A method and system for dynamic adjustment of intelligent vehicles based on user experience

Abstract

The present invention is applicable to the field of intelligent driving, and provides a method and system for dynamic adjustment of intelligent vehicles based on user experience. The system includes: an emotion and physiological monitoring module, an emotion analysis and behavior recognition module, a dynamic driving model construction module, and a driving state adjustment strategy module. A dynamic driving model is constructed by combining the intensity of emotions, non-driving physical behaviors, and the characteristics of the external environment of the vehicle. This model has a powerful simulation capability and can reflect the possible behavioral states of users under current driving conditions. By dynamically simulating the driver's state, the system can predict the driver's reaction in a specific situation, so as to take corresponding safety measures in advance. For example, if the model shows that the driver is prone to emotional fluctuations under complex road conditions, the system can actively adjust the vehicle's driving mode, reduce the speed, or increase monitoring of the environment, which not only prevents potential dangers from occurring, but also improves driving safety and comfort.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent driving, and in particular relates to a method and system for dynamically adjusting an intelligent vehicle based on user experience. Background Art

[0002] In recent years, physiological and emotional monitoring technologies have gradually been introduced into the field of intelligent driving. Wearable devices can be used to monitor the driver's heart rate and skin galvanic response in real time, assess their emotional state, and provide personalized suggestions for driving assistance. In addition, intelligent driving systems also rely on data analysis and machine learning, using large amounts of data for training to identify the complex relationship between the driver and the environment, thereby improving the level of intelligent decision-making.

[0003] Vehicle-to-everything (V2X) technology enables vehicles to communicate with their surroundings (such as other vehicles, traffic lights, and infrastructure) in real time to obtain critical information, thereby improving driving safety and efficiency. Through information sharing, vehicles can adjust driving strategies in a timely manner and actively respond to emergencies.

[0004] Traditional driver assistance systems usually only focus on the driving status of the vehicle, while ignoring the psychological and physiological status of the driver, which makes the system limited in dealing with the driver's emotional fluctuations. In addition, existing technologies often lack comprehensive consideration of environmental factors, which can easily lead to drivers' misjudgment in complex road conditions. They also lack the ability to dynamically predict the driver's behavior. This makes it impossible for the system to foresee the driver's possible reactions in complex situations, resulting in delayed safety measures. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for dynamic adjustment of an intelligent vehicle based on user experience, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is implemented in this way: a method for dynamically adjusting an intelligent vehicle based on user experience, the method comprising:

[0007] Read the driver's emotional state and physiological reactions through facial recognition and heart rate monitoring, while monitoring the environment inside and outside the car to identify additional traffic signs and surrounding hazards;

[0008] Analyze the collected driver's emotional state, determine and dynamically generate a driver's emotional intensity score, and obtain the driver's body posture based on physiological response data, identify non-driving body behaviors, evaluate body state and driving behavior, including passive reactions, and identify the excitement of the surrounding environment during driving based on the characteristics of the inside and outside of the car, and determine the interference with the user's driving attention;

[0009] Combine emotion intensity, non-driving physical behavior, and external environment characteristics to build a dynamic driving model to simulate the driving state that the user may experience under current conditions;

[0010] Define the various parameter ranges required for a reasonable driving state, analyze the relationship between the current driving state and the reasonable driving state, and generate and implement an adjustment strategy for the current driving state.

[0011] As a further solution of the present invention, the method of reading the driver's emotional state and physiological response through facial recognition and heart rate monitoring, while monitoring the environment outside the vehicle, identifying additional traffic signals and surrounding dangerous objects, specifically includes:

[0012] Capture the driver's facial expressions and analyze facial emotional characteristics;

[0013] Use the data acquisition equipment in the car to obtain the driver's heart rate data and muscle reaction in real time, and analyze the changes in heart rate and muscle tension;

[0014] Identify the driver's posture, analyze the torso tilt and center of gravity changes, and record all passive reactions;

[0015] Use onboard equipment to monitor roads, traffic signals and potential obstacles, and measure the excitement potential of the surrounding environment in real time.

[0016] As a further solution of the present invention, the collected emotional state of the driver is analyzed, and the driver's emotional intensity score is determined and dynamically generated. At the same time, the driver's body posture is obtained based on the physiological response data, non-driving body behavior is identified, and the body state and driving behavior are evaluated, including passive reactions. At the same time, based on the characteristics of the external environment of the vehicle, the excitement level of the surrounding environment during driving is identified, and the interference with the user's driving attention is determined, which specifically includes:

[0017] Identify the driver's current emotion category based on facial emotion features, calculate the emotion intensity, quantify the emotional state, and analyze the autonomic nervous system activity in combination with efficiency data to determine the emotional tension;

[0018] Identify the driver's movement patterns and fluctuations based on the driver's posture, and analyze unexpected behaviors;

[0019] Based on the characteristics of the environment inside and outside the vehicle, the complexity of the road conditions is analyzed and the excitement level is summarized to quantify the environmental risk.

[0020] As a further solution of the present invention, the analysis of unexpected behavior specifically includes:

[0021] Categorize behavioral patterns, including expected and unexpected behaviors;

[0022] The expected behavior refers to all driving operations that can be foreseen during this driving process;

[0023] Define a baseline of behavior under normal driving conditions and identify behaviors that exceed the baseline

[0024] Analyze the corresponding patterns in the environment under specific conditions, analyze potential driving factors, and determine whether unexpected behaviors are superimposed on systemic interference to cause safety hazards;

[0025] The systemic interference includes the influence of the in-vehicle environment and the influence of the out-vehicle environment.

[0026] As a further solution of the present invention, the combination of emotion intensity, non-driving physical behavior and external environment characteristics to build a dynamic driving model to simulate the driving state that the user may experience under current conditions specifically includes:

[0027] Data on emotion intensity, physical behavior, and environmental characteristics were summarized;

[0028] Examining the linear relationships between internally interconnected emotions and behaviors , determine the user's potential reaction behavior and trend, and determine the current driving condition factors and analyze their correlation with user behavior;

[0029] Build a dynamic driving model to simulate possible driving behaviors and their impact paths based on the current driving environment, and calculate the accident ratio conversion coefficient ;

[0030] For each output, cross-validate the consistency of conclusions from different data sources and analyze whether changes in external conditions are often accompanied by changes in user posture.

[0031] As a further aspect of the present invention, the linear relationship between the internally interconnected emotions and behaviors is examined. , determine the user's potential response behavior and trends, specifically:

[0032] ;

[0033] in, represents the sentiment data value, Represents the behavior data value, and Indicates the average value of the data;

[0034] ;

[0035] in, represents the predicted driving behavior indicator, is the measured emotion intensity, is a constant term, The slope represents the effect of emotion intensity on driving behavior.

[0036] As a further solution of the present invention, the calculation of the accident ratio conversion coefficient , specifically:

[0037] ;

[0038] in, is a behavioral response factor, which reflects the impact of the driver’s emotions and behavioral status on the accident risk. is a system response indicator that considers the impact of specific physiological responses on driving decisions and reaction time. For external vibration factors, consider the impact of physical interference outside the vehicle on stability. Represents the normalized ratio.

[0039] 8. The method according to claim 7, characterized in that the definition of each parameter interval required for a reasonable driving state, analyzing the relationship between the current driving state and the reasonable driving state, and generating and implementing an adjustment strategy for the current driving state specifically includes:

[0040] Determine the reasonable evaluation range of parameters such as heart rate, emotion score, and vehicle stability, and compare the real-time driving parameters with the preset reasonable range to analyze whether there is any deviation;

[0041] Conduct multi-dimensional analysis of emotional, behavioral, and environmental data to identify potential correlations between the data;

[0042] Generate driving adjustment strategies based on associated influences and current driving parameters.

[0043] Another object of the present invention is to provide a smart car dynamic adjustment system based on user experience, the system comprising:

[0044] Emotion and physiological monitoring module, which is used to read the driver's emotional state and physiological response through facial recognition and heart rate monitoring, while monitoring the environment inside and outside the car to identify additional traffic signs and surrounding hazards;

[0045] The emotion analysis and behavior recognition module is used to analyze the collected driver's emotional state, determine and dynamically generate the driver's emotion intensity score, and obtain the driver's body posture based on physiological response data, identify non-driving body behaviors, and evaluate body state and driving behavior, including passive reactions. At the same time, based on the characteristics of the inside and outside environment of the car, it identifies the excitement level of the surrounding environment during driving and determines the interference with the user's driving attention;

[0046] The dynamic driving model building module is used to combine the intensity of emotions, non-driving physical behaviors and external environment characteristics to build a dynamic driving model to simulate the driving state that the user may experience under current conditions;

[0047] The driving state adjustment strategy module is used to define the various parameter ranges required for a reasonable driving state, analyze the relationship between the current driving state and the reasonable driving state, and generate and implement the adjustment strategy for the current driving state.

[0048] The beneficial effects of the present invention are:

[0049] By analyzing the driver's emotional state and physiological reactions, the system can generate a dynamic emotion intensity score and identify the driver's body posture and non-driving behavior. This process enables the system to evaluate the driver's psychological state and physical reactions in real time and identify potential interference factors. For example, if the driver's emotional score shows high anxiety or fatigue, the system can issue a warning in time and advise the driver to take a break or adjust the driving style. This detailed analysis of the driver's emotional state not only improves the sensitivity to the driver's psychological changes, but also provides the necessary basis for subsequent adjustments to the driving style, thereby effectively reducing the risk of accidents caused by emotional fluctuations. In addition, combined with the evaluation of the characteristics of the environment inside and outside the car, the system can fully identify possible interference sources during driving, such as an overly noisy environment or frequent traffic signal changes, which provides data support for optimizing the driving environment.

[0050] A dynamic driving model is constructed by combining emotional intensity, non-driving physical behavior and external environmental characteristics. This model has a strong simulation capability and can reflect the user's possible behavioral state under current driving conditions. By dynamically simulating the driver's state, the system can predict the driver's reaction in a specific situation and take corresponding safety measures in advance. For example, if the model shows that the driver is prone to emotional fluctuations under complex road conditions, the system can actively adjust the vehicle's driving mode, reduce speed or increase monitoring of the environment, which not only prevents potential dangers, but also improves driving safety and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of a method for dynamic adjustment of a smart car based on user experience provided by an embodiment of the present invention;

[0052] Figure 2 A flowchart for reading the emotional state and physiological response of the driver, monitoring the environment inside and outside the vehicle, and identifying additional traffic signals and surrounding hazards provided by an embodiment of the present invention;

[0053] Figure 3A flow chart for determining and dynamically generating a driver's emotion intensity score, identifying non-driving physical behaviors, and identifying the excitement level of the surrounding environment during driving provided by an embodiment of the present invention;

[0054] Figure 4 A flowchart of combining emotion intensity, non-driving physical behavior and external environment characteristics to build a dynamic driving model and simulate the driving state that the user may experience under current conditions, provided in an embodiment of the present invention;

[0055] Figure 5 A flowchart for defining various parameter intervals required for a reasonable driving state provided by an embodiment of the present invention, analyzing the relationship between the current driving state and the reasonable driving state, and generating and implementing an adjustment strategy for the current driving state;

[0056] Figure 6 A structural block diagram of a smart car dynamic adjustment system based on user experience provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0059] Figure 1 A flowchart of a method for dynamically adjusting a smart car based on user experience provided by an embodiment of the present invention, such as Figure 1 As shown, a method for dynamic adjustment of a smart car based on user experience includes:

[0060] S100, which reads the driver's emotional state and physiological reactions through facial recognition and heart rate monitoring, while also monitoring the vehicle's internal and external environment to identify additional traffic signs and surrounding hazards;

[0061] In this step, the driver's facial image is captured in real time through high-precision cameras and advanced computer vision algorithms, and key facial feature points are analyzed to identify and quantify micro-expressions, such as anger, anxiety, and calmness. Subsequently, the in-car acquisition equipment is used to obtain the driver's heart rate changes and muscle reactions in real time. The heart rate monitoring device reflects the driver's tension or comfort through wearable devices or on-board sensors, while the electromyography (EMG) sensor monitors muscle tension to evaluate physiological load. At the same time, sensor technology is used to monitor the driver's body posture in real time, including the torso tilt angle and center of gravity changes, so as to identify the driver's tension or relaxation state, and record non-active reactions such as hand movements and head rotation, so as to evaluate the degree of attention to the surrounding environment. Finally, the combination of technologies such as lidar, cameras and ultrasonic sensors is used to monitor the road conditions, traffic signs and signal light status around the vehicle, and evaluate the excitement potential of the external environment by analyzing factors such as traffic flow, pedestrian activities and weather changes, providing basic data for subsequent driving state adjustments.

[0062] Real-time capture of the driver's emotions and physiological reactions can not only quickly identify and adapt to individual differences, making the vehicle's control system more humane and intelligent, but also accurately reflect the driver's actual driving status through a multi-dimensional state assessment model, thereby enhancing driving safety. In dangerous situations, real-time monitoring of the driver's emotional changes and physical reactions can enable preventive measures to be taken in advance to improve safety. In addition, data-driven analysis can identify and optimize factors that cause interference in the driving experience, such as adjusting the in-car music or seat comfort to adapt to the driver's emotional state and provide a personalized driving mode. Combining external environmental data with the driver's physiological state, the system can also provide the driver with intelligent decision-making support, enhance driving safety and comfort, thereby achieving dynamic adjustment and optimization of smart cars in a changing environment, and bringing a safer and more comfortable driving experience to the driver.

[0063] like Figure 2 As shown, the driver's emotional state and physiological response are read through facial recognition and heart rate monitoring, while the external environment is monitored to identify additional traffic signals and surrounding hazards, including:

[0064] S110, captures the driver’s facial expressions and analyzes facial emotional features;

[0065] S120, using the collection equipment in the car to obtain the driver's heart rate data and muscle reaction in real time, and analyze the changes in heart rate and muscle tension;

[0066] S130, identifying the driver’s posture, analyzing the torso tilt and center of gravity changes, and recording all passive responses;

[0067] The S140 uses onboard equipment to monitor roads, traffic signals and potential obstacles, and measures the excitement potential of the surrounding environment in real time.

[0068] S200, analyzing the collected emotional state of the driver, determining and dynamically generating a driver's emotional intensity score, acquiring the driver's body posture based on the physiological response data, identifying non-driving body behaviors, evaluating the body state and driving behaviors, including passive reactions, and identifying the excitement of the surrounding environment during driving based on the characteristics of the vehicle's interior and exterior environments, and determining interference with the user's driving attention;

[0069] In this step, the system can accurately determine the driver's current emotion category and calculate the intensity of the emotion by identifying facial emotion features. This process involves a detailed analysis of facial expressions, using machine learning algorithms to connect facial micro-expressions with specific emotions and quantify the strength of emotional states. At the same time, the driver's emotional tension is determined by combining heart rate data and the activity of the autonomic nervous system. This comprehensive analysis can not only reflect the driver's psychological state, but also indicate the stress level during driving.

[0070] Secondly, based on the driver's body posture, the system identifies its movement patterns and fluctuations, and analyzes possible unexpected behaviors. By monitoring the driver's posture changes, hand movements and other passive reactions through high-precision sensors, the system can determine whether the driver has any unexpected behaviors during driving. In addition, the system will define a baseline value for behavior under normal driving conditions, use this as a reference, identify behaviors that exceed this baseline value, and then parse out the patterns that correspond to the environment under specific conditions, analyze potential driving factors, and determine whether unexpected behaviors cause safety hazards due to systemic interference.

[0071] By integrating the driver's emotional state, physical reactions and environmental characteristics, the system can form a comprehensive understanding of the driver's psychological and physiological state. This multi-dimensional assessment not only improves the real-time monitoring capability of driving safety, but also effectively identifies potential safety hazards. When the driver is in a state of high tension, the system can respond in time, adjust vehicle settings or provide warnings to help the driver maintain the best driving state and reduce the risk of accidents. In addition, by identifying unexpected behaviors, the system can optimize the driving experience, provide personalized feedback and suggestions, and enhance the driver's comfort and sense of security.

[0072] like Figure 3As shown, the collected emotional state of the driver is analyzed, the driver's emotional intensity score is determined and dynamically generated, and the driver's body posture is obtained based on the physiological response data, non-driving body behavior is identified, and the body state and driving behavior, including passive reactions, are evaluated. At the same time, based on the characteristics of the external environment, the excitement of the surrounding environment during driving is identified, and the interference with the user's driving attention is determined, specifically including:

[0073] S210, identifying the driver's current emotion category based on facial emotion features, calculating the emotion intensity, quantifying the emotional state, and analyzing the autonomic nervous system activity in combination with efficiency data to determine the emotional tension;

[0074] S220, identifying the driver's action pattern and its fluctuation based on the driver's posture, and analyzing unexpected behavior;

[0075] S230 analyzes the complexity of road conditions and summarizes the excitement level based on the characteristics of the vehicle's internal and external environments, and quantifies the environmental risk.

[0076] In this embodiment, the analysis of unexpected behavior specifically includes:

[0077] Categorize behavioral patterns, including expected and unexpected behaviors;

[0078] The expected behavior refers to all driving operations that can be foreseen during this driving process;

[0079] The categorized behaviors are “expected (e.g., driving at a constant speed)” and “unexpected (sudden braking, sudden gripping of the steering wheel, unconscious foot application of the brake or accelerator)”

[0080] Define a baseline of behavior under normal driving conditions and identify behaviors that exceed the baseline;

[0081] Analyze the corresponding patterns in the environment under specific conditions, analyze potential driving factors, and determine whether unexpected behaviors are superimposed on systemic interference to cause safety hazards;

[0082] The systemic interference includes the influence of the in-vehicle environment and the influence of the out-vehicle environment.

[0083] Systematic interference can include many factors, not just the external environment, such as:

[0084] 1. External environment

[0085] Traffic conditions: For example, congestion, complex intersections, strong winds or adverse weather conditions.

[0086] External sounds: Sudden or continuous noises, such as horns or construction noises.

[0087] 2. In-car environment

[0088] Audio or navigation equipment: excessive volume, sudden changes in sound, or incorrect navigation instructions.

[0089] Interior lighting: Strong glare from brake lights or changes in brightness on large screens.

[0090] 3. Technical and system anomalies

[0091] In-vehicle system: abnormal alert caused by sensor system failure or software error.

[0092] Mechanical Problems: Sudden mechanical failures or maintenance reminders (such as the low oil pressure light coming on).

[0093] These systemic disturbances may have a superimposed effect with the driver's unexpected behavior, leading to passive safety hazards. Therefore, comprehensive identification and mature response to systemic disturbances are crucial.

[0094] S300 combines emotion intensity, non-driving physical behavior and external environment characteristics to build a dynamic driving model to simulate the driving state that the user may experience under current conditions;

[0095] This step comprehensively summarizes the emotion intensity, non-driving physical behavior, and external environment characteristic data to build a dynamic driving model that simulates the possible driving state of the user under the current driving conditions. First, by summarizing the collected emotion and behavior data, the linear relationship between the internally interconnected emotions and behaviors is analyzed. This process aims to determine the user's potential reaction behavior and trend. Specifically, the system will use statistical methods to perform regression analysis on the emotion data values ​​and behavior data values ​​to determine the impact of emotion intensity on user behavior. This analysis not only helps to identify how emotional fluctuations lead to changes in driving behavior, but also provides theoretical support for subsequent driving mode adjustments.

[0096] Next, the current driving conditions are considered and analyzed in relation to the user's behavior. For example, the system monitors changes in the external environment, such as weather, traffic flow, and road conditions, and assesses how these factors affect the driver's emotional and physical reactions. This comprehensive assessment provides the necessary input data for building a dynamic driving model, which not only simulates driving behaviors that may occur in the current driving environment, but also calculates accident ratio conversion factors to reflect the risk level under different driving conditions.

[0097] In model construction, it is crucial to cross-validate the consistency of conclusions from different data sources. By analyzing the relationship between changes in external conditions and changes in user posture, the system can identify the driver's potential response in a specific environment. This process includes the calculation of emotional intensity and behavioral response factors to assess the impact of the driver's emotional and behavioral state on accident risk. At the same time, the system considers the impact of physiological reactions on driving decisions and reaction time, and normalizes external vibration factors to ensure that the model can accurately reflect the complexity of the actual driving environment.

[0098] By integrating emotions, physical behavior and environmental characteristics, the system is able to form a highly dynamic driving model that reflects the driver's psychological and physiological state in real time. This multi-dimensional analysis not only improves the understanding of driver behavior, but also effectively predicts potential driving risks. When the system identifies emotional fluctuations or physical discomfort, it can take timely measures, such as adjusting the driving mode or providing warnings, to help drivers stay safe in complex environments. In addition, by cross-validating different data sources, the accuracy and reliability of the model are ensured, thereby enhancing the personalization and safety of the driving experience.

[0099] like Figure 4 As shown, the emotional intensity, non-driving physical behavior and external environment characteristics are combined to build a dynamic driving model to simulate the driving state that the user may experience under current conditions, including:

[0100] S310, summarizing the data on emotion intensity, physical behavior, and environmental characteristics;

[0101] S320, examine the linear relationship between internally interconnected emotions and behaviors , determine the user's potential reaction behavior and trend, and determine the current driving condition factors and analyze their correlation with user behavior;

[0102] S330: Build a dynamic driving model, simulate possible driving behaviors and their impact paths based on the current driving environment, and calculate the accident ratio conversion coefficient ;

[0103] S340, for each output, cross-validate the consistency of conclusions from different data sources, and analyze whether changes in external vehicle conditions are often accompanied by changes in user posture.

[0104] In this embodiment, the linear relationship between the internally interconnected emotions and behaviors is examined. , determine the user's potential response behavior and trends, specifically:

[0105] ;

[0106] in, represents the sentiment data value, Represents the behavior data value, and Indicates the average value of the data;

[0107] ;

[0108] in, represents the predicted driving behavior indicator, is the measured emotion intensity, is a constant term, The slope represents the effect of emotion intensity on driving behavior.

[0109] In this embodiment, the calculation of the accident ratio conversion coefficient , specifically:

[0110] ;

[0111] in, is a behavioral response factor, which reflects the impact of the driver’s emotions and behavioral status on the accident risk. is a system response indicator that considers the impact of specific physiological responses on driving decisions and reaction time. For external vibration factors, consider the impact of physical interference outside the vehicle on stability. Represents the normalized ratio.

[0112] S400, defining various parameter intervals required for a reasonable driving state, analyzing the relationship between the current driving state and the reasonable driving state, and generating and implementing an adjustment strategy for the current driving state.

[0113] By monitoring parameters such as heart rate, emotion score and vehicle stability, the normal range in different driving situations is identified. For example, in tense or complex road conditions, the driver's heart rate may rise significantly, while it remains stable in smooth driving conditions. Based on historical data, the system generates a personalized normal heart rate range, and defines the normal fluctuation range of emotional state based on the emotion intensity score generated in step S200, which can effectively identify the impact of different emotional states such as mild anxiety and high-intensity anger. At the same time, vehicle sensors are used to monitor data such as acceleration, steering angle and stability control to evaluate the vehicle's handling and set a reasonable range for stability parameters.

[0114] Next, the system monitors the real-time driving parameters and compares them with the set reasonable range to analyze whether there are obvious deviations. Through real-time monitoring, the system can automatically record significant abnormalities and take corresponding measures. For a more comprehensive assessment, the system analyzes emotional, behavioral and environmental data in multiple dimensions, using data mining and machine learning techniques to identify potential correlations between the data. For example, the system analyzes how the driver's emotional changes affect their reaction time, driving behavior and vehicle handling stability, and then uses statistical regression analysis to clarify the degree of influence of different factors on driving behavior.

[0115] Finally, based on the analysis results, the system generates adjustment strategies for the current driving state. These strategies are based not only on real-time data but also on historical data to ensure effectiveness and safety. For example, if the heart rate is higher than the normal range and the emotional score shows anxiety, the system may suggest that the driver take a break or reduce the driving speed; in a complex traffic environment, if distraction is detected, the system can lower the music volume or provide an audio warning to help the driver concentrate. The significant advantages of the S400 procedure are its comprehensiveness, real-time and personalization. Through multi-dimensional data analysis, it can comprehensively evaluate the driver's physiological, emotional and environmental status to ensure a deep understanding of the overall situation. Real-time monitoring and comparison of driving parameters with reasonable intervals enable the system to respond quickly and adjust driving strategies in time, thereby reducing the risk of accidents. In addition, based on personal historical data and real-time monitoring, the personalized driving adjustment strategies formulated by the system meet the needs of different drivers and improve the comfort and safety of the driving experience. These advantages not only improve the safety and driving experience of smart cars, but also promote intelligent interaction between users and vehicles, and promote the development of smart car technology.

[0116] like Figure 5 As shown, the definition of each parameter interval required for a reasonable driving state, analyzing the relationship between the current driving state and the reasonable driving state, generating and implementing an adjustment strategy for the current driving state, specifically includes:

[0117] S410, determining a reasonable evaluation range of parameters such as heart rate, emotion score, and vehicle stability, and comparing the real-time driving parameters with a preset reasonable range to analyze whether there is a deviation;

[0118] S420, performing multi-dimensional analysis on the emotion, behavior, and environment data to identify potential correlation effects between the data;

[0119] S430: Generate a driving adjustment strategy based on the associated impact and the current driving parameters.

[0120] Figure 6 The structural block diagram of the intelligent automobile dynamic adjustment system based on user experience provided by an embodiment of the present invention is as follows: Figure 6 As shown, a smart car dynamic adjustment system based on user experience, the system includes:

[0121] The emotion and physiological monitoring module 100 is used to read the driver's emotional state and physiological response through facial recognition and heart rate monitoring, and monitor the environment inside and outside the vehicle to identify additional traffic signs and surrounding hazards;

[0122] The emotion analysis and behavior recognition module 200 is used to analyze the collected emotional state of the driver, determine and dynamically generate the driver's emotion intensity score, and obtain the driver's body posture based on the physiological response data, identify non-driving body behaviors, evaluate the body state and driving behavior, including passive reactions, and identify the excitement of the surrounding environment during driving based on the characteristics of the inside and outside of the car, and determine the interference with the user's driving attention;

[0123] A dynamic driving model building module 300 is used to combine the emotion intensity, non-driving physical behavior and the characteristics of the vehicle's external environment to build a dynamic driving model to simulate the driving state that the user may experience under current conditions;

[0124] The driving state adjustment strategy module 400 is used to define various parameter intervals required for a reasonable driving state, analyze the relationship between the current driving state and the reasonable driving state, and generate and implement an adjustment strategy for the current driving state.

[0125] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0127] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dynamic adjustment of intelligent vehicles based on user experience, characterized in that: The method comprises: Read the driver's emotional state and physiological reactions through facial recognition and heart rate monitoring, while monitoring the environment inside and outside the car to identify additional traffic signs and surrounding hazards; Analyze the collected driver's emotional state, determine and dynamically generate a driver's emotional intensity score, and obtain the driver's body posture based on physiological response data, identify non-driving body behaviors, evaluate body state and driving behavior, including passive reactions, and identify the excitement of the surrounding environment during driving based on the characteristics of the inside and outside of the car, and determine the interference with the user's driving attention; Summarize the data of emotion intensity, physical behavior and environmental characteristics; examine the linear relationship between internally interconnected emotions and behaviors, judge the user's potential reaction behavior and trend, determine the current driving condition factors, and analyze their association with user behavior; build a dynamic driving model, simulate possible driving behaviors and their impact paths based on the current driving environment, and calculate the accident ratio conversion coefficient ARC ; For each output, cross-validate the consistency of conclusions from different data sources and analyze whether changes in external conditions are often accompanied by changes in user posture; the linear relationship between internally interconnected emotions and behaviors is examined , determine the user's potential response behavior and trends, specifically: ； in, represents the sentiment data value, Represents the behavior data value, and Indicates the average value of the data; ； in, represents the predicted driving behavior indicator, is the measured emotion intensity, is a constant term, The slope represents the effect of emotion intensity on driving behavior; Calculate the accident ratio conversion factor , specifically: ； in, is a behavioral response factor, which reflects the impact of the driver’s emotions and behavioral status on the accident risk. is a system response indicator that considers the impact of specific physiological responses on driving decisions and reaction time. For external vibration factors, consider the impact of physical interference outside the vehicle on stability. represents the normalized ratio; Define the various parameter ranges required for a reasonable driving state, analyze the relationship between the current driving state and the reasonable driving state, and generate and implement an adjustment strategy for the current driving state.

2. The method according to claim 1, characterized in that The method uses facial recognition and heart rate monitoring to read the driver's emotional state and physiological response, and monitors the environment outside the vehicle to identify additional traffic signals and surrounding hazards, including: Capture the driver's facial expressions and analyze facial emotional characteristics; Use the data acquisition equipment in the car to obtain the driver's heart rate data and muscle reaction in real time, and analyze the changes in heart rate and muscle tension; Identify the driver's posture, analyze the torso tilt and center of gravity changes, and record all passive reactions; Use onboard equipment to monitor roads, traffic signals and potential obstacles, and measure the excitement potential of the surrounding environment in real time.

3. The method according to claim 2, characterized in that The collected driver's emotional state is analyzed, and the driver's emotional intensity score is determined and dynamically generated. At the same time, the driver's body posture is obtained based on the physiological response data, non-driving body behavior is identified, and the body state and driving behavior, including passive reactions, are evaluated. At the same time, based on the characteristics of the external environment, the excitement of the surrounding environment during driving is identified, and the interference with the user's driving attention is determined, specifically including: Identify the driver's current emotion category based on facial emotion features, calculate the emotion intensity, quantify the emotional state, and analyze the autonomic nervous system activity in combination with efficiency data to determine the emotional tension; Identify the driver's movement patterns and fluctuations based on the driver's posture, and analyze unexpected behaviors; Based on the characteristics of the environment inside and outside the vehicle, the complexity of the road conditions is analyzed and the excitement level is summarized to quantify the environmental risk.

4. The method according to claim 3, characterized in that The analysis of unexpected behaviors specifically includes: Categorize behavioral patterns, including expected and unexpected behaviors; The expected behavior refers to all driving operations that can be foreseen during this driving process; Define a baseline of behavior under normal driving conditions and identify behaviors that exceed the baseline; Analyze the corresponding patterns in the environment under specific conditions, analyze potential driving factors, and determine whether unexpected behaviors are superimposed on systemic interference to cause safety hazards; The systemic interference includes the influence of the in-vehicle environment and the influence of the out-vehicle environment.

5. The method according to claim 1, characterized in that The definition of each parameter range required for a reasonable driving state, analyzing the relationship between the current driving state and the reasonable driving state, and generating and implementing an adjustment strategy for the current driving state specifically include: Determine the reasonable evaluation range of heart rate, emotion score, and vehicle stability parameters, and compare the real-time driving parameters with the preset reasonable range to analyze whether there is any deviation; Conduct multi-dimensional analysis of emotional, behavioral, and environmental data to identify potential correlations between the data; Generate driving adjustment strategies based on associated influences and current driving parameters.

6. A smart car dynamic adjustment system based on user experience, characterized in that: The system comprises: Emotion and physiological monitoring module, which is used to read the driver's emotional state and physiological reactions through facial recognition and heart rate monitoring, while monitoring the environment inside and outside the car to identify additional traffic signs and surrounding hazards; The emotion analysis and behavior recognition module is used to analyze the collected driver's emotional state, determine and dynamically generate the driver's emotion intensity score, and obtain the driver's body posture based on physiological response data, identify non-driving body behaviors, and evaluate body state and driving behavior, including passive reactions. At the same time, based on the characteristics of the inside and outside environment of the car, it identifies the excitement level of the surrounding environment during driving and determines the interference with the user's driving attention; Dynamic driving model building module, used to summarize emotion intensity, physical behavior and environmental characteristics data; check the linear relationship between internally interconnected emotions and behaviors, judge the user's potential reaction behavior and trend, determine the current driving condition factors, and analyze their association with user behavior; build a dynamic driving model, simulate possible driving behaviors and their impact paths based on the current driving environment, and calculate the accident ratio conversion coefficient ARC ; For each output, cross-validate the consistency of conclusions from different data sources and analyze whether changes in external conditions are often accompanied by changes in user posture; the linear relationship between internally interconnected emotions and behaviors is examined , determine the user's potential response behavior and trends, specifically: ； in, represents the sentiment data value, Represents the behavior data value, and Indicates the average value of the data; ； in, represents the predicted driving behavior indicator, is the measured emotion intensity, is a constant term, The slope represents the effect of emotion intensity on driving behavior; Calculate the accident ratio conversion factor , specifically: ； in, is a behavioral response factor, which reflects the impact of the driver’s emotions and behavioral status on the accident risk. is a system response indicator that considers the impact of specific physiological responses on driving decisions and reaction time. For external vibration factors, consider the impact of physical interference outside the vehicle on stability. represents the normalized ratio; The driving state adjustment strategy module is used to define the various parameter ranges required for a reasonable driving state, analyze the relationship between the current driving state and the reasonable driving state, and generate and implement the adjustment strategy for the current driving state.

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