Intelligent traffic system and operation method thereof

Abstract

The invention discloses an intelligent traffic system and an operation method thereof, and relates to the technical field of vehicle traffic, and the method comprises the steps: obtaining vehicle driving data and physiological signal data in real time through a sensor, and carrying out the preprocessing; constructing an emotion recognition model according to the vehicle driving data and analyzing behavior characteristics of a user; identifying an emotional state in real time according to the user behavior characteristics and the physiological signal data; and performing emotion recognition calibration on the driver regularly, and optimizing the emotion recognition model according to a calibration result. By recognizing the emotional state and behavior characteristics of the driver in real time, potential dangerous driving behaviors can be warned in advance, an alarm can be given in time, the driver is reminded to be cold and quiet, and traffic accidents caused by out-of-control emotion are avoided; a traffic management department can more accurately know traffic conditions and driver emotion distribution conditions of different road sections and different time periods.

Description

Technical Field

[0001] The present invention relates to the field of vehicle traffic technology, in particular to an intelligent traffic system and an operation method thereof. Background Art

[0002] With the acceleration of urbanization and the continuous growth of car ownership, traffic congestion, frequent traffic accidents and other problems are becoming increasingly severe, and the demand for intelligent transportation systems is becoming more urgent. Traditional intelligent transportation systems mainly focus on the monitoring and regulation of vehicle flow, real-time feedback of road conditions, and vehicle navigation and positioning, aiming to improve traffic efficiency and reduce road congestion. These systems often ignore the role of the driver as a key factor in traffic operation. In the actual driving process, the driver's emotional state and behavioral characteristics have a vital impact on traffic safety. According to statistics, the proportion of traffic accidents caused by unstable drivers is increasing year by year. On urban roads, drivers frequently overtake and brake suddenly due to impatience, which can easily cause rear-end collisions; in long-distance driving, fatigue and sleepiness cause inattention. Concentration may cause the vehicle to deviate from the lane, causing serious safety hazards. Emotional fluctuations will also affect the driver's decision-making ability and reaction speed, making it impossible for them to make timely and accurate responses when facing sudden road conditions. At the same time, although the existing vehicle driving data collection technology can obtain basic information such as the vehicle's speed and position, the collection of information on the driver's physiological state is relatively insufficient. The traditional traffic system lacks effective means to monitor the driver's emotional changes in real time and accurately, and combine this information with vehicle driving data for comprehensive analysis. Although some high-end vehicles are equipped with a simple fatigue monitoring system, its function is relatively single, and it is unable to fully and accurately identify the driver's various emotional states, and it is difficult to perform intelligent traffic intervention and assistance based on the driver's emotional and behavioral characteristics.

[0003] However, the common solutions currently available have many shortcomings, including the following: existing technologies fail to conduct in-depth fusion analysis of vehicle driving data and physiological signal data to achieve intelligent decision-making support for traffic systems; they usually only process vehicle driving data in isolation, without fully leveraging the potential value of the combination of the two; they are unable to intelligently adjust traffic signals, provide personalized driving advice, or take proactive safety intervention measures based on the driver’s real-time emotional state and behavioral characteristics, and thus are unable to effectively improve the safety and efficiency of traffic operations. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above problems existing in the existing intelligent transportation system and its operation method, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide an intelligent transportation system and an operating method thereof, which are suitable for solving the problem that the prior art fails to perform deep fusion analysis of vehicle driving data and physiological signal data to achieve intelligent decision-making support for the transportation system, fails to give full play to the potential value contained in the combination of the two, and fails to intelligently adjust traffic signals according to the driver's real-time emotional state and behavioral characteristics, provide personalized driving suggestions or take active safety intervention measures, thereby failing to effectively improve the safety and efficiency of traffic operation.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for operating an intelligent transportation system, which includes using sensors to obtain vehicle driving data and physiological signal data in real time and preprocessing them; constructing an emotion recognition model based on the vehicle driving data and analyzing the user's behavior characteristics; identifying the emotional state in real time based on the user's behavior characteristics and physiological signal data; regularly calibrating the driver's emotion recognition and optimizing the emotion recognition model based on the calibration results.

[0009] As a preferred solution of the intelligent transportation system operation method described in the present invention, wherein: the sensor includes a physiological signal monitoring sensor and a vehicle driving data acquisition sensor; the vehicle driving data includes speed-related data, control data and driving trajectory data; the physiological signal data includes heart rate data, skin conductivity data and seat pressure distribution data; the user behavior characteristics include driving operation habit-related data, driving route selection and travel regularity data and vehicle usage pattern data.

[0010] As a preferred solution of the intelligent transportation system operation method described in the present invention, the specific steps of constructing the emotion recognition model are as follows: dividing the vehicle driving data and generating user behavior labels; constructing an emotion recognition model based on a recurrent neural network and extracting user behavior characteristics; using a machine learning algorithm to further analyze the extracted user behavior characteristics; evaluating the model and adjusting the model structure or parameters according to the evaluation results.

[0011] As a preferred solution of the intelligent transportation system operation method of the present invention, the specific formula for analyzing user behavior characteristics is as follows:

[0012]

[0013] Where B is the user's behavioral activity during driving; vh is the maximum speed; v l is the minimum speed; is the absolute value of the average steering angle; n br is the number of braking actions; is the average heart rate; ΔHR max is the maximum change value of heart rate; G max Maximum value of skin conductivity.

[0014] As a preferred embodiment of the intelligent transportation system operation method described in the present invention, the specific situation of the user's behavioral activity during driving is as follows: when the user's behavioral activity during driving is 0, it indicates an ideal and extremely stable driving behavior state, and the driver's physiological state is stable, without large fluctuations in heart rate and obvious incoordination of physiological signals due to factors such as emotions; when the user's behavioral activity during driving is between 0 and a first threshold, it indicates that the driver's driving behavior begins to become active, there will be some speed changes, increased steering operations and braking actions, but not to a dangerous level, the driver's physiological state will also fluctuate to a certain extent, the difference between heart rate changes and physiological signals begins to appear, and the system performs appropriate monitoring and reminders; when the user's behavioral activity during driving is greater than the first threshold, it indicates that the driver's driving behavior is very aggressive, with large speed fluctuations, frequent steering and a large number of braking operations, and the physiological state is also unstable, with large heart rate fluctuations and obvious incoordination between physiological signals. In this case, the system needs to take stronger measures.

[0015] As a preferred solution of the intelligent transportation system operation method of the present invention, the specific formula for real-time recognition of emotional state is as follows:

[0016]

[0017] Among them, E is a comprehensive indicator of the driver’s emotional state; is the average speed of the vehicle over a period of time; σ v is the speed standard deviation; θ max is the maximum steering angle; is the number of maximum steering angles; σ HR is the standard deviation of heart rate; G is the standard deviation of skin conductivity.

[0018] As a preferred embodiment of the intelligent transportation system operation method described in the present invention, the specific situation of the above-mentioned comprehensive index of the driver's emotional state is as follows: when the comprehensive index of the driver's emotional state is 0, it means that the driver is in an extremely calm state, the vehicle is stationary, there is no operation related to driving behavior, and the physiological state is relatively stable, and there is no obvious fluctuation of heart rate and skin conductivity caused by factors such as emotions; when the comprehensive index of the driver's emotional state is between 0 and the second threshold, it indicates that the driver's driving behavior begins to become active, there will be some speed changes and an increase in steering operations, but not to a dangerous level, the driver's physiological state will also fluctuate to a certain extent, and heart rate changes and skin conductivity changes begin to appear. For the emotional state in this interval, the system can perform appropriate monitoring and reminders; when the comprehensive index of the driver's emotional state is greater than the second threshold, it indicates that the driver's driving behavior is very aggressive, the speed fluctuates greatly, the steering is frequent, and the physiological state is also unstable, the heart rate fluctuates greatly and the skin conductivity fluctuates significantly, and the system needs to take stronger measures.

[0019] In the second aspect, in order to further solve the above-mentioned technical problems, the embodiments of the present invention provide an intelligent transportation system, which includes: a data collection module, used to obtain and pre-process vehicle driving data and physiological signal data; a model building module, used to build an emotion recognition model and analyze the user's behavioral characteristics; an emotion recognition module, used to identify the emotional state in real time according to the user's behavioral characteristics and physiological signal data; a model optimization module, used to optimize the emotion recognition model according to the calibration results.

[0020] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of an intelligent transportation system operation method as described in the first aspect of the present invention is implemented.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent transportation system operation method as described in the first aspect of the present invention is implemented.

[0022] The beneficial effects of the present invention are as follows: the present invention can provide early warning of potential dangerous driving behaviors by real-time identification of the driver's emotional state and behavioral characteristics, and can issue an alarm in time to remind the driver to stay calm and avoid traffic accidents caused by emotional loss of control. Based on the statistical analysis of a large amount of vehicle driving data and the driver's emotional state, the traffic management department can more accurately understand the traffic conditions and driver emotional distribution in different sections and at different times. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, and provides an intelligent transportation system operation method, comprising the following steps:

[0030] S1: Use sensors to obtain vehicle driving data and physiological signal data in real time and perform preprocessing.

[0031] Preferably, Figure 1 The figure shows the implementation process of the present invention. First, the vehicle driving data and physiological signal data are acquired in real time by using sensors and preprocessed. Then, an emotion recognition model is constructed based on the vehicle driving data and the user's behavior characteristics are analyzed. Then, the emotional state is recognized in real time based on the user's behavior characteristics and physiological signal data. Finally, the driver is regularly calibrated for emotion recognition and the emotion recognition model is optimized based on the calibration results.

[0032] Furthermore, the sensor includes a physiological signal monitoring sensor and a vehicle driving data acquisition sensor.

[0033] Furthermore, the vehicle driving data includes speed-related data, control data and driving trajectory data.

[0034] Furthermore, the physiological signal data includes heart rate data, skin conductivity data, and seat pressure distribution data.

[0035] Specifically, the preprocessing ensures the quality and consistency of the data by performing real-time preprocessing of vehicle driving data and physiological signal data on the data node, including data cleaning, noise reduction and standardization.

[0036] S2: Build an emotion recognition model based on vehicle driving data and analyze user behavior characteristics.

[0037] Preferably, the user behavior characteristics include data related to driving operation habits, driving route selection and travel regularity data, and vehicle usage pattern data.

[0038] Specifically, the steps for building an emotion recognition model are as follows: divide the vehicle driving data and generate user behavior labels.

[0039] An emotion recognition model is built based on a recurrent neural network and user behavior characteristics are extracted.

[0040] Use machine learning algorithms to further analyze the extracted user behavior features.

[0041] Evaluate the model and adjust the model structure or parameters based on the evaluation results.

[0042] Furthermore, the specific formula for analyzing user behavior characteristics is as follows:

[0043]

[0044] Where B is the user's behavioral activity during driving; v h is the maximum speed; v l is the minimum speed; is the absolute value of the average steering angle; n br is the number of braking actions; is the average heart rate; ΔHR max is the maximum change value of heart rate; G max Maximum value of skin conductivity.

[0045] Furthermore, a formula that can measure user behavior characteristics is constructed by integrating vehicle driving related data and driver physiological signal data. The core of this formula is to consider key factors that can reflect the aggressiveness of driving operations and changes in physiological states, and combine them in a reasonable mathematical form.

[0046] Furthermore, the specific situation of the user's behavioral activity level during driving is as follows: when the user's behavioral activity level during driving is 0, it indicates an ideal and extremely stable driving behavior state, and the driver's physiological state is stable, without large fluctuations in heart rate and obvious incoordination of physiological signals due to factors such as emotions.

[0047] When the user's behavioral activity level during driving is between 0 and the first threshold, it indicates that the driver's driving behavior begins to become active, with some speed changes, increased steering operations and braking actions, but not reaching a dangerous level. The driver's physiological state will also fluctuate to a certain extent, and the difference between heart rate changes and physiological signals will begin to appear, and the system will conduct appropriate monitoring and reminders.

[0048] When the user's behavioral activity during driving is greater than the first threshold, it indicates that the driver's driving behavior is very aggressive, with large speed fluctuations, frequent steering and a large number of braking operations, and an unstable physiological state, large heart rate fluctuations and obvious incoordination between physiological signals. In this case, the system needs to take more drastic measures.

[0049] Furthermore, the determination of the first threshold value needs to be based on statistical analysis of data, traffic rules and driving specifications, vehicle characteristics and driver characteristics, as well as verification through simulation and experiments.

[0050] S3: Identify emotional states in real time based on user behavior characteristics and physiological signal data.

[0051] Preferably, the specific formula for real-time recognition of emotional state is as follows:

[0052]

[0053] Among them, E is a comprehensive indicator of the driver’s emotional state; is the average speed of the vehicle over a period of time; σ v is the speed standard deviation; θ max is the maximum steering angle; is the number of maximum steering angles; σ HR is the standard deviation of heart rate; G is the standard deviation of skin conductivity.

[0054] Furthermore, the driver's emotional state is quantitatively identified by integrating vehicle driving-related data and driver physiological signal data. The core idea is to select key parameters that can reflect the aggressiveness of driving behavior and the degree of fluctuation of physiological state, and then combine them in a reasonable mathematical form so that the final formula result can reflect different emotional state characteristics.

[0055] Furthermore, the specific situation of the above-mentioned comprehensive index of the driver's emotional state is as follows: when the comprehensive index of the driver's emotional state is 0, it means that the driver is in an extremely calm state, the vehicle is stationary, there is no operation related to driving behavior, and the physiological state is relatively stable, and there is no obvious fluctuation in heart rate and skin conductivity due to factors such as emotions.

[0056] When the comprehensive indicator of the driver's emotional state is between 0 and the second threshold, it means that the driver's driving behavior begins to become active, with some speed changes and increased steering operations, but not reaching a dangerous level. The driver's physiological state will also fluctuate to a certain extent, with changes in heart rate and skin conductivity beginning to appear. For the emotional state in this range, the system can conduct appropriate monitoring and reminders.

[0057] When the comprehensive index of the driver's emotional state is greater than the second threshold, it indicates that the driver's driving behavior is very aggressive, with large speed fluctuations, frequent turns, and an unstable physiological state, large heart rate fluctuations and obvious skin conductivity fluctuations. The system needs to take more drastic measures.

[0058] Furthermore, the determination of the second threshold value needs to be based on data statistical analysis, traffic rules and driving specifications, vehicle characteristics and driver characteristics, as well as through simulation and experimental verification.

[0059] Preferably, the behavioral characteristics and physiological signal data are organically integrated to achieve more accurate identification of the driver's emotional state. This fusion method makes full use of the driver's operating intentions reflected by the behavioral data and the intrinsic emotional physiological reactions reflected by the physiological data, complementing and verifying each other, thereby improving the accuracy and reliability of emotion recognition.

[0060] S4: Regularly calibrate the driver's emotion recognition and optimize the emotion recognition model based on the calibration results.

[0061] Preferably, as time goes by, the driving environment changes, and the driver's personal status changes, the driver's emotions and behavior patterns may change. Regular calibration and optimization can enable the emotion recognition model to continuously adapt to these changes and maintain a high recognition accuracy. The driver's emotional response threshold may change over a period of time due to work pressure, life events and other factors. Through regular calibration, the model can adjust internal parameters based on the latest driver data to ensure that the judgment of the driver's emotional state is always accurate and reliable.

[0062] Preferably, optimizing the model using newly collected data is a data-driven continuous improvement mechanism. During each calibration process, the system will collect more actual driving data and corresponding emotional state information of the driver. These newly added data can be used to further improve the structure and parameters of the model and improve the generalization ability and performance of the model. As the system accumulates more driver data in different seasons, regions, and traffic conditions, the model can gradually learn a wider range of emotions and behavior patterns, so that it can better cope with various complex and changeable actual driving scenarios, and continuously improve its own intelligence level and practicality.

[0063] This embodiment also provides an intelligent transportation system, including: a data collection module, used to obtain vehicle driving data and physiological signal data and perform preprocessing; a model construction module, used to build an emotion recognition model and analyze the user's behavioral characteristics; an emotion recognition module, used to identify the emotional state in real time based on the user's behavioral characteristics and physiological signal data; a model optimization module, used to optimize the emotion recognition model according to the calibration results.

[0064] This embodiment also provides a computer device, which is applicable to a method for operating an intelligent transportation system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for operating an intelligent transportation system as proposed in the above embodiment.

[0065] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0066] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for operating an intelligent transportation system as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0067] In summary, the present invention can provide early warning of potential dangerous driving behaviors by real-time identification of the driver's emotional state and behavioral characteristics, and can issue an alarm in time to remind the driver to stay calm and avoid traffic accidents caused by emotional loss of control. Based on the statistical analysis of a large amount of vehicle driving data and the driver's emotional state, the traffic management department can more accurately understand the traffic conditions and driver emotional distribution in different sections and at different times.

[0068] Example 2

[0069] Referring to Tables 1 and 2, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0070] Table 1 shows the driver emotion and behavior data collected in this example according to the present invention and the prior art.

[0071] Table 1 Driver emotion and behavior data table

[0072]

[0073] Table 2 shows a detailed table of physiological indicators of driver's emotional state collected by the present invention and the prior art.

[0074] Table 2 Details of physiological indicators of emotional state

[0075]

[0076] It can be seen from the above table that the present invention can provide early warning of potential dangerous driving behaviors and avoid traffic accidents caused by emotional out-of-control. It can also accurately identify the driver's various emotional states and avoid traffic accidents caused by emotional out-of-control.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent transportation system operation method, characterized in that: include: Use sensors to obtain vehicle driving data and physiological signal data in real time and perform preprocessing; Building an emotion recognition model based on the vehicle driving data and analyzing the user's behavioral characteristics; Identify emotional states in real time based on the user's behavioral characteristics and physiological signal data; Regularly calibrate the driver's emotion recognition and optimize the emotion recognition model based on the calibration results.

2. The intelligent transportation system operation method according to claim 1, characterized in that: The sensors include physiological signal monitoring sensors and vehicle travel data acquisition sensors; The vehicle driving data includes speed-related data, control data and driving trajectory data; The physiological signal data includes heart rate data, skin conductivity data and seat pressure distribution data; The user behavior characteristics include data related to driving operation habits, driving route selection and travel regularity data, and vehicle usage pattern data.

3. The intelligent transportation system operation method according to claim 2, characterized in that: The specific steps of constructing the emotion recognition model are as follows: Dividing the vehicle driving data and generating user behavior tags; Build an emotion recognition model based on recurrent neural network and extract user behavior characteristics; Use machine learning algorithms to further analyze the extracted user behavior features; Evaluate the model and adjust the model structure or parameters based on the evaluation results.

4. The intelligent transportation system operation method according to claim 3, characterized in that: The specific formula for analyzing user behavior characteristics is as follows: Where B is the user's behavioral activity during driving; v h is the maximum speed; v l is the minimum speed; is the absolute value of the average steering angle; n br is the number of braking actions; is the average heart rate; ΔHR max is the maximum change value of heart rate; G max Maximum value of skin conductivity.

5. The intelligent transportation system operation method according to claim 4, characterized in that: The specific situation of the user's behavioral activity during driving is as follows: When the user's behavioral activity level during driving is 0, it indicates an ideal and extremely stable driving behavior state, and the driver's physiological state is stable, without significant fluctuations in heart rate and obvious incoordination of physiological signals caused by factors such as emotions; When the user's behavioral activity level during driving is between 0 and the first threshold, it indicates that the driver's driving behavior begins to become active, with some speed changes, increased steering operations and braking actions, but not reaching a dangerous level. The driver's physiological state also fluctuates to a certain extent, and the difference between heart rate changes and physiological signals begins to appear. The system conducts appropriate monitoring and reminders; When the user's behavioral activity during driving is greater than the first threshold, it indicates that the driver's driving behavior is very aggressive, with large speed fluctuations, frequent steering and a large number of braking operations, and an unstable physiological state, large heart rate fluctuations and obvious incoordination between physiological signals. In this case, the system needs to take more drastic measures.

6. The intelligent transportation system operation method according to claim 5, characterized in that: The specific formula for real-time recognition of emotional state is as follows: Among them, E is a comprehensive indicator of the driver’s emotional state; is the average speed of the vehicle over a period of time; σ v is the speed standard deviation; θ max is the maximum steering angle; is the number of maximum steering angles; σ HR is the standard deviation of heart rate; G is the standard deviation of skin conductivity.

7. The intelligent transportation system operation method according to claim 6, characterized in that: The specific conditions of the above-mentioned comprehensive indicators of the driver's emotional state are as follows: When the comprehensive index of the driver's emotional state is 0, it means that the driver is in an extremely calm state, the vehicle is stationary, there is no operation related to driving behavior, and the physiological state is relatively stable, without obvious fluctuations in heart rate and skin conductivity caused by factors such as emotions; When the comprehensive indicator of the driver's emotional state is between 0 and the second threshold, it means that the driver's driving behavior begins to become active, with some speed changes and increased steering operations, but not to a dangerous level. The driver's physiological state will also fluctuate to a certain extent, with changes in heart rate and skin conductivity beginning to appear. For the emotional state in this range, the system can conduct appropriate monitoring and reminders; When the comprehensive index of the driver's emotional state is greater than the second threshold, it indicates that the driver's driving behavior is very aggressive, with large speed fluctuations, frequent turns, and an unstable physiological state, large heart rate fluctuations and obvious skin conductivity fluctuations. The system needs to take more drastic measures.

8. An intelligent transportation system, based on an intelligent transportation system operation method according to any one of claims 1 to 7, characterized in that: include, A data collection module, used to obtain vehicle driving data and physiological signal data and perform pre-processing; Model building module, used to build emotion recognition model and analyze user behavior characteristics; Emotion recognition module, used to identify emotional states in real time based on user behavior characteristics and physiological signal data; The model optimization module is used to optimize the emotion recognition model according to the calibration results.

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 intelligent transportation system operation method described in any one of claims 1 to 7 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 an intelligent transportation system operation method according to any one of claims 1 to 7 are implemented.

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