A method and control system for mobile phone management during driving

Abstract

The present invention relates to the field of driving technology, and specifically discloses a method and control system for mobile phone management during driving. The present invention obtains vehicle operation state data and mobile phone multi-modal sensing data, combines vehicle CAN bus information with mobile phone sensor data, calculates a motion coupling degree value based on the deviation between the steering wheel angle and the mobile phone acceleration, analyzes the operation synchronization rate through throttle operation and gyroscope response to evaluate the coordination of hand movements, calculates a spatial constraint deviation value by combining the magnetometer orientation and the driving position coordinate offset, monitors the abnormal postures of the driver, generates a risk assessment value by fusing the three indicators, dynamically manages the mobile phone functions, enables basic functions such as voice interaction at low risk, restricts touch operations at medium risk, and completely disables non-core functions at high risk. Through multi-source data fusion and hierarchical management strategies, this system balances the emergency communication needs on the premise of ensuring driving safety, accurately identifies distracted driving behaviors, and solves the driving safety hazards caused by mobile phone use.

Description

Technical Field

[0001] The present invention relates to the field of driving technology, and in particular to a method and a control system for controlling a mobile phone during driving. Background Art

[0002] With the popularity of smartphones, it is becoming more and more common for drivers to use their phones while driving. According to statistics, about 68% of traffic accidents are directly related to drivers' distracted use of their phones, which has become one of the important factors causing traffic accidents.

[0003] In Chinese patent CN207837007U, there is a driver's mobile phone safety control device, including a control box, a single-chip microcomputer, an electric lock, a pressure sensor, a GPS positioning device and an audio output device; the control box includes a box cover and a box body with an opening on the top, the box cover covers the top opening of the box body, the electric lock is arranged on the box cover, the pressure sensor and the GPS positioning device are arranged at the bottom of the box body, and the audio output device is arranged on the outer surface of the box body; the single-chip microcomputer is respectively connected to the electric lock, the pressure sensor, the GPS positioning device and the audio output device. The beneficial effects of adopting the above scheme are: monitoring the status of the mobile phone during driving to prevent the driver from operating the mobile phone while driving, safely controlling the mobile phone, preventing the driver from being distracted by operating the mobile phone while driving, and improving road safety;

[0004] A driving control system and method in Chinese patent CN201410314270.X, the system includes: a smart phone placed on a car to have the same motion state as the car, the smart phone having a built-in acceleration sensor for measuring acceleration; a diagnostic service platform connected to the smart phone for communication, receiving and processing direction data and acceleration data sent by the smart phone, comparing the processing results with preset conditions and sending corresponding prompt information to the smart phone;

[0005] When controlling existing mobile phones, the driving mode is usually used to restrict the mobile phone. If the driving mode needs to be released to use the mobile phone, the mobile phone needs to be manually controlled. This will cause the driver to be distracted and cause dangerous driving problems. Therefore, a mobile phone control method and control system during driving are needed to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide a method and a control system for controlling a mobile phone during driving, so as to solve the technical problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for controlling a mobile phone during driving, comprising:

[0009] Obtain the operating status data of the vehicle and the multi-modal sensing data of the mobile terminal;

[0010] Obtain the vehicle CAN bus data information according to the vehicle operating status data;

[0011] Obtain the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the multi-modal sensing data of the mobile terminal;

[0012] Obtain the coordinate offset degree of the driver's seat, and obtain the spatial constraint deviation value according to the multi-modal sensing data of the mobile terminal and the coordinate offset degree of the driver's seat;

[0013] Calculate the risk assessment value according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value, and control the mobile phone during the driving process according to the risk assessment value.

[0014] Preferably, the step of obtaining the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the multi-modal sensing data of the mobile terminal includes:

[0015] Obtain the acceleration of the accelerometer built in the mobile phone according to the multi-modal sensing data of the mobile terminal, and obtain the first vehicle speed and the steering wheel angle based on the vehicle CAN bus data information;

[0016] Correct the first vehicle speed based on the GPS signal and the inertial navigation system to obtain the corrected vehicle speed;

[0017] Obtain the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels and the cornering stiffness of the rear wheels, and obtain the stability coefficient according to the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels and the cornering stiffness of the rear wheels, where the calculation formula is:

[0018] ;

[0019] Wherein, represents the stability coefficient, represents the total mass of the vehicle, represents the wheelbase, represents the cornering stiffness of the front wheels, represents the cornering stiffness of the rear wheels;

[0020] Obtain the lateral acceleration according to the corrected vehicle speed, the wheelbase of the vehicle, the steering wheel angle and the stability coefficient, where the calculation formula is:

[0021] ;

[0022] Wherein, represents the lateral acceleration, represents the vehicle speed, represents the wheelbase, represents the steering wheel angle, represents the stability coefficient;

[0023] Obtain the acceleration deviation ratio according to the lateral acceleration and the acceleration;

[0024] Obtain the concentration duration of the driver looking straight ahead within a preset time, and calculate the driving distraction correction factor according to the concentration duration and the preset driving operation time. The calculation formula is:

[0025] ;

[0026] Wherein, represents the driving distraction correction factor, represents the concentration duration, represents the preset driving operation time, represents the natural constant;

[0027] Obtain the corrected acceleration deviation ratio according to the driving distraction correction factor and the acceleration deviation ratio, and use the corrected acceleration deviation ratio as the value of the motion coupling degree.

[0028] Preferably, the steps of obtaining the motion coupling degree value and the operation synchronization rate from the vehicle CAN bus data information and the mobile terminal multimodal sensing data further include:

[0029] Obtain the throttle opening information according to the vehicle CAN bus data information;

[0030] Obtain the gyroscope response information built in the mobile phone according to the mobile terminal multimodal sensing data;

[0031] Obtain multiple throttle opening values within a preset time according to the throttle opening information, and obtain the throttle opening curve according to the multiple throttle opening values and the preset time;

[0032] Obtain multiple gyroscope response speeds within a preset time according to the gyroscope response information, and obtain the gyroscope response speed curve according to the multiple gyroscope response speeds and the preset time;

[0033] Compare the throttle opening curve and the gyroscope response speed curve based on the preset time stamp in sequence to obtain multiple operation reaction times of the throttle opening - gyroscope response speed;

[0034] Obtain multiple operation reaction deviation times according to the multiple operation reaction times and the preset operation reaction time;

[0035] Obtain the operation response deviation achievement rate based on multiple operation response deviation times and a preset standard deviation time, and use the operation response deviation achievement rate as the operation synchronization rate.

[0036] Preferably, the step of obtaining the spatial constraint deviation value according to the mobile terminal multimodal sensing data and the driving position coordinate offset includes:

[0037] Obtain the azimuth angle of the magnetometer built in the mobile phone according to the mobile terminal multimodal sensing data;

[0038] Based on the displacement sensor on the seat rail, obtain the current front-back adjustment amount and the current left-right adjustment amount of the driver's driving position relative to the standard driving position;

[0039] Calculate the driving position coordinate offset according to the current front-back adjustment amount and the current left-right adjustment amount. The calculation formula is:

[0040] ;

[0041] Wherein, represents the driving position coordinate offset, represents the current front-back adjustment amount, represents the current left-right adjustment amount;

[0042] Calculate the driving position coordinate offset according to the driving position coordinate offset and a preset standard threshold;

[0043] Obtain the driver's head pose information based on the in-vehicle camera, and extract the information from the driver's head pose information based on the OpenPose algorithm to obtain the nose tip coordinates;

[0044] Obtain the center coordinates of the driver's driving position, wherein the center coordinates are the plane center coordinates of the driving position seat cushion, and use the center coordinates as the coordinate origin;

[0045] Vertically extend the coordinate origin towards the vehicle head direction to the edge of the driving position to obtain the edge coordinates, and obtain the vehicle head direction vector according to the edge coordinates and the coordinate origin;

[0046] Obtain the coordinate system of the center coordinates, and project the nose tip coordinates into the coordinate system to obtain the first nose tip coordinates;

[0047] Obtain the nose tip projection vector according to the first nose tip coordinates and the coordinate origin, and calculate the angle between the nose tip and the vehicle head according to the nose tip projection vector and the vehicle head direction vector. The calculation formula is:

[0048] ;

[0049] Wherein, Denotes the angle between the nasal tip and the vehicle's front end. Denotes the nasal tip projection vector. Denotes the vehicle's front end direction vector.

[0050] Take the angle between the nasal tip and the vehicle's front end as the azimuth angle between the nasal tip and the vehicle's front end, and obtain the deviation azimuth angle based on the magnetometer azimuth angle and the azimuth angle between the nasal tip and the vehicle's front end.

[0051] Perform weighted calculation on the deviation azimuth angle and the driving position coordinate offset to obtain a comprehensive deviation value, and take the comprehensive deviation value as the spatial constraint deviation value.

[0052] Preferably, the step of calculating the risk assessment value according to the motion coupling degree value, operation synchronization rate, and constraint deviation value includes:

[0053] Normalize the motion coupling degree value to obtain a normalized motion coupling degree value.

[0054] Normalize the operation synchronization rate to obtain a normalized operation synchronization rate value.

[0055] Normalize the constraint deviation value to obtain a normalized constraint deviation value.

[0056] Calculate the risk assessment value according to the normalized motion coupling degree value, normalized operation synchronization rate value, and normalized constraint deviation value, where the calculation formula is:

[0057] ;

[0058] Where, Denotes the risk assessment value. Denotes the normalized motion coupling degree value. Denotes the weight factor of the normalized motion coupling degree value. Denotes the normalized operation synchronization rate value. Denotes the weight factor of the normalized operation synchronization rate value. Denotes the normalized constraint deviation value.

[0059] Preferably, the step of controlling the mobile phone during driving according to the risk assessment value includes:

[0060] Calculate the risk assessment difference according to the risk assessment value and the preset standard risk assessment value.

[0061] Judge whether the risk assessment difference satisfies the preset threshold range.

[0062] When the risk assessment difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, the driving process is determined to be a low-risk interval, and a first risk instruction is generated. According to the first risk instruction, the first control of the mobile phone is released, wherein the first risk refers to sending the first risk instruction to the mobile phone based on the acquisition terminal, and after the mobile phone receives the first risk instruction, it modifies the cgroup configuration of the Linux kernel to obtain an independent cgroup configuration, allocates independent permission resources to the voice / navigation process based on the independent cgroup configuration, obtains voice / navigation application permissions, and allows the mobile phone to perform voice interaction and navigation operations based on the voice / navigation application permissions;

[0063] When the risk assessment difference satisfies the preset threshold interval range, the driving process is determined to be a medium risk interval, and a second risk instruction is generated. According to the risk instruction, the second control of the mobile phone control is released, wherein the second risk refers to sending the second risk instruction to the mobile phone based on the collection terminal, and the mobile phone modifies the Telephony service whitelist verification logic after receiving the second risk instruction, and the whitelist verification logic releases the whitelist restriction based on the second risk instruction, when the whitelist restriction is released, and a touch screen operation inhibition instruction is generated, the touch screen inhibition driver is started based on the touch screen operation inhibition instruction, and the touch screen operation is suppressed based on the touch screen inhibition driver;

[0064] When the risk assessment difference does not meet the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, the driving process is determined to be a high-risk interval, and a third risk instruction is generated. The third control of the mobile phone is released according to the risk instruction, wherein the third risk refers to sending the third risk instruction to the mobile phone based on the acquisition terminal, and after the mobile phone receives the third risk instruction, starting the preset RF front-end dynamic switch based on the third risk instruction, wherein the preset RF front-end dynamic switch executes to open the emergency frequency band RF channel and close other RF channels, and makes the mobile phone only release the SOS emergency call function hardware lock, and the communication module remains disabled.

[0065] The present application also provides a mobile phone control system during driving, including:

[0066] A first acquisition module, used to acquire vehicle operation status data and mobile terminal multimodal sensing data;

[0067] A second acquisition module is used to acquire vehicle CAN bus data information according to the vehicle running status data;

[0068] A third acquisition module is used to acquire a motion coupling degree value and an operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensing data;

[0069] A fourth acquisition module, configured to acquire the driving position coordinate offset of the driver, and acquire a spatial constraint deviation value according to the multi-modal sensing data of the mobile terminal and the driving position coordinate offset;

[0070] A first calculation module, configured to calculate a risk assessment value according to the motion coupling degree value, the operation synchronization rate, and the constraint deviation value, and control the mobile phone during driving according to the risk assessment value.

[0071] Preferably, the third acquisition module includes:

[0072] A first acquisition unit, configured to acquire the acceleration of the accelerometer built in the mobile phone according to the multi-modal sensing data of the mobile terminal, and acquire the first vehicle speed and the steering wheel angle based on the vehicle CAN bus data information;

[0073] A first correction unit, configured to correct the first vehicle speed based on the GPS signal and the inertial navigation system to obtain a corrected vehicle speed;

[0074] A second acquisition unit, configured to acquire the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels, and the cornering stiffness of the rear wheels, and acquire a stability coefficient according to the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels, and the cornering stiffness of the rear wheels;

[0075] A third acquisition unit, configured to acquire a lateral acceleration according to the corrected vehicle speed, the wheelbase of the vehicle, the steering wheel angle, and the stability coefficient;

[0076] A fourth acquisition unit, configured to acquire an acceleration deviation ratio according to the lateral acceleration and the acceleration;

[0077] A fifth acquisition unit, configured to acquire the focus duration of the driver looking straight ahead within a preset time, calculate a driving distraction correction factor according to the focus duration and the preset driving operation time, acquire a corrected acceleration deviation ratio according to the driving distraction correction factor and the acceleration deviation ratio, and use the corrected acceleration deviation ratio as the motion coupling degree value.

[0078] This application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0079] This application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0080] The beneficial effects of the present application are as follows: The present invention obtains vehicle operation state data (such as speed, acceleration) and mobile phone multimodal sensing data, combines vehicle CAN bus information (steering wheel angle, throttle opening) with mobile phone sensor data (accelerometer, gyroscope), and uses GPS and inertial navigation systems to correct vehicle speed data. Calculate the motion coupling degree value based on the deviation between the steering wheel angle and the mobile phone acceleration to quantify the relevance of the in-vehicle computer state; analyze the operation synchronization rate through throttle operation and gyroscope response to evaluate the coordination of hand movements; calculate the spatial constraint deviation value by combining the magnetometer azimuth and the driving position coordinate offset to monitor the abnormal posture of the driver. Generate a risk assessment value by integrating the three indicators to dynamically control mobile phone functions: basic functions such as voice interaction are enabled at low risk, touch operations are restricted at medium risk, and non-core functions are completely disabled at high risk. Through multi-source data fusion and hierarchical control strategies, this system balances the emergency communication needs on the premise of ensuring driving safety, accurately identifies distracted driving behaviors, and solves the driving safety hazards caused by mobile phone use. Description of the Drawings

[0081] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present application.

[0082] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present application.

[0083] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0084] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0085] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0086] As Figures 1-3 shown, the present application provides a method for controlling a mobile phone during driving, including:

[0087] S1. Obtain the vehicle operation state data and the mobile terminal multimodal sensing data;

[0088] S2. Obtain the vehicle CAN bus data information according to the vehicle operation state data;

[0089] S3. Obtain the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensing data;

[0090] S4. Obtain the driving position coordinate offset of the driver, and obtain the spatial constraint deviation value according to the mobile terminal multimodal sensing data and the driving position coordinate offset;

[0091] S5. Calculate a risk assessment value based on the degree of motion coupling value, the operation synchronization rate, and the constraint deviation value, and control the mobile phone during driving according to the risk assessment value.

[0092] As described in the above steps S1-S5, since the existing mobile phones usually use driving mode to restrict the mobile phones during management and control, and the mobile phone needs to be manually controlled to release the driving mode, which will cause the driver to be distracted and cause dangerous driving problems. Therefore, the present invention first obtains the vehicle's operating status data and the mobile terminal's multimodal sensor data, so as to provide basic data for subsequent analysis and management and control. The vehicle operation status data reflects the driving conditions of the vehicle, such as speed, acceleration, etc., while the built-in sensor information of the mobile phone can reflect the dynamic changes of the mobile phone. The acquisition of this data is the premise for a comprehensive assessment of driving risks. At the same time, it detects whether the vehicle is in a state of driving, accelerating, turning, etc., and whether the mobile phone is moving, rotating, etc., which is helpful to discover potential distracted driving behaviors. Then, the vehicle CAN bus data information is obtained according to the vehicle operation status data. In this way, the mobile phone sensor information (accelerometer, gyroscope, magnetometer data) and vehicle CAN bus data (the source of key information such as steering wheel angle and throttle opening) closely related to mobile phone control are further screened and extracted through the vehicle operation status data, making the data more targeted. At the same time, it can also more accurately associate the vehicle driving status with the mobile phone status. For example, when the vehicle turns, the steering wheel angle information and the mobile phone sensor information are combined to determine whether the driver's driving operation is affected by operating the mobile phone, and then the first vehicle speed is corrected based on the GPS signal and the inertial navigation system to obtain the corrected vehicle speed. Then, the vehicle speed is corrected through two different positioning and navigation technologies, which can improve the accuracy of the vehicle speed. The speed obtained by the vehicle CAN bus may have certain errors, while the GPS signal and inertial navigation system can correct the speed from different angles, so that the final corrected speed is closer to the actual driving speed of the vehicle. This helps to make the subsequent calculation and analysis based on the accurate speed more reliable, so as to more accurately evaluate the relationship between the vehicle's motion state and the use of mobile phones. At the same time, in order to ensure the accuracy and reliability of the entire risk assessment process, as accurate speed data as possible is required. Since the speed obtained by a single data source may have deviations, the use of GPS signals and inertial navigation systems for correction can make up for possible errors and provide a more reliable data basis for subsequent steps. This is an important link to improve the accuracy and reliability of the system. Then, the motion coupling degree value and operation synchronization rate are obtained based on the vehicle CAN bus data information and the multimodal sensor data of the mobile terminal. The steering wheel angle and throttle opening are important indicators that reflect the driver's driving operation intention and the dynamic response of the vehicle.Obtaining the motion coupling degree value can quantify the degree of correlation between the vehicle motion and the mobile phone status. For example, when the vehicle is turning, if the acceleration change of the mobile phone has a large deviation from the lateral acceleration of the vehicle caused by the steering wheel turning, it means that the driver may be operating the mobile phone, which increases the driving risk. At the same time, starting from the key vehicle control data, a motion coupling index related to the mobile phone status is constructed to provide a key quantitative basis for risk assessment. In this way, the potential interference relationship between the driver's operation of the vehicle and the use of the mobile phone can be deeply analyzed. Next, the operation synchronization rate is obtained according to the throttle opening information and the gyroscope response information, so that the operation synchronization rate can be It reflects the synchronization between the driver's hand movement when operating the accelerator and the mobile phone operation (the dynamic changes of the mobile phone are reflected through the gyroscope response). If the operation synchronization rate is low, it means that the driver may be distracted by the mobile phone during driving, affecting the normal control of the accelerator, thereby endangering driving safety. At the same time, by comparing the accelerator operation and the dynamic changes of the mobile phone, the driving risk is evaluated from the perspective of operation coordination, further enriching the dimension of risk assessment, and more comprehensively detecting whether the driver has distracted driving behavior. Secondly, the driver's driving position coordinate offset is obtained, and the spatial constraint is obtained according to the multimodal sensing data of the mobile terminal and the driving position coordinate offset. Deviation value, through the driving position coordinate offset, reflects the position change of the driver in the driver's seat. The spatial constraint deviation value calculated by combining the magnetometer azimuth and head posture information can determine whether the driver's body posture is normal, whether there is abnormal body deviation or head rotation due to focusing on the mobile phone. For example, if the driver's body is excessively offset to one side and the head is facing the mobile phone, it may mean that he is operating the mobile phone and distracting the driving attention. At the same time, starting from the driver's body posture, considering the impact of spatial position factors on driving risks, combined with the previous vehicle and mobile phone operation data, a more comprehensive risk assessment system is formed to improve the reliability of distracted driving behavior detection. Finally, the risk assessment value is calculated according to the motion coupling degree value, operation synchronization rate and constraint deviation value, and the mobile phone is controlled during driving according to the risk assessment value. In this way, a comprehensive risk assessment value is obtained by combining multiple key indicators calculated in the previous steps, which can accurately measure the risk level in the driving process. The risk level can be obtained in advance. Under the premise of ensuring driving safety, the mobile phone control is reasonably released and the driver is allowed to use the functions restricted by the mobile phone. This not only balances the needs of safety and emergency communication, but also solves the problem of drivers being distracted by using mobile phones and causing dangerous driving.

[0093] In one embodiment, the step S3 of acquiring the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensing data includes:

[0094] S301. Obtain the acceleration of the accelerometer built in the mobile phone based on the multi-modal sensing data of the mobile terminal, and obtain the first vehicle speed and the steering wheel angle based on the vehicle CAN bus data information;

[0095] S302. Calibrate the first vehicle speed based on the GPS signal and the inertial navigation system to obtain the calibrated vehicle speed;

[0096] S303. Obtain the vehicle wheelbase, the total vehicle mass, the front wheel cornering stiffness, and the rear wheel cornering stiffness, and obtain the stability coefficient according to the vehicle wheelbase, the total vehicle mass, the front wheel cornering stiffness, and the rear wheel cornering stiffness, where the calculation formula is:

[0097] ;

[0098] Where, represents the stability coefficient, represents the total vehicle mass, represents the wheelbase, represents the front wheel cornering stiffness, represents the rear wheel cornering stiffness;

[0099] S304. Obtain the lateral acceleration according to the calibrated vehicle speed, the vehicle wheelbase, the steering wheel angle, and the stability coefficient, where the calculation formula is:

[0100] ;

[0101] Where, represents the lateral acceleration, represents the vehicle speed, represents the wheelbase, represents the steering wheel angle, represents the stability coefficient;

[0102] S305. Obtain the acceleration deviation ratio according to the lateral acceleration and the acceleration;

[0103] S306. Obtain the concentration duration of the driver looking straight ahead within the preset time, and calculate the driving distraction correction factor according to the concentration duration and the preset driving operation time. The calculation formula is:

[0104] ;

[0105] Where, represents the driving distraction correction factor, represents the concentration duration, represents the preset driving operation time, represents the natural constant;

[0106] S307. Obtain a corrected acceleration deviation ratio based on the driving distraction correction factor and the acceleration deviation ratio, and use the corrected acceleration deviation ratio as the motion coupling degree value.

[0107] As described in the above steps S301 - S307, since vehicle speed is one of the key parameters of the vehicle driving state, therefore, the present invention first obtains the acceleration of the accelerometer built in the mobile phone according to the multi - modal sensing data of the mobile terminal, and obtains the first vehicle speed and the steering wheel angle based on the vehicle CAN (Controller Area Network) bus data information. The definition of the vehicle CAN bus data information involves multiple aspects, including but not limited to the data frame structure, signal parsing, standard definitions of the physical layer and the data link layer, and the selection of the application layer protocol. At the same time, the vehicle speed can directly affect the vehicle's handling performance and driving stability. The response characteristics of the vehicle to the driver's operation are different at different vehicle speeds. For example, when driving at high speed, more cautious operations are required for vehicle steering and acceleration, and a small mistake may lead to serious consequences. At the same time, it can also provide the necessary data basis for subsequent calculations of indicators such as lateral acceleration. Since the calculation of lateral acceleration is closely related to the vehicle speed, accurate acquisition of the vehicle speed can ensure the accuracy of the lateral acceleration calculation, thereby more precisely evaluating the relationship between the vehicle motion state and mobile phone use. Secondly, the vehicle speed is at the core of the entire vehicle motion analysis and is an important link connecting other motion parameters. In the system construction based on vehicle dynamics and mobile phone control, first obtaining the vehicle speed is the logical starting point for a series of subsequent precise calculations and risk assessments. Without vehicle speed data, the accuracy of the subsequent steps cannot be guaranteed. Then, the vehicle wheelbase, vehicle total mass, front wheel cornering stiffness, and rear wheel cornering stiffness are obtained, and a stability coefficient is obtained according to the vehicle wheelbase, the vehicle total mass, the front wheel cornering stiffness, and the rear wheel cornering stiffness. These vehicle parameters (wheelbase, total mass, front and rear wheel cornering stiffness) jointly determine the handling stability of the vehicle. The wheelbase affects the turning radius and driving trajectory of the vehicle, the total mass determines the inertia size of the vehicle, and the front and rear wheel cornering stiffnesses reflect the deformation characteristics of the tires under the action of lateral forces, thereby affecting the sideslip behavior of the vehicle. And the stability coefficient calculated by the formula can quantify the stability degree of the vehicle under different driving conditions. The formula is derived from the vehicle two - degree - of - freedom model (Bicycle Model) through moment balance. The difference in lateral forces between the front and rear wheels will generate a yaw moment, affecting the vehicle steering characteristics. At the same time, this formula quantifies the steering stability characteristics of the vehicle from a dynamic perspective through the vehicle mass, wheelbase, and tire cornering stiffness. Its core logic is that the balance of lateral forces between the front and rear wheels determines the steering characteristics, and the mass and wheelbase affect the inertia distribution and moment relationship. And this calculation method is the theoretical basis of vehicle stability control (such as ESP) and is also a key tool for optimizing handling safety in automotive engineering.Furthermore, the stability coefficient plays a crucial role in correcting and adjusting the subsequent calculation of lateral acceleration, making the calculation result of lateral acceleration more in line with the actual handling performance and stable state of the vehicle, improving the accuracy of evaluating the vehicle's motion state. Then, the lateral acceleration is obtained based on the corrected vehicle speed, vehicle wheelbase, steering wheel angle, and stability coefficient. The lateral acceleration is an important indicator to measure the deviation of the vehicle from straight-line driving under turning or lateral force. It intuitively reflects the dynamic changes of the vehicle in the lateral direction. By comparing it with the acceleration of the mobile phone, it can be judged whether there is an abnormal deviation between the acceleration of the mobile phone and the actual motion of the vehicle when the vehicle generates lateral acceleration (such as during turning), thus detecting possible distracted driving behaviors. For example, if the vehicle generates a certain lateral acceleration during turning, but the mobile phone simultaneously shows a large and unrelated acceleration change, it may mean that the driver is operating the mobile phone instead of focusing on driving. At the same time, the lateral acceleration is a key intermediate variable connecting vehicle handling and mobile phone state analysis. By combining the vehicle speed, vehicle structure parameters, and steering wheel angle information obtained previously, the lateral acceleration is calculated, providing a directly comparable data basis for further quantifying the coupling degree of vehicle and mobile phone motion, which is a key step in realizing the detection of distracted driving from vehicle motion. Finally, the acceleration deviation ratio is obtained based on the lateral acceleration and the acceleration. In this way, the acceleration deviation ratio quantitatively represents the degree of correlation between vehicle motion (reflected by lateral acceleration) and mobile phone motion (reflected by acceleration). If this ratio is large, it indicates a large deviation between vehicle motion and mobile phone motion, and the driver may be operating the mobile phone during driving, increasing the driving risk. This ratio provides an important dimension for subsequent comprehensive risk assessment, enabling the risk assessment to comprehensively consider the dynamic relationship between the vehicle and the mobile phone and more comprehensively and accurately judge the safety status during driving. At the same time, when analyzing the relationship between vehicle and mobile phone motion, a method is needed to quantify the acceleration relationship between the two in order to incorporate it into the overall risk assessment system. Calculating the acceleration deviation ratio and defining it as part of the motion coupling degree value can concisely represent this relationship, providing standardized and comparable data indicators for subsequent multi-factor-based risk assessment, making the entire control method more complete and rigorous logically. Then, the focus duration of the driver looking straight ahead within a preset time is obtained, and the driving distraction correction factor is calculated based on the focus duration and the preset driving operation time. In this way, the focus duration of the driver is an important factor for evaluating the degree of driving distraction. The driving distraction correction factor is calculated by comparing it with the preset driving operation time. The definition of the formula is that the focus duration is a variable directly reflecting the driver's attention concentration. The longer the focus duration, the more focused the driver is on driving and the less likely to be distracted; conversely, the shorter the focus duration, the more likely to be distracted.In the formula, the focus duration participates in the operation and directly affects the result of the driving distraction correction factor. The preset driving operation time provides a time reference standard for the calculation. In different driving scenarios, the preset driving operation time can be set according to the actual situation. For example, the requirements for driving operation time under different road conditions such as urban roads and highways are different. By comparing and calculating with the focus duration, the driver's focus situation within this preset time can be measured. The natural constant is a mathematical constant that plays a role in exponential operations. It makes the formula show a non-linear variation relationship, which is more in line with the complex relationship between the driver's focus level and distraction risk in the actual driving scenario, rather than a simple linear correlation, and the formula is a deformation of the S-shaped function (logistic function). As the focus duration. increases, the exponential part in the formula will change, and the entire denominator

[0108] will increase, resulting in a gradual decrease in the value of the driving distraction correction factor. This means that the longer the focus duration, the smaller the driving distraction correction factor, that is, the smaller the correction effect on the motion coupling degree value, because at this time the driver is less likely to be distracted, and the original motion coupling degree value can better reflect the actual situation; on the contrary, the shorter the focus duration, the larger the driving distraction correction factor, and the greater the correction effect on the motion coupling degree value, because at this time the driver is more likely to be distracted, and a greater adjustment needs to be made to the original motion coupling degree value to more accurately evaluate the driving risk, and it can take into account the influence of the driver's attention concentration during driving on the motion coupling degree. If the driver's focus duration is short, it is more likely to be distracted to operate the mobile phone. At this time, the correction factor will adjust the acceleration deviation ratio so that the motion coupling degree value can more accurately reflect the actual driving risk. Finally, the corrected acceleration deviation ratio is obtained according to the driving distraction correction factor and the acceleration deviation ratio, and the corrected acceleration deviation ratio is used as the motion coupling degree value. In this way, the finally obtained motion coupling degree value comprehensively considers various factors such as vehicle motion parameters, mobile phone acceleration, and driver focus duration. It comprehensively reflects the degree of association between vehicle motion and mobile phone use with a quantitative value, providing key basic data for subsequent comprehensive risk assessment. A larger motion coupling degree value indicates a greater possibility of abnormal association between vehicle and mobile phone motion during driving, that is, there is a higher risk of distracted driving. In one embodiment, step S3 of obtaining the motion coupling degree value and the operation synchronization rate from the vehicle CAN bus data information and the mobile terminal multimodal sensing data further includes:

[0109] S308. Obtain the throttle opening information according to the vehicle CAN bus data information;

[0110] S309. Obtain the gyroscope response information built in the mobile phone according to the multi-modal sensing data of the mobile terminal;

[0111] S3010. Obtain multiple throttle opening values within a preset time according to the throttle opening information, and obtain a throttle opening curve according to the multiple throttle opening values and the preset time;

[0112] S3011. Obtain multiple gyroscope response speeds within a preset time according to the gyroscope response information, and obtain a gyroscope response speed curve according to the multiple gyroscope response speeds and the preset time;

[0113] S3012. Compare the throttle opening curve and the gyroscope response speed curve based on a preset time stamp in sequence to obtain multiple operation reaction times of throttle opening - gyroscope response speed;

[0114] S3013. Obtain multiple operation reaction deviation times according to the multiple operation reaction times and a preset operation reaction time;

[0115] S3014. Obtain an operation reaction deviation achievement rate according to the multiple operation reaction deviation times and a preset standard deviation time, and use the operation reaction deviation achievement rate as the operation synchronization rate.

[0116] As described in the above steps S308-S3014, since the change in throttle opening reflects the driver's control intention and operation frequency of the vehicle power output, the present invention first obtains the throttle opening information according to the vehicle CAN bus data information, and then obtains the gyroscope response information built into the mobile phone according to the mobile terminal multimodal sensing data, wherein the mobile terminal multimodal sensing data is mainly obtained based on the sensor in the mobile phone, and then obtains multiple throttle opening values ​​within a preset time according to the throttle opening information, and obtains a throttle opening curve according to the multiple throttle opening values ​​and the preset time. In this way, by obtaining multiple opening values ​​within the preset time and drawing a curve, the dynamic process of throttle operation can be clearly presented, such as whether there are frequent fluctuations or abnormal opening changes. At the same time, it also provides a throttle operation data basis in a time series for subsequent comparison with the gyroscope response information, which is helpful for analyzing whether the driver is distracted by operating the mobile phone at different throttle operation stages. When studying the correlation between driving operation and mobile phone use, throttle operation is one of the key elements. Displaying its changes over time in the form of a curve can intuitively capture the detailed features of the operation, which is an important pre-step for subsequent precise analysis and judgment, and provides necessary data visualization support for comprehensive evaluation of driving behavior. Then, multiple gyroscope response speeds within a preset time are obtained according to the gyroscope response information, and a gyroscope response speed curve is obtained according to the multiple gyroscope response speeds and the preset time. In this way, the gyroscope response speed can reflect the dynamic changes of the mobile phone in space, including rotation, tilt and other motion states. Obtaining its curve can show the trend of the movement trajectory of the mobile phone during driving, and combined with the throttle opening curve, the driver's operation synchronization can be analyzed from different angles (vehicle power control and mobile phone dynamics). If there is an obvious incoordination between the two curves, it may indicate that the driver is distracted by operating the mobile phone, and the motion state of the mobile phone is an important basis for judging whether the driver is distracted. By constructing the gyroscope response speed curve, the dynamic behavior of the mobile phone can be quantified and correlated with the vehicle operation data for analysis, making the detection of distracted driving more scientific and comprehensive, and enriching the data source of risk assessment. Next, the throttle opening curve and the gyroscope response speed curve are sequentially compared based on the preset timestamp to obtain multiple operation reaction times of the throttle opening-gyroscope response speed, so as to determine the time interval between the throttle operation and the mobile phone movement change at different time points, that is, the operation reaction time. These time data can reflect whether the driver is distracted by operating the mobile phone while operating the vehicle components, and the delayed effect of distracted operation on the vehicle operation response. For example, if the mobile phone also has abnormal movement when the throttle opening changes significantly, and the operation reaction time is too short or too long, it may indicate that the driver's attention is distracted and the driving behavior is risky. At the same time, this step realizes the accurate comparison of vehicle operation and mobile phone dynamics in the time dimension, which is a key link in quantifying the correlation between the two.By obtaining the operation response time, it provides a direct data basis for further evaluating the operation synchronization subsequently, which helps to accurately judge the driver's driving concentration. Secondly, multiple operation response deviation times are obtained based on the multiple operation response times and the preset operation response time, so as to calculate the deviation between the actual operation response time and the preset standard operation response time, further highlighting the difference between the driver's operation behavior and the normal driving expectation. A larger deviation time indicates that the driver's operation synchronization is poor, and there may be a situation of distracted operation of the mobile phone. Moreover, these deviation time data can more precisely depict the abnormal degree of driving behavior, providing a more discriminative index for risk assessment, enhancing the accuracy of driving risk judgment. At the same time, in order to more accurately evaluate the rationality of the driver's operation, it is necessary to compare and analyze the actual operation response time with the standard value. By calculating the deviation time, the evaluation of operation synchronization can be upgraded from simple time recording to the level of comparison with the standard reference, making the risk assessment more scientific and standardized. Finally, an operation response deviation achievement rate is obtained based on the multiple operation response deviation times and the preset standard deviation time, and the operation response deviation achievement rate is used as the operation synchronization rate. In this way, the operation response deviation achievement rate (operation synchronization rate) comprehensively reflects the synchronization performance between the driver's throttle operation and mobile phone operation in the form of a percentage. A synchronization rate close to 100% indicates highly coordinated operation, while a lower synchronization rate strongly implies that the driver may cause out-of-sync operation due to distracted behaviors such as using the mobile phone during driving, increasing the accident risk. And this index provides a key quantitative dimension for the overall risk assessment, making the risk assessment of different driving scenarios and driver behaviors comparable and consistent, facilitating the formulation of unified control strategies. Summarizing multiple operation response deviation times into a comprehensive operation synchronization rate can simplify and standardize the evaluation results of the driver's operation coordination. In the entire control method system, the operation synchronization rate, as an important intermediate variable, is convenient for comprehensive calculation with other risk assessment indicators (such as the degree of motion coupling value, spatial constraint deviation value, etc.), so as to obtain a comprehensive and accurate driving risk assessment value and achieve effective control of mobile phone use during driving.

[0117] In one embodiment, step S4 of obtaining the spatial constraint deviation value according to the multi-modal sensing data of the mobile terminal and the driving position coordinate offset of the driver includes:

[0118] S401. Obtain the azimuth angle of the magnetometer built in the mobile phone according to the multi-modal sensing data of the mobile terminal;

[0119] S402. Obtain the current front-back adjustment amount and the current left-right adjustment amount of the driver's driving position relative to the standard driving position based on the displacement sensor on the seat rail;

[0120] S403. Calculate the driving position coordinate offset according to the current front-back adjustment amount and the current left-right adjustment amount. The calculation formula is:

[0121] ;

[0122] where, represents the driving position coordinate offset, represents the current front-back adjustment amount, represents the current left-right adjustment amount;

[0123] S404. Calculate the driving position coordinate offset according to the driving position coordinate offset and the preset standard threshold;

[0124] S405. Obtain the driver's head pose information based on the in-vehicle camera, and extract the information from the driver's head pose information based on the OpenPose algorithm to obtain the nose tip coordinates;

[0125] S406. Obtain the center coordinates of the driver's driving position. Among them, the center coordinates are the plane center coordinates of the driving seat cushion, and the center coordinates are used as the coordinate origin;

[0126] S407. Vertically extend the coordinate origin towards the vehicle's head direction to the edge of the driving position to obtain the edge coordinates, and obtain the vehicle's head direction vector according to the edge coordinates and the coordinate origin;

[0127] S408. Obtain the coordinate system of the center coordinates, and project the nose tip coordinates into the coordinate system to obtain the first nose tip coordinates;

[0128] S409. Obtain the nose tip projection vector according to the first nose tip coordinates and the coordinate origin, and calculate the angle between the nose tip and the vehicle's head according to the nose tip projection vector and the vehicle's head direction vector. The calculation formula is:

[0129] ;

[0130] where, represents the angle between the nose tip and the vehicle's head, represents the nose tip projection vector, represents the vehicle's head direction vector;

[0131] S4010. Take the angle between the nose tip and the vehicle's head as the nose tip-vehicle head azimuth angle, and obtain the deviation azimuth angle according to the magnetometer azimuth angle and the nose tip-vehicle head azimuth angle;

[0132] S4011. Perform weighted calculation on the deviation azimuth angle and the driving position coordinate offset to obtain a comprehensive deviation value, and take the comprehensive deviation value as the spatial constraint deviation value.

[0133] As described in the above steps S401-S4011, the present invention first obtains the azimuth of the magnetometer built into the mobile phone based on the multimodal sensing data of the mobile terminal, and then obtains the current front and rear adjustment amount and the current left and right adjustment amount of the driver's driving position relative to the standard driving position based on the displacement sensor on the seat rail, so that the adjustment amount of the driving position can reflect the change of the driver's position in the car. In normal driving, the driver usually maintains a relatively stable sitting posture, but if there is a large front and rear or left and right adjustment, it may indicate that the driver's body posture is unstable or there is abnormal behavior, such as changing posture to operate the mobile phone. At the same time, these adjustment amount data provide basic information for the subsequent evaluation of the driver's seat coordinate offset, which helps to determine whether the driver is in a normal driving operation state, and it is necessary to obtain the adjustment amount of the driver's seat as the starting point for analyzing the driver's body posture. It is the direct data source for the subsequent calculation of the driver's position coordinate offset. By monitoring this data, the abnormal change of the driver's position can be preliminarily discovered, so as to further explore its impact on driving safety. It is the basic link for building a complete risk assessment system. Then, the driver's position coordinate offset is calculated according to the current front-to-back adjustment amount and the current left-to-right adjustment amount. The driver's position coordinate offset integrates the front-to-back and left-to-right adjustment amounts into a quantitative index through mathematical calculation, which more intuitively indicates the degree of deviation of the driver's position from the standard position. A larger coordinate offset may mean that the driver's body deviates from the optimal driving posture, increasing the difficulty and risk of misoperation of the vehicle control components. It may also be associated with behaviors such as distracted operation of mobile phones. At the same time, the adjustment amounts in the two directions are integrated into a single offset index, which is convenient for unified analysis and comparison with other indicators in subsequent risk assessment. It simplifies the data processing process, so that the abnormal situation of the driver's position can be presented in a concise and clear way, providing an important quantitative basis for judging driving risks. Then, the driver's position coordinate offset is calculated according to the driver's position coordinate offset and the preset standard threshold. By comparing with the preset standard threshold, it can be clear whether the current driver's position coordinate offset exceeds the normal range. If it exceeds the threshold, it means that there is a large abnormality in the driver's body position and the cause needs to be further analyzed. It is likely related to distracted driving behavior. At the same time, it realizes the standardized judgment of the driving position deviation, so that the risk assessment has clear boundaries and reference standards, and improves the accuracy and reliability of the assessment. Next, the driver's head posture information is obtained based on the in-car camera, and the driver's head posture information is extracted based on the OpenPose algorithm to obtain the nose tip coordinates. The driver's head posture is an important clue to reflect the direction of his attention concentration.By obtaining the head pose information and extracting the nose tip coordinates, the position and orientation changes of the head can be accurately tracked. For example, if the head rotates frequently or deviates from the normal driving line of sight for a long time, and the nose tip coordinates indicate that the head is facing the position where the mobile phone is placed, it is very likely that the driver is focusing on the mobile phone rather than the road conditions, increasing the accident risk. Among them, the head pose plays a key role in distracted driving detection. Using advanced algorithms to extract the nose tip coordinates can more accurately locate the head position. Compared with directly observing the head pose, it provides more accurate quantitative data, laying a foundation for subsequent steps such as calculating the angle between the nose tip and the vehicle's front end, enhancing the scientific nature of judging the driver's attention direction. Then, obtain the central coordinates of the driver's seat. Among them, the central coordinates are the plane central coordinates of the driver's seat cushion, and use the central coordinates as the coordinate origin. Determining the coordinate origin provides a unified reference framework for subsequent vector and angle calculations. Taking the center of the driver's seat as the origin can conveniently describe the relative relationships of the driver's head, nose tip, etc. relative to the driver's seat, making the calculation more standardized and accurate. At the same time, it helps to analyze the relationship between the position changes of various parts of the driver's body and the vehicle direction in the same coordinate system, providing a geometric basis for comprehensively evaluating driving risks. And establishing a reasonable coordinate system is a prerequisite for spatial position analysis. Selecting the center of the driver's seat cushion as the origin conforms to the geometric center concept of the human body in the driver's seat, facilitating subsequent calculations based on vectors and angles, making the entire analysis process more rigorous mathematically and clearer logically, ensuring the consistency of analysis under different vehicles and driving scenarios. Secondly, extend the coordinate origin vertically towards the vehicle's front end to the edge of the driver's seat to obtain the edge coordinates, and obtain the vehicle's front end direction vector according to the edge coordinates and the coordinate origin. The vehicle's front end direction vector clarifies the forward direction of the vehicle, providing a reference direction for subsequent calculation of the angle between the nose tip and the vehicle's front end. By determining this vector, the degree of deviation of the driver's head relative to the vehicle's driving direction can be quantified, and combined with the nose tip coordinates, it can be judged whether the driver's head deviates from the normal driving line of sight range, further evaluating whether there is distracted driving behavior. Then, obtain the coordinate system of the central coordinates and project the nose tip coordinates into the coordinate system to obtain the first nose tip coordinates. Furthermore, the projection operation converts the nose tip coordinates into a coordinate system with the center of the driver's seat as the origin, making the relative relationship between the nose tip position and the vehicle and the driver's seat more intuitive and easy to calculate, and facilitating subsequent calculation of the angle between the nose tip projection vector and the vehicle's front end direction vector, ensuring that all position information is processed within a unified mathematical framework, improving the accuracy and reliability of the analysis. At the same time, in the presence of multiple coordinate systems, in order to perform effective position and angle analysis, the nose tip coordinates need to be unified into the driver's seat center coordinate system.The projection operation realizes this conversion, which enables data from different sources to work together under the same mathematical model. It is a key step in accurate geometric analysis and ensures the rationality and effectiveness of subsequent calculations. Next, the nose tip projection vector is obtained according to the first nose tip coordinate and the coordinate origin, and the angle between the nose tip and the front of the vehicle is calculated according to the nose tip projection vector and the vehicle front direction vector. The angle between the nose tip and the front of the vehicle accurately quantifies the deviation angle between the driver's head direction and the vehicle's driving direction. A larger angle indicates that the driver's head has deviated from the normal driving line of sight and may be paying attention to other things in the car, such as a mobile phone, thereby increasing the driving risk. Secondly, the angle between the nose tip and the front of the vehicle is used as the nose tip-vehicle front azimuth, and the deviation azimuth is obtained according to the magnetometer azimuth and the nose tip-vehicle front azimuth. In this way, the magnetometer azimuth (reflecting the orientation information of the mobile phone) and the nose tip-vehicle front azimuth can be combined to further analyze the relationship between the mobile phone orientation and the driver's head orientation. The deviation azimuth can highlight the inconsistency between the two. If the deviation is large, it means that the driver may be operating a mobile phone and his head is facing the direction of the mobile phone, which increases the driving risk. At the same time, when judging distracted driving, it is necessary not only to consider the individual orientations of the head and the mobile phone, but also to analyze the relative relationship between them. By calculating the deviation azimuth, it is possible to more accurately capture the abnormal head and body posture changes that may be caused by the driver's mobile phone operation, further improve the detection mechanism of distracted driving behavior, and improve the accuracy and reliability of risk assessment. Finally, the deviation azimuth and the driving position coordinate offset are weighted to obtain a comprehensive deviation value, wherein the sum of the weights of the deviation azimuth and the driving position coordinate offset is equal to 1, and the comprehensive deviation value is used as the spatial constraint deviation value. In this way, the spatial constraint deviation value comprehensively considers the deviation of the driver's body position and the deviation of the head and the mobile phone orientation, and quantifies the overall spatial abnormality of the driver during driving with a unified indicator. Then, this value can more comprehensively reflect the driver's distracted driving degree, and provide an important comprehensive indicator for subsequent risk assessment, so that risk assessment can more accurately reflect the actual safety status during driving.

[0134] In one embodiment, the step S5 of calculating the risk assessment value according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value comprises:

[0135] S501, normalizing the motion coupling degree value to obtain a motion coupling degree normalized value;

[0136] S502, normalizing the operation synchronization rate to obtain a normalized value of the operation synchronization rate;

[0137] S503, normalizing the constraint deviation value to obtain a constraint deviation normalized value;

[0138] S504. Calculate the risk assessment value based on the normalized value of the motion coupling degree, the normalized value of the operation synchronization rate, and the normalized value of the constraint deviation. The calculation formula is as follows:

[0139] ;

[0140] where, represents the risk assessment value, represents the normalized value of the motion coupling degree, represents the weight factor of the normalized value of the motion coupling degree, represents the normalized value of the operation synchronization rate, represents the weight factor of the normalized value of the operation synchronization rate, represents the normalized value of the constraint deviation.

[0141] As described in the above steps S501 - S504, the present invention first normalizes the motion coupling degree value to obtain a normalized motion coupling degree value. Through normalization, the motion coupling degree value is converted into a unified standard range (usually between 0 and 1). The advantage of this is that the motion coupling degree values obtained under different vehicles and different driving scenarios are comparable. For example, in a small car and a large truck, due to the different characteristics of the vehicles themselves, the original motion coupling degree values may vary greatly, but after normalization, they can be evaluated on the same scale and are convenient for subsequent comprehensive operations with other normalized indicators (such as the normalized operation synchronization rate value and the normalized constraint deviation value), ensuring that each indicator has the same weight benchmark in the risk assessment calculation and avoiding the over - influence or under - influence of a certain indicator on the assessment result due to the difference in numerical magnitudes. Then, the operation synchronization rate is normalized to obtain a normalized operation synchronization rate value. Similar to the normalization of the motion coupling degree value, after normalization, the operation synchronization rate can have a consistent measurement standard under different driving conditions and driver behavior patterns. Regardless of the differences in the vehicle's power system and driving habits, the normalized operation synchronization rate can reflect the synchronization between the throttle operation and the mobile phone operation in the same way and is convenient for linear combination or weighted calculation with other indicators in the comprehensive risk assessment, ensuring that the operation synchronization rate can accurately reflect its impact on driving safety in the overall risk assessment and avoiding assessment deviations caused by the particularity of the original data. At the same time, in the multi - indicator risk assessment, the operation synchronization rate is an important dimension, but due to the variation of the data generation environment and value range for various factors, normalization processing is necessary. Through this step, the operation synchronization rate can be transformed into a general form that can cooperate with other indicators, providing a standardized data basis for accurately calculating the risk assessment value. Then, the constraint deviation value is normalized to obtain a normalized constraint deviation value, and the constraint deviation value reflects the deviation in spatial constraints such as the driver's body position and head posture. After normalization, it can be comprehensively considered with other normalized indicators on the same magnitude and standard. For example, under the differences in the driving position space layout and driving habits of different vehicle models, the original constraint deviation values may be very different. Normalization gives it a general comparison meaning, and enables the spatial constraint factors to participate in the calculation with appropriate weights in the risk assessment, accurately reflecting the impact of the driver's abnormal body posture during driving on driving safety and jointly constituting a comprehensive risk assessment system with other factors. Finally, the risk assessment value is calculated according to the normalized motion coupling degree value, the normalized operation synchronization rate value, and the normalized constraint deviation value. In this way, the three normalized key indicators are combined through a specific calculation formula to obtain an assessment value that comprehensively reflects the risk of mobile phone use during driving.This value can comprehensively consider the association between vehicle movement and the mobile phone, the synchronization of driver operations, and the spatial constraints of body postures, accurately evaluating driving risks from multiple perspectives. For example, if the risk assessment value is high, it indicates that there is a high possibility of distracted driving during the driving process, and corresponding control measures (such as restricting mobile phone functions or issuing warnings) need to be taken; while a lower risk assessment value indicates that the driving behavior is relatively safe and the use of the mobile phone has no obvious impact on driving.

[0142] In one embodiment, step S5 of controlling the mobile phone during the driving process according to the risk assessment value includes:

[0143] S505. Calculate a risk assessment difference according to the risk assessment value and a preset standard risk assessment value;

[0144] S506. Determine whether the risk assessment difference meets a preset threshold range;

[0145] When the risk assessment difference does not meet the preset threshold range and is less than the minimum value of the preset threshold range, it is determined that the driving process is in a low-risk interval, and a first risk instruction is generated. According to the first risk instruction, the first control of the mobile phone control is released. Among them, the first risk means that the first risk instruction is sent to the mobile phone based on the acquisition terminal. After the mobile phone receives the first risk instruction, it modifies the cgroup configuration of the Linux kernel to obtain an independent cgroup configuration, and allocates independent permission resources to the voice / navigation process based on the independent cgroup configuration to obtain voice / navigation application permissions. Based on the voice / navigation application permissions, the mobile phone is allowed to perform voice interaction and navigation operations;

[0146] When the risk assessment difference meets the preset threshold range, it is determined that the driving process is in a medium-risk interval, and a second risk instruction is generated. According to the risk instruction, the second control of the mobile phone control is released. Among them, the second risk means that the second risk instruction is sent to the mobile phone based on the acquisition terminal. After the mobile phone receives the second risk instruction, it modifies the white list verification logic of the Telephony service, and the white list verification logic releases the white list restriction based on the second risk instruction. When the white list restriction is released, a touch screen operation inhibition instruction is generated, and the touch screen inhibition driver is started based on the touch screen operation inhibition instruction. The touch screen operation is inhibited based on the touch screen inhibition driver;

[0147] When the risk assessment difference does not meet the preset threshold range and is greater than the maximum value of the preset threshold range, it is determined that the driving process is in a high-risk range, and a third risk instruction is generated. According to the risk instruction, the third control release of the mobile phone control is performed. Among them, the third risk means that the third risk instruction is sent to the mobile phone based on the acquisition terminal. After receiving the third risk instruction, the mobile phone starts a preset radio frequency front-end dynamic switch based on the third risk instruction. Among them, the preset radio frequency front-end dynamic switch executes to turn on the emergency frequency band RF channel, turn off other RF channels, and only release the hardware lock of the SOS emergency call function of the mobile phone, and the communication module remains disabled.

[0148] As described in the above steps S605 - S606, the present invention first calculates the risk assessment difference according to the risk assessment value and the preset standard risk assessment value. Calculating the risk assessment difference in this way can intuitively reflect the deviation degree between the current driving condition and the preset safety standard. By quantifying this difference, the risk condition during driving can be judged more precisely. For example, if the risk assessment difference is positive and the value is large, it indicates that the actual risk assessment value far exceeds the preset standard, meaning that there is a high risk of distracted driving caused by mobile phone use during driving. Moreover, this difference provides a clear quantitative basis for subsequent risk judgment, making the risk judgment no longer a vague qualitative analysis but a comparison based on specific values, improving the accuracy and objectivity of risk assessment. At the same time, in the risk assessment system, in order to accurately judge whether control measures need to be taken, the actual risk assessment value needs to be compared with the preset threshold range.Calculating the difference is an effective way to achieve this comparison. It simplifies the complex risk assessment results into a directly comparable value. It is an important prerequisite for the subsequent decision-making steps, ensuring the scientificity and rigor of the risk judgment. Then, it is determined whether the risk assessment difference meets the preset threshold interval range. When the risk assessment difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, the driving process is determined to be a low-risk interval, and a first risk instruction is generated. According to the first risk instruction, the mobile phone control is first released. The first risk refers to sending the first risk instruction to the mobile phone based on the acquisition terminal. After the mobile phone receives the first risk instruction, it modifies the cgroup configuration of the Linux kernel to obtain an independent cgroup configuration. Based on the independent cgroup configuration, independent permission resources are allocated to the voice / navigation process to obtain voice / navigation application permissions. Based on the voice / navigation application permissions, the mobile phone is allowed to perform voice interaction and navigation operations. When the risk assessment difference meets the preset threshold interval range, the driving process is determined to be a medium-risk interval, and a second risk instruction is generated. According to the risk instruction, the mobile phone control is secondly released. The second risk refers to sending the first risk instruction to the mobile phone based on the acquisition terminal. After receiving the first risk instruction, the mobile phone modifies the cgroup configuration of the Linux kernel to obtain an independent cgroup configuration. Based on the independent cgroup configuration, independent permission resources are allocated to the voice / navigation process to obtain voice / navigation application permissions. Based on the voice / navigation application permissions, the mobile phone is allowed to perform voice interaction and navigation operations. The second risk instruction is sent to the mobile phone at the collection terminal. After the mobile phone receives the second risk instruction, the whitelist verification logic of the Telephony service is modified. The whitelist verification logic is based on the second risk instruction to release the whitelist restriction. When the whitelist restriction is released, a touch screen operation inhibition instruction is generated. The touch screen inhibition drive is started based on the touch screen operation inhibition instruction. The touch screen operation is suppressed based on the touch screen inhibition drive. When the risk assessment difference does not meet the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, the driving process is determined to be a high-risk interval, and a third risk instruction is generated. The third control of the mobile phone is released according to the risk instruction. The third risk refers to sending the third risk instruction to the mobile phone based on the collection terminal. After the mobile phone receives the third risk instruction, the preset RF front-end dynamic switch is started based on the third risk instruction. The preset RF front-end dynamic switch executes to open the emergency frequency band RF channel and close other RF channels, and only releases the SOS emergency call function hardware lock of the mobile phone, and the communication module remains disabled. In this way, by calculating the risk assessment difference, the gap between the risk level in the current driving state and the preset threshold interval range can be intuitively reflected. This difference can quantify the risk deviation caused by factors such as mobile phone use during driving. For example, if the difference is positive and the value is large, it means that the actual risk is higher than the standard risk, and there is a high risk of distracted driving caused by mobile phone use during driving; if the difference is negative or the value is small, it means that the actual risk is relatively low and close to or meets the safety standard.This provides clear data basis for accurately judging the driving risk situation subsequently, and then taking corresponding control measures, thereby reducing the possibility of accidents caused by drivers' distracted operation of mobile phones, and can avoid the situations of over-control or under-control. By comprehensively calculating multiple key indicators in the previous steps, a comprehensive risk assessment value is obtained. At the same time, corresponding mobile phone control strategies can be formulated according to the risk assessment value. In this way, it reasonably allows drivers to use mobile phone functions in emergency situations, balances the needs of safety and emergency communication, and can also solve the problem that drivers are distracted by using mobile phones and cause dangerous driving, achieving the balance between driving safety and the convenience of users using mobile phones. At the same time, it can ensure that on the premise of ensuring driving safety, the possible emergency communication needs of drivers are taken into account, achieving the balance between safety and practicality.

[0149] The present application also provides a mobile phone control system during driving, including:

[0150] The first acquisition module 1 is used to acquire the running state data of the vehicle and the multi-modal sensing data of the mobile terminal;

[0151] The second acquisition module 2 is used to acquire the vehicle CAN bus data information according to the vehicle running state data;

[0152] The third acquisition module 3 is used to acquire the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the multi-modal sensing data of the mobile terminal;

[0153] The fourth acquisition module 4 is used to acquire the driving position coordinate deviation of the driver, and acquire the space constraint deviation value according to the multi-modal sensing data of the mobile terminal and the driving position coordinate deviation;

[0154] The first calculation module 5 is used to calculate the risk assessment value according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value, and control the mobile phone during driving according to the risk assessment value.

[0155] In one embodiment, the third acquisition module includes:

[0156] The first acquisition unit is used to acquire the acceleration of the accelerometer built in the mobile phone according to the multi-modal sensing data of the mobile terminal, and acquire the first vehicle speed and the steering wheel angle based on the vehicle CAN bus data information;

[0157] The first correction unit is used to correct the first vehicle speed based on the GPS signal and the inertial navigation system to obtain the corrected vehicle speed;

[0158] A second acquisition unit, configured to acquire the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels, and the cornering stiffness of the rear wheels, and acquire a stability coefficient according to the wheelbase of the vehicle, the total mass of the vehicle, the cornering stiffness of the front wheels, and the cornering stiffness of the rear wheels;

[0159] A third acquisition unit, configured to acquire a lateral acceleration according to the corrected vehicle speed, the wheelbase of the vehicle, the steering wheel angle, and the stability coefficient;

[0160] A fourth acquisition unit, configured to acquire an acceleration deviation ratio according to the lateral acceleration and the acceleration;

[0161] A fifth acquisition unit, configured to acquire the concentration duration of the driver looking straight ahead within a preset time, calculate a driving distraction correction factor according to the concentration duration and a preset driving operation time, acquire a corrected acceleration deviation ratio according to the driving distraction correction factor and the acceleration deviation ratio, and use the corrected acceleration deviation ratio as a value of the degree of motion coupling.

[0162] The present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the mobile phone control method during driving described above are implemented.

[0163] The present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the mobile phone control method during driving described above are implemented.

[0164] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0165] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article, or method including that element.

[0166] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for controlling mobile phones during driving, characterized in that: include: Acquire vehicle operation status data and mobile terminal multimodal sensor data; Acquiring vehicle CAN bus data information according to the vehicle running status data; Acquire the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensor data, specifically acquire the acceleration of the accelerometer built into the mobile phone, and acquire the lateral acceleration of the vehicle based on the vehicle CAN bus data information; Acquire an acceleration deviation ratio according to the lateral acceleration and the acceleration; Acquiring the concentration time of the driver, and calculating a driving distraction correction factor according to the concentration time and a preset driving operation time, and acquiring a corrected acceleration deviation ratio according to the driving distraction correction factor and the acceleration deviation ratio as a motion coupling degree value; Acquiring throttle opening information according to the vehicle CAN bus data information; Get the built-in gyroscope response information of the mobile phone; Acquiring a throttle opening curve according to the throttle opening information; Acquire a gyroscope response speed curve according to the gyroscope response information; Comparing the throttle opening curve with the gyroscope response speed curve to obtain multiple operation reaction times; Acquire a plurality of operation reaction deviation times according to the plurality of operation reaction times and preset operation reaction times; Acquire an operation reaction deviation achievement rate according to the plurality of operation reaction deviation times and a preset standard deviation time as an operation synchronization rate; Obtain the coordinate offset of the driver's driving position, and obtain the spatial constraint deviation value according to the coordinate offset, specifically obtaining the azimuth of the magnetometer built into the mobile phone; Obtaining the front-to-back adjustment amount and the left-to-right adjustment amount of the driver's driving position relative to the standard driving position; Calculating a first driving position coordinate offset according to the front-to-back adjustment amount and the left-to-right adjustment amount; Calculating a driving position coordinate deviation according to the first driving position coordinate deviation and a preset standard threshold; Get the coordinates of nose tip; Get the coordinate origin; Obtain edge coordinates, and obtain the vehicle head direction vector according to the edge coordinates and the coordinate origin; Acquire a nose tip-vehicle front azimuth according to the acquired nose tip coordinates and the coordinate origin, and acquire a deviation azimuth according to the magnetometer azimuth and the nose tip-vehicle front azimuth; Performing weighted calculation on the deviation azimuth and the driving position coordinate offset to obtain a comprehensive deviation value as a space constraint deviation value; A risk assessment value is calculated according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value, and the mobile phone is controlled during driving according to the risk assessment value.

2. The method for controlling mobile phones during driving according to claim 1, characterized in that: The step of acquiring the motion coupling degree value and the operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensing data includes: Acquire the acceleration of the accelerometer built into the mobile phone according to the multimodal sensing data of the mobile terminal, and acquire the first vehicle speed and the steering wheel angle based on the vehicle CAN bus data information; Correcting the first vehicle speed based on the GPS signal and the inertial navigation system to obtain a corrected vehicle speed; Obtaining a vehicle wheelbase, a vehicle gross mass, a front wheel cornering stiffness, and a rear wheel cornering stiffness, and obtaining a stability coefficient according to the vehicle wheelbase, the vehicle gross mass, the front wheel cornering stiffness, and the rear wheel cornering stiffness; Obtaining lateral acceleration according to the corrected vehicle speed, vehicle wheelbase, steering wheel angle and stability factor; Acquire an acceleration deviation ratio according to the lateral acceleration and the acceleration; The concentration time of the driver looking forward within a preset time is obtained, and a driving distraction correction factor is calculated according to the concentration time and the preset driving operation time, a corrected acceleration deviation ratio is obtained according to the driving distraction correction factor and the acceleration deviation ratio, and the corrected acceleration deviation ratio is used as a motion coupling degree value.

3. The method for controlling mobile phones during driving according to claim 1, characterized in that: The step of obtaining the motion coupling degree value and the operation synchronization rate from the vehicle CAN bus data information and the mobile terminal multimodal sensing data also includes: Acquiring throttle opening information according to the vehicle CAN bus data information; Acquiring response information of a gyroscope built into the mobile phone according to the multimodal sensing data of the mobile terminal; Acquire a plurality of throttle opening values ​​within a preset time according to the throttle opening information, and acquire a throttle opening curve according to the plurality of throttle opening values ​​and the preset time; Acquire a plurality of gyroscope response speeds within a preset time according to the gyroscope response information, and acquire a gyroscope response speed curve according to the plurality of gyroscope response speeds and the preset time; Comparing the throttle opening curve and the gyroscope response speed curve in sequence based on a preset timestamp to obtain a plurality of operation reaction times of throttle opening-gyroscope response speed; Acquire a plurality of operation reaction deviation times according to the plurality of operation reaction times and preset operation reaction times; An operation reaction deviation achievement rate is acquired according to the plurality of operation reaction deviation times and a preset standard deviation time, and the operation reaction deviation achievement rate is used as an operation synchronization rate.

4. The method for controlling mobile phones during driving according to claim 1, characterized in that: The step of obtaining the coordinate offset of the driver's driving position and obtaining the space constraint deviation value according to the coordinate offset includes: Acquiring the azimuth of a magnetometer built into the mobile phone according to the multimodal sensing data of the mobile terminal; Acquiring a current front-to-rear adjustment amount and a current left-to-right adjustment amount of the driver's driving position relative to a standard driving position based on a displacement sensor on a seat rail; Calculating a first driving position coordinate offset according to the current front-to-back adjustment amount and the current left-to-right adjustment amount; Calculating a driving position coordinate deviation according to the first driving position coordinate deviation and a preset standard threshold; The driver's head posture information is obtained based on the in-car camera, and the driver's head posture information is extracted based on the OpenPose algorithm to obtain the coordinates of the nose tip; Obtaining the center coordinates of the driver's driving seat, wherein the center coordinates are the plane center coordinates of the driver's seat cushion, and the center coordinates are used as the coordinate origin; The coordinate origin is vertically extended toward the front of the vehicle to the edge of the driver's seat to obtain edge coordinates, and a vehicle front direction vector is obtained according to the edge coordinates and the coordinate origin; Acquire the coordinate system of the center coordinate, and project the nose tip coordinate into the coordinate system to obtain a first nose tip coordinate; Acquire a nose tip projection vector according to the first nose tip coordinate and the coordinate origin, and calculate an angle between the nose tip and the front of the vehicle according to the nose tip projection vector and the front direction vector of the vehicle; Taking the angle between the nose tip and the front of the vehicle as the nose tip-vehicle front azimuth, and obtaining the deviation azimuth according to the magnetometer azimuth and the nose tip-vehicle front azimuth; The deviation azimuth and the driving position coordinate offset are weightedly calculated to obtain a comprehensive deviation value, and the comprehensive deviation value is used as a space constraint deviation value.

5. The method for controlling mobile phones during driving according to claim 1, characterized in that: The step of calculating the risk assessment value according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value comprises: Normalizing the motion coupling degree value to obtain a motion coupling degree normalized value; Normalizing the operation synchronization rate to obtain a normalized value of the operation synchronization rate; Normalizing the constraint deviation value to obtain a constraint deviation normalized value; The risk assessment value is calculated according to the normalized value of the motion coupling degree, the normalized value of the operation synchronization rate and the normalized value of the constraint deviation.

6. The method for controlling mobile phones during driving according to claim 1, characterized in that: The step of controlling the mobile phone during driving according to the risk assessment value includes: Calculating a risk assessment difference based on the risk assessment value and a preset standard risk assessment value; Determine whether the risk assessment difference meets a preset threshold interval range; When the risk assessment difference does not meet the preset threshold interval range and is less than the minimum value of the preset threshold interval range, the driving process is determined to be a low-risk interval, and a first risk instruction is generated. According to the first risk instruction, the first control of the mobile phone is released, wherein the first risk instruction is sent to the mobile phone based on the acquisition terminal. After the mobile phone receives the first risk instruction, it modifies the cgroup configuration of the Linux kernel to obtain an independent cgroup configuration, allocates independent permission resources to the voice / navigation process based on the independent cgroup configuration, obtains voice / navigation application permissions, and allows the mobile phone to perform voice interaction and navigation operations based on the voice / navigation application permissions; When the risk assessment difference satisfies the preset threshold interval range, the driving process is determined to be a medium risk interval, and a second risk instruction is generated. According to the second risk instruction, the second control of the mobile phone control is released, wherein the second risk instruction is sent to the mobile phone based on the collection terminal, and after the mobile phone receives the second risk instruction, it modifies the Telephony service whitelist verification logic, and the whitelist verification logic releases the whitelist restriction based on the second risk instruction. When the whitelist restriction is released, a touch screen operation inhibition instruction is generated, and a touch screen inhibition driver is started based on the touch screen operation inhibition instruction, and the touch screen operation is suppressed based on the touch screen inhibition driver; When the risk assessment difference does not meet the preset threshold interval range and is greater than the maximum value of the preset threshold interval range, the driving process is determined to be a high-risk interval, and a third risk instruction is generated. The third control of the mobile phone is released according to the risk instruction, wherein the third risk instruction is sent to the mobile phone based on the acquisition terminal. After the mobile phone receives the third risk instruction, the preset RF front-end dynamic switch is started based on the third risk instruction, wherein the preset RF front-end dynamic switch opens the emergency frequency band RF channel and closes other RF channels, and the mobile phone only releases the SOS emergency call function hardware lock, and the communication module remains disabled.

7. A mobile phone control system during driving is used to execute the mobile phone control method during driving as claimed in any one of claims 1 to 6, characterized in that: include: A first acquisition module, used to acquire vehicle operation status data and mobile terminal multimodal sensing data; A second acquisition module is used to acquire vehicle CAN bus data information according to the vehicle running status data; A third acquisition module is used to acquire a motion coupling degree value and an operation synchronization rate according to the vehicle CAN bus data information and the mobile terminal multimodal sensing data; A fourth acquisition module is used to acquire a driver's driving position coordinate offset, and acquire a spatial constraint deviation value according to the multimodal sensing data of the mobile terminal and the driving position coordinate offset; The first calculation module is used to calculate a risk assessment value according to the motion coupling degree value, the operation synchronization rate and the constraint deviation value, and to control the mobile phone during driving according to the risk assessment value.

8. The mobile phone control system during driving according to claim 7, characterized in that: The third acquisition module includes: A first acquisition unit, configured to acquire the acceleration of an accelerometer built into the mobile phone according to the multimodal sensing data of the mobile terminal, and acquire a first vehicle speed and a steering wheel angle based on vehicle CAN bus data information; A first correction unit, configured to correct the first vehicle speed based on a GPS signal and an inertial navigation system to obtain a corrected vehicle speed; a second acquisition unit, configured to acquire a vehicle wheelbase, a vehicle gross mass, a front wheel cornering stiffness, and a rear wheel cornering stiffness, and acquire a stability coefficient according to the vehicle wheelbase, the vehicle gross mass, the front wheel cornering stiffness, and the rear wheel cornering stiffness; A third acquisition unit, used for acquiring a lateral acceleration according to the corrected vehicle speed, the vehicle wheelbase, the steering wheel angle and the stability coefficient; a fourth acquisition unit, configured to acquire an acceleration deviation ratio according to the lateral acceleration and the acceleration; a fifth acquisition unit, for acquiring a driver's concentration time of looking forward within a preset time, and calculating a driving distraction correction factor according to the concentration time and a preset driving operation time, and acquiring a corrected acceleration deviation ratio according to the driving distraction correction factor and the acceleration deviation ratio, and using the corrected acceleration deviation ratio as a motion coupling degree value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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