A method and system for driver behavior assessment and prediction based on driving scenarios

Abstract

The application relates to the technical field of intelligent driving, and particularly discloses a driver behavior evaluation and prediction method and system based on a driving scene, which comprises the following steps: monitoring and extracting information of a driving scene, in-vehicle personnel and a driver state; establishing a driving scene and environment model according to the driving scene, establishing a vehicle use scene model according to the in-vehicle personnel information, and generating a driver model, intelligent driving performance parameters and driver behavior evaluation parameters according to the driver state information of the driver in different driving modes; comprehensively evaluating the driving behavior and driving style of the driver in the manual driving process based on the driving scene and environment model, the vehicle use scene model and the driver model, so as to adjust and predict the intelligent driving performance parameters and the driver behavior evaluation parameters; and optimizing and adjusting the driving strategy in the intelligent driving process based on the driving performance parameters and the driver behavior evaluation parameters, so as to improve the interactive experience of the driver and improve the safety of the intelligent driving.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and more specifically to a method and system for driver behavior assessment and prediction based on driving scenarios. Background Technology

[0002] With the continuous development of intelligent driving assistance technology, more and more functions are being applied to intelligent driving vehicles, bringing great convenience to users. However, due to insufficient understanding of these functions, there is over-reliance on and misuse of driver assistance features, posing safety risks to users. To further enhance the intelligence of driver assistance, enabling vehicles to better understand drivers and drivers to better understand vehicles, it is necessary to assess and predict drivers' driving styles. This will allow drivers to better understand the boundaries of assistance functions, and for assistance functions to better understand the driver's reactions and driving ability limits, thereby providing a safer and more intelligent interactive experience.

[0003] Current methods for assessing driver behavior mainly involve two approaches: one is to use driver control actuators, such as the driver's throttle opening and steering angle, to rate the driver's style; the other is to use a driver monitoring system (DMS) to perceive and recognize the driver's face and movements, determine the driver's level of focus, and issue warnings for unsafe driving states such as distraction or drowsiness. However, the information input by these two methods is relatively limited, and the assessment information based on the rating system is also quite broad. It is difficult to adapt to complex and ever-changing scenarios if the judgment is based solely on the driver's behavior inside the vehicle.

[0004] The driving behavior assessment and prediction method based on driving scenarios provided by this invention addresses the problems mentioned above, such as excessive reliance due to drivers' insufficient understanding of the capabilities of intelligent assisted driving functions, and untimely warnings or poor interactive experiences caused by insufficient assessment of different driver styles by assisted intelligent driving systems. It also overcomes the limitation of assessment information based solely on driver actions and monitoring. Summary of the Invention

[0005] To achieve the objectives of this invention, this application provides a method for driver behavior assessment and prediction based on driving scenarios, comprising:

[0006] Step S1: Monitor and extract information on the driving scenario, in-vehicle occupants, and driver status;

[0007] Step S2: Establish a driving scenario and environment model based on the driving scenario, establish a vehicle usage scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters, and driver behavior evaluation parameters based on the driver's state information in different driving modes.

[0008] Step S3: Based on the driving scenario and environment model, vehicle usage scenario model and driver model, comprehensively evaluate the driver's driving behavior and driving style during manual driving, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters.

[0009] Step S4: Optimize and adjust the driving strategy during the intelligent driving process based on the driving performance parameters and driver behavior evaluation parameters.

[0010] In some specific embodiments, step S1 includes:

[0011] The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features.

[0012] The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information.

[0013] The in-vehicle monitoring system can be used to monitor the number of passengers and their seating arrangement, or the external monitoring system can be used to monitor the type, weight, and volume of cargo carried in the vehicle.

[0014] In some specific embodiments, in step S2, a convolutional neural network is used to extract features from the collected data of different driving scenarios, classify and identify changes in driving scenarios, and combine a dynamic time series model to capture the changing patterns of driving scenarios over time, so as to achieve accurate modeling of the characteristics of vehicle dynamic driving scenarios.

[0015] In some specific embodiments, in step S2, the information of the people in the vehicle and the driver's status information are classified using the K-means clustering algorithm, typical driving habits are extracted, and the consistency of the driver's operating behavior in different driving scenarios is analyzed by the dynamic time warping algorithm. A driver model is constructed by combining the driver's long-term behavior data.

[0016] In some specific embodiments, in step S3, the driver's operating behavior in different driving scenarios is statistically analyzed, and the lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in the curve scenario are extracted in different driving scenarios. Multi-objective optimization algorithms are used to generate intelligent driving performance parameters and driver behavior evaluation parameters.

[0017] To achieve the same inventive objective, this application also provides a driver behavior assessment and prediction system based on driving scenarios, comprising:

[0018] Information extraction module: used to monitor and extract information on driving scenarios, in-vehicle occupants, and driver status;

[0019] Model building module: used to build a driving scenario and environment model based on the driving scenario, build a vehicle use scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters and driver behavior evaluation parameters based on the driver's state information in different driving modes.

[0020] Parameter prediction module: used to comprehensively evaluate the driver's driving behavior and driving style during manual driving based on the driving scenario and environment model, vehicle usage scenario model and driver model, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters;

[0021] Strategy optimization module: used to optimize and adjust the driving strategy during intelligent driving based on the driving performance parameters and driver behavior evaluation parameters.

[0022] In some specific embodiments, the information extraction module is used for:

[0023] The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features.

[0024] The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information.

[0025] The in-vehicle monitoring system can be used to monitor the number of passengers and their seating arrangement, or the external monitoring system can be used to monitor the type, weight, and volume of cargo carried in the vehicle.

[0026] In some specific embodiments, the model building module is used for:

[0027] By using convolutional neural networks to extract features, classify, and identify changes in driving scenarios from image data collected from different driving scenarios, and combining this with dynamic time series models to capture the changing patterns of driving scenarios over time, we can achieve accurate modeling of the dynamic driving scenario characteristics of vehicles.

[0028] In some specific embodiments, the model building module is used for:

[0029] The K-means clustering algorithm is used to classify the information of the occupants and the driver's status information, extract typical driving habits, and analyze the consistency of driver operation behavior in different driving scenarios through dynamic time warping algorithm. A driver model is constructed by combining long-term driver behavior data.

[0030] In some specific embodiments, the parameter prediction module is used for:

[0031] Statistical analysis of driver behavior in different driving scenarios is used to extract lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in curve scenarios. Multi-objective optimization algorithms are then used to generate intelligent driving performance parameters and driver behavior evaluation parameters.

[0032] The beneficial effects of the above technical solution are as follows:

[0033] This application provides a method and system for driver behavior assessment and prediction based on driving scenarios. By comprehensively assessing and predicting the driver's behavior through the driver's control behavior in different driving scenarios, different models are established. Thus, during the use of intelligent driving functions, based on vehicle passenger and cargo information and driver behavior analysis during safe driving, intelligent driving performance parameters applicable to different drivers and vehicle usage scenarios are given, thereby improving the intelligent driving experience. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating a method for driver behavior assessment and prediction based on a driving scenario, provided as an embodiment of the present invention;

[0036] Figure 2 A diagram illustrating a scenario-based driver behavior assessment and prediction method is provided as an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of the structure of a driver behavior assessment and prediction system based on a driving scenario, provided as an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0039] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0040] Example 1

[0041] One embodiment of the present invention provides a method for driver behavior assessment and prediction based on driving scenarios, referring to... Figure 1 , Figure 2 As shown, it includes:

[0042] Step S1: Monitor and extract information on driving scenarios, in-vehicle occupants, and driver status.

[0043] In a specific embodiment of the present invention, step S1 includes:

[0044] The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features.

[0045] The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information.

[0046] Specifically, the driver's physiological and behavioral data, including pupil movement, eye closure time, head posture, and operational data, such as accelerator force, brake force, and steering wheel angle rate, are acquired through the driver monitoring system (DMS).

[0047] The in-vehicle monitoring system can be used to monitor the number of passengers and their seating arrangement, or the external monitoring system can be used to monitor the type, weight, and volume of cargo carried in the vehicle.

[0048] Specifically, external sensors include: cameras, millimeter-wave radar, and lidar; driving scenarios include lane lines, surrounding vehicle trajectories, traffic light status, weather conditions (such as rain, snow, and fog), and road surface features (such as curves and slopes).

[0049] Step S2: Establish a driving scenario and environment model based on the driving scenario, establish a vehicle usage scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters, and driver behavior evaluation parameters based on the driver's state information in different driving modes.

[0050] In a specific embodiment of the present invention, in step S2, a convolutional neural network is used to extract features, classify and identify changes in driving scenarios from image data of different driving scenarios, such as lane changing, overtaking, and following other vehicles. The dynamic time series model is combined to capture the changing patterns of driving scenarios over time, so as to achieve accurate modeling of the dynamic driving scenario characteristics of the vehicle.

[0051] In a specific embodiment of the present invention, in step S2, the information of the people in the vehicle and the driver's state information are classified using the K-means clustering algorithm, typical driving habits (such as aggressive driving and conservative driving) are extracted, and the consistency of the driver's operating behavior in different driving scenarios is analyzed by the dynamic time warping algorithm. A driver model is constructed by combining the driver's long-term behavior data.

[0052] Step S3: Based on the driving scenario and environment model, vehicle usage scenario model and driver model, comprehensively evaluate the driver's driving behavior and driving style during manual driving, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters.

[0053] In a specific embodiment of the present invention, in step S3, the driver's operation behavior in different driving scenarios is statistically analyzed, and the lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in the curve scenario are extracted in different driving scenarios. The intelligent driving performance parameters and driver behavior evaluation parameters are generated using a multi-objective optimization algorithm.

[0054] Specifically, driver behavior is comprehensively evaluated in manual driving mode. This involves extracting data from different scenarios and driver actions to comprehensively assess driver behavior, such as lane change time, overtaking habits, speed at the end of different curves, following distance, and reaction time in dangerous situations. First, by statistically analyzing driver behavior in different scenarios, quantitative parameters are extracted, including lane change time, speed difference with the vehicle in front, relative distance in lane change scenarios, and curvature control speed in curve scenarios. Combined with the driver's reaction time to dangerous scenarios (such as sudden lane cutting), accelerator and brake operation characteristics, a comprehensive assessment of driver style is made, and a quantitative model of driving behavior is established. Subsequently, time series prediction models (such as LSTM or GRU) are used to model and predict driver behavior during driving. Combining the outputs of the scenario model, driver behavior model, and in-vehicle scenario model, a multi-objective optimization algorithm generates personalized driving performance parameters, such as acceleration rate of change, braking force, and steering response speed.

[0055] In intelligent driving mode, the intelligent driving system strategy is dynamically adjusted based on personalized driving performance parameters. For example, in highway scenarios, the following distance is dynamically optimized by combining passenger information; during braking, the comfort of the braking process is improved by adjusting the rate of deceleration change; in severe weather or dangerous scenarios, the vehicle control response time and alarm thresholds are adjusted according to the driver's style to ensure safety and reliability.

[0056] Step S4: Optimize and adjust the driving strategy during the intelligent driving process based on the driving performance parameters and driver behavior evaluation parameters.

[0057] This application continuously improves the safety and interactive experience of intelligent driving by optimizing and enriching driving scenarios and refining evaluation indicators. This invention achieves a closed-loop process from data collection to behavior modeling, evaluation and prediction, and strategy optimization, ensuring more accurate driving behavior evaluation and significantly improving the safety and interactive experience of intelligent driving.

[0058] Example 2

[0059] One embodiment of the present invention provides a driver behavior assessment and prediction system based on driving scenarios, referring to... Figure 3 As shown, it includes:

[0060] Information extraction module 10: used to monitor and extract information on driving scenarios, in-vehicle occupants, and driver status;

[0061] Model building module 20: is used to build a driving scenario and environment model based on the driving scenario, build a vehicle use scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters and driver behavior evaluation parameters based on the driver's state information in different driving modes.

[0062] Parameter prediction module 30: is used to comprehensively evaluate the driver's driving behavior and driving style during manual driving based on the driving scenario and environment model, vehicle usage scenario model and driver model, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters.

[0063] Strategy optimization module 40: used to optimize and adjust the driving strategy during the intelligent driving process based on the driving performance parameters and driver behavior evaluation parameters.

[0064] In one specific embodiment of the present invention, the information extraction module 10 is used for:

[0065] The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features.

[0066] The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information.

[0067] The in-vehicle monitoring system can be used to monitor the number of passengers and their seating arrangement, or the external monitoring system can be used to monitor the type, weight, and volume of cargo carried in the vehicle.

[0068] Specifically, external sensors include: cameras, millimeter-wave radar, and lidar; driving scenarios include lane lines, surrounding vehicle trajectories, traffic light status, weather conditions (such as rain, snow, and fog), and road surface features (such as curves and slopes).

[0069] In one specific embodiment of the present invention, the model building module 20 is used for:

[0070] By using convolutional neural networks to extract features, classify, and identify changes in driving scenarios from image data collected from different driving scenarios, and combining this with dynamic time series models to capture the changing patterns of driving scenarios over time, we can achieve accurate modeling of the dynamic driving scenario characteristics of vehicles.

[0071] In one specific embodiment of the present invention, the model building module 20 is used for:

[0072] The K-means clustering algorithm is used to classify the information of the occupants and the driver's status information, extract typical driving habits, and analyze the consistency of driver operation behavior in different driving scenarios through dynamic time warping algorithm. A driver model is constructed by combining long-term driver behavior data.

[0073] In one specific embodiment of the present invention, the parameter prediction module 30 is used for:

[0074] Statistical analysis of driver behavior in different driving scenarios is used to extract lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in curve scenarios. Multi-objective optimization algorithms are then used to generate intelligent driving performance parameters and driver behavior evaluation parameters.

[0075] Specifically, driver behavior is comprehensively evaluated in manual driving mode. This involves extracting data from different scenarios and driver actions to comprehensively assess driver behavior, such as lane change time, overtaking habits, speed at the end of different curves, following distance, and reaction time in dangerous situations. First, by statistically analyzing driver behavior in different scenarios, quantitative parameters are extracted, including lane change time, speed difference with the vehicle in front, relative distance in lane change scenarios, and curvature control speed in curve scenarios. Combined with the driver's reaction time to dangerous scenarios (such as sudden lane cutting), accelerator and brake operation characteristics, a comprehensive assessment of driver style is made, and a quantitative model of driving behavior is established. Subsequently, time series prediction models (such as LSTM or GRU) are used to model and predict driver behavior during driving. Combining the outputs of the scenario model, driver behavior model, and in-vehicle scenario model, a multi-objective optimization algorithm generates personalized driving performance parameters, such as acceleration rate of change, braking force, and steering response speed.

[0076] In intelligent driving mode, the intelligent driving system strategy is dynamically adjusted based on personalized driving performance parameters. For example, in highway scenarios, the following distance is dynamically optimized by combining passenger information; during braking, the comfort of the braking process is improved by adjusting the rate of deceleration change; in severe weather or dangerous scenarios, the vehicle control response time and alarm thresholds are adjusted according to the driver's style to ensure safety and reliability.

[0077] This application provides a method and system for driver behavior assessment and prediction based on driving scenarios. By comprehensively assessing and predicting the driver's behavior through the driver's control behavior in different driving scenarios, different models are established. Thus, during the use of intelligent driving functions, based on vehicle passenger and cargo information and driver behavior analysis during safe driving, intelligent driving performance parameters applicable to different drivers and vehicle usage scenarios are given, thereby improving the intelligent driving experience.

[0078] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0080] The methods and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0081] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "a specific embodiment" or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for driver behavior assessment and prediction based on driving scenarios, characterized in that, include: Step S1: Monitor and extract information on the driving scenario, in-vehicle occupants, and driver status; Step S2: Establish a driving scenario and environment model based on the driving scenario, establish a vehicle usage scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters, and driver behavior evaluation parameters based on the driver's state information in different driving modes. Step S3: Based on the driving scenario and environment model, vehicle usage scenario model and driver model, comprehensively evaluate the driver's driving behavior and driving style during manual driving, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters. Step S4: Optimize and adjust the driving strategy during the intelligent driving process based on the driving performance parameters and driver behavior evaluation parameters; Step S1 includes: The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features. The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information. Use an in-vehicle monitoring system to monitor the number of passengers and their seating arrangement, or use an external monitoring system to monitor the type, weight, and volume of cargo carried in the vehicle. In step S2, a convolutional neural network is used to extract features, classify and identify changes in driving scenarios from the collected image data of different driving scenarios, and a dynamic time series model is combined to capture the changing patterns of driving scenarios over time, so as to achieve accurate modeling of the characteristics of vehicle dynamic driving scenarios. Step S3 includes: statistically analyzing the driver's operational behavior in different driving scenarios, extracting lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in curve scenarios under different driving scenarios, and using multi-objective optimization algorithms to generate intelligent driving performance parameters and driver behavior evaluation parameters. In step S2, the K-means clustering algorithm is used to classify the information of the people in the vehicle and the driver's status information, extract typical driving habits, and analyze the consistency of the driver's operation behavior in different driving scenarios through the dynamic time warping algorithm. A driver model is constructed by combining the driver's long-term behavior data.

2. A driver behavior assessment and prediction system based on driving scenarios, characterized in that, include: Information extraction module: used to monitor and extract information on driving scenarios, in-vehicle occupants, and driver status; Model building module: used to build a driving scenario and environment model based on the driving scenario, build a vehicle use scenario model based on the information of the people in the vehicle, and generate a driver model, intelligent driving performance parameters and driver behavior evaluation parameters based on the driver's state information in different driving modes. Parameter prediction module: used to comprehensively evaluate the driver's driving behavior and driving style during manual driving based on the driving scenario and environment model, vehicle usage scenario model and driver model, so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters; Strategy optimization module: used to optimize and adjust the driving strategy during the intelligent driving process based on the driving performance parameters and driver behavior evaluation parameters; The information extraction module is used for: The system utilizes external sensors to collect data on driving scenarios, including lane markings, surrounding vehicle trajectories, traffic light status, weather conditions, and road surface features. The DMS is used to monitor driver status information, including driver pupil movement, eye closure time, head posture, and operation information. Use an in-vehicle monitoring system to monitor the number of passengers and their seating arrangement, or use an external monitoring system to monitor the type, weight, and volume of cargo carried in the vehicle. The model building module is used for: By using convolutional neural networks to extract features, classify and identify changes in driving scenarios from image data collected from different driving scenarios, and combining this with a dynamic time series model to capture the changing patterns of driving scenarios over time, we can achieve accurate modeling of the dynamic driving scenario characteristics of vehicles. The parameter prediction module is used for: Statistical analysis of driver behavior in different driving scenarios is used to extract lane change time, speed difference with the vehicle in front, relative distance, and curvature control speed in curve scenarios. Multi-objective optimization algorithms are used to generate intelligent driving performance parameters and driver behavior evaluation parameters. The model building module is used for: The K-means clustering algorithm is used to classify the information of the occupants and the driver's status information, extract typical driving habits, and analyze the consistency of driver operation behavior in different driving scenarios through dynamic time warping algorithm. A driver model is constructed by combining the driver's long-term behavior data.

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