by monitoring changes in eye movements. This method ensures driver safety while also preventing accidents and improving road safety.

[0033] Furthermore, by precisely locating key points in a driver's facial image and calculating the distance between symmetrical key points on the upper and lower edges of the mouth, this invention can obtain highly accurate information about mouth changes. This precise measurement improves the accuracy of fatigue level recognition, reduces misjudgments, and provides more accurate judgment criteria for autonomous driving systems.

[0034] Furthermore, similar to measuring lip changes, this invention provides accurate detection of eye fatigue by locating key points of the eyes and calculating the average distance between the upper and lower edges of the eyes. The eyes are a crucial indicator of a driver's concentration; by monitoring changes in the eyes, the degree of driver fatigue can be effectively identified, enhancing the safety and reliability of the entire system. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0036] Figure 2 This is a flowchart of face image data processing in a specific embodiment;

[0037] Figure 3 This is a schematic diagram of key points in a facial image according to a specific embodiment. Detailed Implementation

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments to facilitate a clear understanding of the present invention, but these descriptions do not constitute a limitation on the present invention.

[0039] Example 1

[0040] like Figure 1 As shown, the present invention provides a method for taking over autonomous driving in a vehicle, comprising the following steps:

[0041] The driver's fatigue level is identified based on real-time collected facial images.

[0042] Based on the driver's level of fatigue, corresponding reminders are issued, and the driver can choose to maintain vehicle operation through autonomous driving or control the vehicle to pull over to the side of the road through autonomous driving.

[0043] Specifically, the process of identifying the driver's fatigue level based on real-time collected driver facial images includes: locating the lips in the driver's facial image in real time, and determining the driver's fatigue level based on the frequency of lip changes exceeding a set value.

[0044] Specifically, the process of identifying the driver's fatigue level based on real-time collected driver facial images includes: locating the driver's eyes in the driver's facial image in real time, and determining the driver's fatigue level based on the frequency of changes in the eyes within a set range.

[0045] Specifically, the lip change amplitude is the change in distance between the upper and lower edges of the mouth at two adjacent sampling times; the set value is 1.5 times the distance between the upper and lower edges of the mouth at the previous sampling time in two adjacent sampling times; if the frequency of the lip change amplitude being greater than the set value is greater than the corresponding set standard, the driver's physical condition is marked as low and a corresponding prompt sound is output.

[0046] Specifically, the eye variation amplitude is the change in distance between the upper and lower edges of the eye at two adjacent sampling times; the set range includes a first set range and a second set range.

[0047] The minimum value of the first set range is 0.25 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; the maximum value of the first set range is 0.75 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the first set range is greater than the corresponding set standard, the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and automatic driving will be activated to maintain vehicle driving.

[0048] The minimum value of the second set range is 0; the maximum value of the first set range is 0.25 times the distance between the upper and lower edges of the mouth in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the second set range is greater than the corresponding set standard, the driver's physical condition will be marked as very low, a corresponding prompt sound will be output, and the automatic driving control will be activated to pull the vehicle to the side of the road.

[0049] Specifically, if the driver's physical condition is marked as low, and the frequency of lip changes exceeding the set value within a set time period exceeds the corresponding set standard, then the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and autonomous driving will be activated to maintain vehicle operation.

[0050] Specifically, if the driver's physical condition is marked as low, and the frequency of eye changes within a first set range is greater than the corresponding set standard within a set time period, then the driver's physical condition will be marked as very low, a corresponding prompt sound will be output, and the autonomous driving control will be activated to pull the vehicle to the side of the road.

[0051] Specifically, by locating key points in the driver's face image, several key points located at the upper and lower edges of the mouth are determined; the distance between the corresponding symmetrical key points at the upper and lower edges of the mouth is calculated; and the average distance between the upper and lower edges of the mouth is obtained by averaging the calculated distances between all symmetrical key points.

[0052] Specifically, by locating key points in the driver's face image, several key points located at the upper and lower edges of the eyes are determined; the distance between the corresponding symmetrical key points at the upper and lower edges of the glasses is calculated; and the average distance between the upper and lower edges of the eyes is obtained by averaging the calculated distances between all symmetrical key points.

[0053] Example 2

[0054] This invention provides a method for autonomous driving takeover in vehicles. By comprehensively assessing driver fatigue levels using recorded data, it determines whether the driver is capable of normal driving and, in conjunction with autonomous driving functions, effectively avoids vehicle accidents caused by driver fatigue. Addressing the safety issue of drivers being unable to drive normally due to sudden physical conditions, this invention utilizes a comprehensive driver fatigue monitoring device. Based on the driver's driving status, it effectively identifies the driver's physical condition, determines whether the driver can complete the driving process normally, and, based on different levels of driver physical condition, provides safety reminders while automatically activating different levels of autonomous driving functions. This effectively reduces vehicle accidents caused by sudden driver health issues. The specific steps include, as follows: Figure 2 As shown:

[0055] The first step involves using an in-vehicle front-facing image acquisition device to capture the driver's facial image in real time and identify and locate 68 key points within the facial image. The specific method for locating these key points is standard practice in existing technology. These key points typically include the positions of facial features such as the eyes, nose, and mouth. By detecting these key points, functions such as driver facial recognition and expression recognition can be achieved. Figure 3 As shown. The sampling frequency is 5 times / s.

[0056] The second step is to calculate the distances between the symmetrical key points on the upper and lower edges of the eyes and the upper and lower edges of the lips at two adjacent sampling times, i.e., d1(n) and d2(n) at the two time steps, and the corresponding changes Δd1(n) and Δd2(n), where:

[0057]

[0058]

[0059] Δd1(n)=|d1(n+1)-d1(n)|

[0060] Δd2(n)=|d2(n+1)-d2(n)|

[0061] In the formula, D38-D42 represent the y-coordinates of key points on the right eye (with the eye closed as the y-direction), D44-D48 represent the y-coordinates of key points on the left eye, D51-D53 represent the y-coordinates of key points on the upper edge of the lips, D57-D59 represent the y-coordinates of key points on the lower edge of the lips, d1(n) represents the distance between the upper and lower edges of the driver's lips at the nth time step, d2(n) represents the distance between the upper and lower edges of the driver's eyes at the nth time step, Δd1(n) represents the change in the distance between the upper and lower edges of the driver's lips at two adjacent time steps, and Δd2(n) represents the change in the distance between the upper and lower edges of the driver's eyes at two adjacent time steps. If the calculated data does not show any anomalies...

[0062] The third step is to assess the driver's level of fatigue based on the following criteria:

[0063] If the change in lip position Δd1(n) is greater than 1.5*d1, then determine whether the driver is yawning and record the frequency.

[0064] If the driver yawns more than 3 times per 5 minutes, which is significantly higher than the normal yawning frequency, the system will determine that the driver is beginning to enter a state of fatigue and record the driver's physical condition as low.

[0065] The risk of a driver falling asleep is determined based on the amplitude of eye movement change Δd2(n). If the amplitude of eye movement change Δd2(n) remains in the range of 0.25d2-0.75d2 within 10 seconds, the driver's eyes are in a semi-closed to closed state for a long time, which is very similar to the eye state when a person is fatigued. At this time, the driver is judged to be in a fatigued state and has a high risk of falling asleep, and the driver's physical condition is recorded as low.

[0066] If the eye change amplitude Δd2(n) remains within the range of 0-0.25d2 within 10 seconds, the driver's eyes are in a completely closed state for a long time, which is very similar to a person being asleep. It is determined that the driver is asleep or unconscious, and the driver's physical condition is recorded as very low.

[0067] Based on three levels of driver condition: low, low, and very low, different autonomous driving response plans are provided to assist the driver in driving and pulling over to avoid potential dangers during driving. The process is as follows: Figure 1 As shown, the specific solutions are as follows:

[0068] When a driver's physical condition is assessed as low—meaning the driver can barely manage to drive for a short period—the vehicle will promptly alert the driver to their poor condition, providing various indicators of unsatisfactory physical condition, informing the driver of potential risks, and advising them to stop driving immediately. The vehicle will continuously monitor the driver's physical condition, and if the driver's condition does not improve—meaning the yawning frequency remains greater than 3 times per 5 minutes—the vehicle will be upgraded to the next level.

[0069] When a driver's physical condition is assessed as low, meaning the driver is on the verge of being unable to drive normally and there is a risk of driving safety hazards, the vehicle will repeatedly remind the driver of their poor physical condition, providing a list of unsatisfactory indicators and informing the driver of the numerous risks involved. The vehicle will automatically activate its hazard lights, and the autonomous driving system will promptly initiate to assist driving and slow the car down. Simultaneously, the monitoring frequency will be increased, specifically assessing whether the eye movement amplitude Δd2(n) remains consistently within the 0-0.25d2 range over 8 seconds. If this is detected, it is determined that the driver's physical condition has not improved, and the vehicle will be upgraded to the next level.

[0070] When the driver's physical condition is determined to be very low, meaning the driver is no longer able to drive the vehicle and there is an imminent risk to the vehicle's safety, the vehicle will repeatedly remind the driver that their physical condition is poor and attempt to wake up the drowsy or unconscious driver by increasing the volume of the reminder. The autonomous driving system will immediately activate, assist the vehicle in pulling over to the side of the road, automatically turn on the hazard lights (with priority over the driver), and call a frequently used contact to inform the driver that their physical condition is very low and that medical assistance may be required if necessary.

[0071] This embodiment, through the aforementioned different response schemes designed based on different driver physical conditions, classifies and determines the driver's physical condition and implements corresponding countermeasures based on different levels. With the assistance of this enhanced active safety solution, on the one hand, it effectively avoids the risk of immediate activation of autonomous driving due to driver physical problems, even when the driver is capable of normal driving, potentially interfering with and affecting the driver's normal driving, causing misjudgment of vehicle conditions and leading to unnecessary car accidents. On the other hand, it also addresses situations where the driver's condition is extremely poor, rendering normal driving impossible and posing a significant safety hazard. By prioritizing autonomous driving over the driver, it promptly takes over driving control, avoiding the risk of car accidents caused by driver errors due to unconsciousness, fainting, or confusion. Based on the above comprehensive considerations, this enhanced active safety solution effectively avoids car accidents caused by driver physical problems, improves vehicle active safety performance, and greatly optimizes the driver's experience.

[0072] Example 3

[0073] This invention provides a vehicle autonomous driving takeover system for implementing the vehicle autonomous driving takeover method described in the above technical solution, comprising: a fatigue level recognition module and an autonomous driving takeover module;

[0074] The fatigue level recognition module is used to identify the driver's fatigue level based on real-time captured driver facial images;

[0075] The autonomous driving takeover module is used to output corresponding reminders based on the driver's fatigue level, and to choose between maintaining vehicle driving through autonomous driving or controlling the vehicle to pull over to the side of the road through autonomous driving.

[0076] Example 4

[0077] This invention provides a vehicle, the vehicle comprising:

[0078] One or more processors;

[0079] Memory, used to store one or more programs;

[0080] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle autonomous driving takeover method described in the above technical solution.

[0081] Example 5

[0082] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle autonomous driving takeover method described above.

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0088] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A method for taking over autonomous driving in a vehicle, characterized in that: Includes the following steps: The driver's fatigue level is identified based on real-time collected driver facial images. The process of identifying the driver's fatigue level based on real-time collected driver facial images includes: real-time positioning of the lips in the driver's facial image and determining the driver's fatigue level based on the frequency of lip changes exceeding a set value; and real-time positioning of the eyes in the driver's facial image and determining the driver's fatigue level based on the frequency of eye changes within a set range. The lip change amplitude is defined as the change in distance between the upper and lower edges of the mouth at two adjacent sampling times. The set value is 1.5 times the distance between the upper and lower edges of the mouth at the previous sampling time. If the frequency of the lip change amplitude exceeding the set value is greater than the corresponding set standard, the driver's physical condition will be marked as low, and a corresponding prompt sound will be output. The eye variation amplitude is defined as the change in distance between the upper and lower edges of the eye at two adjacent sampling times; the set range includes a first set range and a second set range. The minimum value of the first set range is 0.25 times the distance between the upper and lower edges of the eyes in the previous sampling time in two adjacent sampling times; the maximum value of the first set range is 0.75 times the distance between the upper and lower edges of the eyes in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the first set range is greater than the corresponding set standard, the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and automatic driving will be activated to maintain vehicle driving. The minimum value of the second set range is 0; the maximum value of the second set range is 0.25 times the distance between the upper and lower edges of the eyes in the previous sampling time in two adjacent sampling times; if the frequency of eye changes within the second set range is greater than the corresponding set standard, the driver's physical condition will be marked as very low, a corresponding prompt sound will be output, and the automatic driving control will be activated to pull the vehicle to the side of the road. Based on the driver's level of fatigue, corresponding reminders are issued, and the driver can choose to maintain vehicle operation through autonomous driving or control the vehicle to pull over to the side of the road through autonomous driving.

2. The method according to claim 1, characterized in that: If the driver's physical condition is marked as low, and the frequency of lip changes exceeding the set value within a set time period exceeds the corresponding set standard, then the driver's physical condition will be marked as low, a corresponding prompt sound will be output, and automatic driving will be activated to maintain vehicle operation.

3. The method according to claim 1, characterized in that: If the driver's physical condition is marked as low, and the frequency of eye changes within the first set range is greater than the corresponding set standard within a set time period, then the driver's physical condition will be marked as very low, a corresponding prompt sound will be output, and the autonomous driving control will be activated to pull the vehicle to the side of the road.

4. The method according to claim 1, characterized in that: By locating key points in the driver's face image, several key points located at the upper and lower edges of the mouth are determined; the distance between the corresponding symmetrical key points at the upper and lower edges of the mouth is calculated; and the average distance between the upper and lower edges of the mouth is obtained by averaging the calculated distances between all symmetrical key points.

5. A method according to claim 1, characterized in that: By locating key points in the driver's facial image, several key points located at the upper and lower edges of the eyes were determined. Calculate the distance between the corresponding symmetrical key points at the top and bottom edges of the glasses; average the calculated distances between all symmetrical key points to obtain the average distance between the top and bottom edges of the glasses.

6. A vehicle autonomous driving takeover system, characterized in that: The method for implementing the vehicle autonomous driving takeover method according to any one of claims 1-5 includes: a fatigue level recognition module and an autonomous driving takeover module; The fatigue level recognition module is used to identify the driver's fatigue level based on real-time captured driver facial images; The autonomous driving takeover module is used to output corresponding reminders based on the driver's fatigue level, and to choose between maintaining vehicle driving through autonomous driving or controlling the vehicle to pull over to the side of the road through autonomous driving.

7. A vehicle, characterized in that, The vehicles include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle autonomous driving takeover method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the vehicle autonomous driving takeover method as described in any one of claims 1-5.

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