Vehicle driving habit forming method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle driving habit forming method and device, electronic equipment and a storage medium, and relates to the field of vehicle driving, and the method comprises the steps: identifying the relevance between the process data of driving control and the vehicle state data according to the vehicle state data and the corresponding process data of driving control; analyzing the vehicle state data and the corresponding driving control process data, and marking to-be-corrected driving control process data; according to the marked to-be-corrected driving control process data, a correction intervention strategy is set; according to an intervention correction strategy, prompt or / and intervention information is output on the man-machine terminal and the driving control feedback; analyzing the vehicle state data and the process data of the corresponding driving control according to the execution of the intervention correction strategy, and evaluating the correction state of the driving control; a large driving control correction model is constructed, and data information of a traffic specification class, a vehicle operation specification class and a biological feature adaptation class is integrated into the large model.

Description

Technical Field

[0001] The present application relates to the field of vehicle driving, and in particular to a method for developing vehicle driving habits, a device for developing vehicle driving habits, an electronic device, a storage medium, and a vehicle. Background Art

[0002] With the increasing number of vehicles and the emergence of various new models, road conditions are becoming increasingly complex. Poor driving habits pose a safety hazard and impact traffic safety. Currently, AI-assisted vehicle systems are primarily used for autonomous driving, while research on manual control intervention is limited. Even some research is limited to using AI to analyze driving habits and provide drivers with prompts to correct them. Without a method to develop these habits, drivers often find it difficult to correct them.

[0003] Specifically, existing technologies have the following problems:

[0004] 1. It only analyzes the driver's driving habits and current status, but lacks prompts and corrections to driving habits;

[0005] 2. It mainly provides reminders and interventions for fatigue driving, but lacks a comprehensive driving habit analysis and development model;

[0006] 3. The focus is mainly on large vehicles such as buses and logistics transport vehicles, and there is a lack of driving habit model training for small and medium-sized passenger cars;

[0007] 4. In terms of artificial intelligence (AI) applications, there is no technical application that uses large models to train and infer driving habit models, nor is there any integration of traffic regulations and road civilized driving constraints into AI models.

[0008] In summary, current automobiles need to track, analyze, and develop driving habits for drivers of small and medium-sized passenger cars, and use AI algorithms and auxiliary means to continuously improve drivers' driving habits and enhance driving safety.

[0009] Therefore, a solution for developing vehicle driving habits is needed. Information on vehicle driving habits can be obtained through driving control and vehicle status data. Vehicle driving habits can be evaluated based on standard modules. An intervention and correction strategy can be set to evaluate vehicle driving habits. The results of the intervention and correction can be re-evaluated to iterate the large model. Summary of the Invention

[0010] The purpose of the present invention is to provide a vehicle driving habit development method, a vehicle driving habit development device, an electronic device, a storage medium and a vehicle, which at least solve the problem of obtaining vehicle driving habit information, the problem of evaluating vehicle driving habits, the problem of intervention and correction, and one of the technical problems in the problem of iterating large models.

[0011] The present invention provides the following solutions:

[0012] According to one aspect of the present invention, a method for developing vehicle driving habits is provided, the method comprising:

[0013] Obtain vehicle status data and corresponding driving control process data;

[0014] Identifying, based on the vehicle status data and the corresponding driving control process data, a correlation between the driving control process data and the vehicle status data;

[0015] Analyze vehicle status data and corresponding driving control process data, and mark driving control process data to be corrected;

[0016] Setting intervention and correction strategies based on driving control process data marked for correction;

[0017] Output prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention and correction strategy;

[0018] Analyze vehicle status data and corresponding driving control process data based on the execution of the intervention correction strategy, and evaluate the correction status of the driving control;

[0019] Build a large model of driving control correction, and integrate data information of traffic regulations, vehicle operation regulations and biometric adaptation into the large model.

[0020] According to two aspects of the present invention, a vehicle driving habit cultivation system is provided, comprising: a driving input and vehicle state sampling and identification module, a driving habit analysis and evaluation module, a bad driving operation prompt intervention module, a driving habit evaluation and improvement module, and a driving habit training and reasoning model module;

[0021] Driving input and vehicle status sampling and identification module, used for data collection of control behavior and vehicle status;

[0022] Driving habit analysis and evaluation module, which is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies;

[0023] An improper driving operation prompt intervention module is used to output prompts and / or intervention information according to the intervention correction strategy;

[0024] A driving habit assessment and improvement module is used to modify intervention and correction strategies based on output prompts and / or intervention information;

[0025] The driving habit training and reasoning model module is used to build and train a large model for correcting driving control behavior based on historical data collected from control behavior and vehicle status, and output prompts and / or intervention information.

[0026] Furthermore, the driving input and vehicle status sampling and identification module is used to collect data on control behavior and vehicle status, including:

[0027] The driving input and vehicle status sampling and identification module includes: an external vehicle environment sensing module, an internal vehicle environment sensing module, and a vehicle controlled sensing module;

[0028] The vehicle exterior environment sensing module includes an exterior vision module and a parking space radar module;

[0029] The in-vehicle environment sensing module includes an in-vehicle vision module;

[0030] The vehicle controlled sensing module includes a steering wheel sensing module, a throttle sensing module, and a brake sensing module;

[0031] It also includes a center console module, a driving habit sampling and recording module, and a driving habit data recognition module:

[0032] The center console module is used to record the events of control behavior based on the data of the vehicle's controlled sensing module;

[0033] The driving habit sampling and recording module is used to record the events of control behavior and the synchronized data of the external and internal vehicle environment states as the basic data for driving habit analysis and evaluation;

[0034] The driving habit data identification module is used to identify the basic data for driving habit analysis and evaluation, and to filter the habit type data in the data.

[0035] Furthermore, the driving habit analysis and evaluation module is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies, including:

[0036] The driving input and vehicle status sampling and identification module includes: driving habit AI model interface module, driving habit data analysis module, driving habit comprehensive evaluation module, and driving operation suggestion feedback module;

[0037] Driving habit AI model interface module, used to obtain standard control behavior model;

[0038] A driving habit data analysis module is used to evaluate control behavior events according to a standard control behavior model and analyze the behavior deviation of driving habits in the standard control behavior model;

[0039] A driving habit comprehensive assessment module is used to analyze the behavioral deviations of driving habits in the standard control behavior model and evaluate the risks arising from the behavioral deviations;

[0040] Risks include traffic safety risks, equipment loss risks, and vital signs risks;

[0041] The driving operation suggestion feedback module is used to analyze the behavioral deviations of driving habits in the standard control behavior model, evaluate the risks caused by the behavioral deviations, and generate intervention and correction strategies;

[0042] The intervention and correction strategies include marking behavioral deviation habits and setting them on the human-machine terminal and driving control feedback to output prompts and / or intervention information.

[0043] Furthermore, the bad driving operation prompt intervention module is configured to output prompts and / or intervention information according to the intervention correction strategy, including:

[0044] The bad driving operation prompt intervention module includes: a bad driving operation prompt module, a bad driving operation intervention module and a driving operation prompt intervention record storage module;

[0045] The bad driving operation prompt module is used to control the display or warning terminal and output prompt information according to the intervention and correction strategy;

[0046] The bad driving operation intervention module is used to control the control end and / or vehicle function response and output intervention information according to the intervention correction strategy;

[0047] Output intervention information includes the torque and / or control range of the control end;

[0048] The output intervention information also includes the interval, delay and / or direction of the response feedback of the control vehicle function;

[0049] Driving operation prompt intervention record storage module, used to record the process data of executing intervention correction strategy;

[0050] Inputting the process data of executing the intervention correction strategy into the driving habit training and reasoning model module for iterating the preset driving control correction macro model;

[0051] The process data for executing the intervention and correction strategy includes collecting historical data of control behavior and vehicle status, and outputting prompts and / or intervention information.

[0052] Furthermore, the driving habit training and reasoning model module is used to build and train a large model for correcting driving behavior based on historical data of collected driving behavior and vehicle status and output prompts and / or intervention information, including:

[0053] The driving habit training and reasoning model module includes: building an AI large model based on multiple neural networks; combining offline large model training with online real-time reasoning to build a driving habit training and reasoning model module;

[0054] The driving habit training and reasoning model module inputs historical data of control behavior and vehicle status and outputs prompts and / or intervention information;

[0055] The driving habit training and reasoning model module also inputs data on traffic regulations, vehicle operation regulations, and biometric adaptation;

[0056] The driving habit training and reasoning model module iteratively trains the model based on the process data of executing the intervention correction strategy.

[0057] According to three aspects of the present invention, a vehicle driving habit cultivation device is provided, comprising: a driving habit sampling and identification unit, a driving habit analysis and evaluation unit, a driving habit prompt intervention unit, a driving habit evaluation and improvement unit, and an AI driving habit large model training unit;

[0058] The driving habit sampling and identification unit includes obtaining vehicle status data and corresponding driving control process data;

[0059] Identifying, based on the vehicle status data and the corresponding driving control process data, a correlation between the driving control process data and the vehicle status data;

[0060] The driving habit analysis and evaluation unit includes analyzing vehicle status data and corresponding driving control process data, marking the driving control process data to be corrected;

[0061] Setting intervention and correction strategies based on driving control process data marked for correction;

[0062] The driving habit prompt intervention unit includes outputting prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention correction strategy;

[0063] The driving habit assessment and improvement unit includes analyzing vehicle status data and corresponding driving control process data based on the execution of intervention correction strategies, and evaluating the correction status of driving control;

[0064] The AI ​​driving habit big model training unit includes building a big model for driving control correction and integrating data information on traffic regulations, vehicle operation regulations, and biometric adaptation into the big model.

[0065] According to four aspects of the present invention, there is provided an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0066] A computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the vehicle driving habit formation method.

[0067] According to five aspects of the present invention, a computer-readable storage medium is provided, which stores a computer program that can be executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the vehicle driving habit formation method.

[0068] According to a sixth aspect of the present invention, there is provided a vehicle comprising:

[0069] An electronic device for implementing the steps of the method for developing vehicle driving habits;

[0070] a processor that runs a program, and when the program runs, executes the steps of the vehicle driving habit formation method based on data output by the electronic device;

[0071] The storage medium is used to store a program, and when the program is running, it executes the steps of the vehicle driving habit forming method for data output from the electronic device.

[0072] Through the above solution, the following beneficial technical effects are achieved:

[0073] This application identifies the correlation between the driving control process data and the vehicle status data based on the vehicle status data and the corresponding driving control process data, eliminates the limitation of only collecting driving behavior data, and obtains driving habit data with vehicle status origin.

[0074] This application expands habit correction from a single human-machine terminal to driving control feedback by outputting prompts and / or intervention information on the human-machine terminal and driving control feedback, reducing the excessive reliance on people's subjective initiative to respond to improvement information in control behavior correction, and completing the establishment of new habits faster and more accurately.

[0075] This application makes the reference standard universal, practical and feasible by integrating data on traffic regulations, vehicle operation regulations and biometric adaptation into the big model. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flowchart of a method for developing vehicle driving habits provided by one or more embodiments of the present invention.

[0077] Figure 2This is a structural diagram of a vehicle driving habit formation system provided by one or more embodiments of the present invention.

[0078] Figure 3 This is a structural diagram of a vehicle driving habit-forming device provided by one or more embodiments of the present invention.

[0079] Figure 4 2 is a schematic diagram of a driving habit analysis and evaluation according to a specific embodiment of the present invention.

[0080] Figure 5 2 is a schematic diagram of improving driving habit assessment according to a specific embodiment of the present invention.

[0081] Figure 6 It is a schematic diagram of AI-based driving habit training and reasoning according to a specific embodiment of the present invention.

[0082] Figure 7 This is a structural block diagram of an electronic device for a method for developing vehicle driving habits provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0083] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] Figure 1 This is a flowchart of a method for developing vehicle driving habits provided by one or more embodiments of the present invention.

[0085] like Figure 1 The vehicle driving habit formation method shown includes:

[0086] Step S1, obtaining vehicle status data and corresponding driving control process data;

[0087] Step S2, identifying the correlation between the driving control process data and the vehicle status data based on the vehicle status data and the corresponding driving control process data;

[0088] Step S3, analyzing the vehicle state data and the corresponding driving control process data, and marking the driving control process data to be corrected;

[0089] Step S4, setting an intervention correction strategy based on the driving control process data marked to be corrected;

[0090] Step S5: Outputting prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention correction strategy;

[0091] Step S6, analyzing the vehicle state data and the corresponding driving control process data based on the execution of the intervention correction strategy, and evaluating the correction state of the driving control;

[0092] Step S7: construct a large model for driving control correction, and integrate data information of traffic regulations, vehicle operation regulations, and biometric adaptation into the large model.

[0093] Specifically, in one embodiment, a driving habit development system based on artificial intelligence (AI) algorithms establishes control over the entire process from driving habit sampling and identification, analysis, evaluation, prompt intervention, to driving habit improvement and enhancement by integrating the steps of driving habit sampling and identification, driving habit analysis and evaluation, driving habit prompt intervention, and driving habit assessment and improvement.

[0094] In the driving habit sampling and identification step: sensors placed on the steering wheel, accelerator and brake pedals, as well as the vehicle's own speed and engine speed sensors, collect the driver's driving control input and vehicle status. Based on the AI ​​model, the driving habit sampling results are identified to confirm the characteristics and types of bad driving habits and extract the control inputs corresponding to bad driving habits;

[0095] In the driving habit analysis and evaluation step: Based on the AI ​​driving habit model, driving habits are analyzed and evaluated, driving habits that need to be corrected are identified, and necessary prompts and intervention methods are analyzed. The expected effects of prompts and interventions are evaluated, and correction strategies for prompts and interventions are provided. Strategies and implementation methods for developing driving habits at different driving stages are also provided.

[0096] In the step of driving habit prompt intervention: specific prompts and intervention operations are given according to the driving habit correction strategy, development strategy and implementation method. By actively prompting intervention operations, the driver's control input is corrected in real time when bad driving operations occur, driving habits are improved, and real-time development of driving habits is achieved.

[0097] In the step of driving habit assessment and improvement: after a period of driving habit formation, the driving habit assessment results and opinions are given according to the vehicle status and driving control input during the driving process, and based on this, the personalized driving habit formation model is improved in a targeted manner to promote further improvement of driving habits.

[0098] In the steps of training the AI ​​driving habit big model: use multiple neural networks to build a big model for driving habit identification, analysis and evaluation, and prompt intervention; use the big model method to train the driving habit formation model; integrate the driving principles required by traffic laws and traffic civilization into the big model training, and realize real-time online reasoning to drive prompt intervention in driving habits and complete the evaluation and improvement of driving habits.

[0099] In the above embodiment, the driving habit development system based on artificial intelligence (AI) algorithm has the ability to develop driving habits for different vehicle models and drivers. It can directly implement driving habit prompt intervention based on driving habit analysis and evaluation, and complete the entire process of driving habits from sampling, identification, analysis, evaluation, prompting, intervention to evaluation and improvement, integrating traffic laws and traffic civilization standards to achieve driving habit development.

[0100] Figure 2 This is a structural diagram of a vehicle driving habit formation system provided by one or more embodiments of the present invention.

[0101] like Figure 2 The vehicle driving habit development system shown includes: a driving input and vehicle state sampling and identification module, a driving habit analysis and evaluation module, a bad driving operation prompt intervention module, a driving habit evaluation and improvement module, and a driving habit training and reasoning model module;

[0102] Driving input and vehicle status sampling and identification module, used for data collection of control behavior and vehicle status;

[0103] Driving habit analysis and evaluation module, which is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies;

[0104] An improper driving operation prompt intervention module is used to output prompts and / or intervention information according to the intervention correction strategy;

[0105] A driving habit assessment and improvement module is used to modify intervention and correction strategies based on output prompts and / or intervention information;

[0106] The driving habit training and reasoning model module is used to build and train a large model for correcting driving control behavior based on historical data collected from control behavior and vehicle status, and output prompts and / or intervention information.

[0107] In this embodiment, the driving input and vehicle status sampling and identification module is used to collect data on control behavior and vehicle status, including:

[0108] The driving input and vehicle status sampling and identification module includes: an external vehicle environment sensing module, an internal vehicle environment sensing module, and a vehicle controlled sensing module;

[0109] The external environment sensing module includes an external vision module and a parking radar module;

[0110] The in-vehicle environment sensing module includes an in-vehicle vision module;

[0111] The vehicle controlled sensing module includes a steering wheel sensing module, an accelerator sensing module, and a brake sensing module;

[0112] It also includes a center console module, a driving habit sampling and recording module, and a driving habit data recognition module:

[0113] The center console module is used to record the events of control behavior based on the data of the vehicle's controlled sensing module;

[0114] The driving habit sampling and recording module is used to record the events of control behavior and the synchronized data of the external and internal vehicle environment states as the basic data for driving habit analysis and evaluation;

[0115] The driving habit data identification module is used to identify the basic data for driving habit analysis and evaluation, and to filter the habit type data in the data.

[0116] In this embodiment, the driving habit analysis and evaluation module is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies including:

[0117] The driving input and vehicle status sampling and identification module includes: driving habit AI model interface module, driving habit data analysis module, driving habit comprehensive evaluation module, and driving operation suggestion feedback module;

[0118] Driving habit AI model interface module, used to obtain standard control behavior model;

[0119] A driving habit data analysis module is used to evaluate control behavior events according to a standard control behavior model and analyze the behavior deviation of driving habits in the standard control behavior model;

[0120] A driving habit comprehensive assessment module is used to analyze the behavioral deviations of driving habits in the standard control behavior model and evaluate the risks arising from the behavioral deviations;

[0121] Risks include traffic safety risks, equipment loss risks, and vital signs risks;

[0122] The driving operation suggestion feedback module is used to analyze the behavioral deviations of driving habits in the standard control behavior model, evaluate the risks caused by the behavioral deviations, and generate intervention and correction strategies;

[0123] The intervention and correction strategies include marking behavioral deviation habits and setting them on the human-machine terminal and driving control feedback to output prompts and / or intervention information.

[0124] In this embodiment, the bad driving operation prompt intervention module is used to output prompts and / or intervention information according to the intervention correction strategy, including:

[0125] The bad driving operation prompt intervention module includes: a bad driving operation prompt module, a bad driving operation intervention module and a driving operation prompt intervention record storage module;

[0126] The bad driving operation prompt module is used to control the display or warning terminal and output prompt information according to the intervention and correction strategy;

[0127] The bad driving operation intervention module is used to control the control end and / or vehicle function response and output intervention information according to the intervention correction strategy;

[0128] Output intervention information includes the torque and / or control range of the control end;

[0129] The output intervention information also includes the interval, delay and / or direction of the response feedback of the control vehicle function;

[0130] Driving operation prompt intervention record storage module, used to record the process data of executing intervention correction strategy;

[0131] Input the process data of executing the intervention correction strategy into the driving habit training and reasoning model module to iterate the preset driving control correction model;

[0132] The process data for executing the intervention and correction strategy includes collecting historical data of control behavior and vehicle status, and outputting prompts and / or intervention information.

[0133] In this embodiment, the driving habit training and inference model module is used to build and train a large model for correcting driving behavior based on historical data collected from driving behavior and vehicle status, outputting prompts and / or intervention information, and includes:

[0134] The driving habit training and reasoning model module includes: building an AI large model based on multiple neural networks; combining offline large model training with online real-time reasoning to build a driving habit training and reasoning model module;

[0135] The driving habit training and reasoning model module inputs historical data of control behavior and vehicle status and outputs prompts and / or intervention information;

[0136] The driving habit training and reasoning model module also inputs data on traffic regulations, vehicle operation regulations, and biometric adaptation;

[0137] The driving habit training and reasoning model module iteratively trains the model based on the process data of executing the intervention correction strategy.

[0138] Specifically, in one embodiment, a driving habit development system based on artificial intelligence (AI) algorithms primarily includes: a driving input and vehicle status sampling and identification module, a driving habit analysis and evaluation module, a bad driving operation prompt intervention module, a driving habit assessment and improvement module, and an AI-based driving habit training and inference model. The functions of each component and their implementation are described below:

[0139] Driving input and vehicle status sampling and identification module:

[0140] 1) Surrounding environment cameras: These are installed on the front, rear, and sides of the vehicle. They capture images of blind spots around the vehicle and transmit them to a data processing unit for analysis. These cameras monitor the road conditions, vehicle conditions, and obstacles around the vehicle in real time.

[0141] 2) Radar sensor: By transmitting wireless signals and receiving echoes, it detects the position and distance of obstacles or other vehicles around the vehicle, providing accurate information on road conditions and the surrounding conditions of the vehicle;

[0142] 3) Steering wheel control sensor: including steering position sensor and steering torque sensor, which obtain the driver's steering input and obtain the input conditions of the control process through steering position, torque and control time;

[0143] 4) Accelerator pedal sensor: This sensor measures the displacement and speed of the accelerator pedal, and is used to determine the timing, force, angle, and rate of change of the accelerator pedal.

[0144] 5) Brake pedal sensor: This sensor measures the displacement and speed of the brake pedal, used to determine the timing and force of the brake operation, the angle of the brake pedal, and its rate of change.

[0145] 6) Operation records of the center console and operating panel: Records the operation inputs during driving, used to determine non-driving operation events other than steering wheel, accelerator, and brake;

[0146] 7) Cabin camera (independently selectable): records the driver's various actions to determine whether there are any bad habits that affect driving safety, and serves as the in-cabin image input condition for driving input sampling;

[0147] 8) Driving Habit Sampling and Recording Unit: The vehicle's surrounding conditions and driving control inputs acquired by the above sensors are stored in the sampling and recording unit. The stored data serves as the basis for AI model training and perception, and also serves as the basic data for driving habit analysis and evaluation;

[0148] 9) Driving habit data recognition unit: Based on the characteristics of AI model training, it identifies sampled and recorded data, provides front-end recognition for AI model training and driving habit analysis and evaluation, and completes classification and type recognition.

[0149] Driving habit analysis and evaluation module:

[0150] like Figure 4 The schematic diagram of driving habit analysis and evaluation is shown in FIG.

[0151] 1) Driving Habit AI Model Interface: The standards generated by the driving habit AI model serve as the benchmark for developing driving habits. The evaluation criteria of the analysis and evaluation module are obtained from the AI ​​model. The interface unit extracts data benchmarks from the AI ​​model during the analysis and evaluation process.

[0152] 2) Driving Habit Data Analysis Unit: Based on the data stored in the sampling and recording unit, it analyzes driving habits, including indicators such as control timing, control force, and control accuracy. It also analyzes the behavior of the vehicle while driving on the road, analyzes the impact of the control process on surrounding traffic participants, and provides an analysis of whether there are violations of traffic laws and traffic etiquette standards;

[0153] 3) Comprehensive Driving Habits Assessment Unit: This unit evaluates the driver's driving habits, first identifying driving behaviors and maneuvers that violate traffic laws and traffic etiquette standards; secondly, assessing unhealthy driving habits that impact traffic safety; and finally, evaluating driving habit improvements that could improve traffic etiquette and traffic efficiency.

[0154] 4) Driving operation suggestion feedback unit: Based on the comprehensive evaluation results of driving habits, it gives a driving habit feedback rating, extracts a suggestion feedback model for bad driving habits based on the AI ​​model, and gives a suggestion and feedback list, indicating the prompt content that needs to be given and the control intervention operation method that needs to be implemented.

[0155] Bad driving operation prompt intervention module:

[0156] 1) Improper driving operation prompt unit: Based on the suggestions and feedback list of the driving operation suggestion and feedback unit, the unit provides improper driving operation prompts in a pre-designed format for each prompt content item, including voice prompts, light prompts, and display screen content prompts. This is applicable to improper driving operations that generally and slightly violate driving habits guidelines;

[0157] 2) Improper Driving Operation Intervention Unit: Based on the suggestions and feedback list from the Driving Operation Suggestion and Feedback Unit, the unit provides interventions for improper driving operations in a pre-designed manner. This includes active steering force feedback, accelerator and brake pedal force feedback, and other interventions. This unit is suitable for serious violations of driving habit guidelines. Through direct intervention, it eliminates improper driving habits that affect safe driving.

[0158] 3) Driving operation prompt intervention record storage unit: The driving operation prompt intervention record storage is a source of basic data for AI driving habit training, and together with vehicle status and driver control input, it is part of the basic training data of the driving habit model.

[0159] Driving habit assessment and improvement module:

[0160] like Figure 5 The schematic diagram of driving habit evaluation improvement is shown in the figure.

[0161] 1) Driving Habit Assessment Unit: Based on the good driving habit guidelines provided by the AI ​​model, a driving habit assessment mechanism is established. This assessment mechanism is fed into the driving habit assessment unit. Based on the sampled recognition data of driving operations and the driving operation prompt intervention records, a comprehensive assessment of the driver's driving habits is provided, which also serves as a data guide for the development of driving habits.

[0162] 2) Driving Habit Improvement Unit: After AI model training and reasoning, it provides a driving habit improvement guidance mode that suits the driver's individual operation. It provides enhanced memory guidance intervention for individual driving habits to promote the improvement of driving habits.

[0163] 3) Active Driving Habit Development Unit: Based on the driving habit assessment and improvement unit, a driving habit development model is constructed. Development strategies are provided based on the driver's personalized driving habits. Prompts and interventions for the development of driving habits are given during normal driving, and the development strategies are continuously improved based on the improvement of driving habits.

[0164] AI-based driving habit training and reasoning model:

[0165] like Figure 6 The diagram shown is a schematic diagram of AI-based driving habit training and reasoning.

[0166] A large AI model is constructed using a multi-neural network approach. The hardware utilizes a high-performance GPU integrated into the vehicle's electronic system. Offline large-scale model training is combined with online real-time inference to create an AI-based driving habit training and inference model. The model uses basic driving guidelines, traffic regulations, and traffic etiquette guidelines as foundational data, and uses onboard vehicle status parameters, driver input, and AI prompt intervention records as incremental data. This enables a combination of offline and online big data mining. Offline reinforcement training is used to improve model performance, adapting it to the needs of driving habit training and inference models.

[0167] The driving habit cultivation system based on artificial intelligence (AI) algorithms of this embodiment can produce the following beneficial effects:

[0168] Improve driving habits and enhance driving safety: Directly eliminate driving safety hazards through prompts and interventions. Through multiple rounds of prompt interventions, improve the driver's safe driving level, improve the driver's safe driving habits, and improve road driving safety.

[0169] Effectively promote good driving habits required by traffic laws and traffic etiquette: Integrate traffic laws and traffic etiquette requirements into the AI ​​large-scale model to achieve intelligent, front-end, and real-time popularization of good driving habits. By cultivating driving habits among a large number of drivers, the orderly promotion of traffic laws and traffic etiquette standards is achieved;

[0170] Improve driving experience and enhance traffic awareness: Some bad driving habits are caused by drivers who are not aware of them. AI-assisted methods can provide real-time prompts and intervention to address bad driving behaviors, improve driving habits, enhance the driving experience, and enhance drivers' awareness of traffic etiquette.

[0171] Improve driving efficiency by promoting good driving habits: Good driving habits are a prerequisite for civilized and orderly traffic. The more good driving habits are promoted, the higher the road traffic efficiency will be. By cultivating drivers' driving habits, driving efficiency can be greatly improved, and in the long run, road traffic conditions will also be greatly improved.

[0172] Figure 3 This is a structural diagram of a vehicle driving habit-forming device provided by one or more embodiments of the present invention.

[0173] like Figure 3 The vehicle driving habit cultivation device shown includes: a driving habit sampling and identification module, a driving habit analysis and evaluation module, a driving habit prompt intervention module, a driving habit evaluation and improvement module, and an AI driving habit large model training module;

[0174] The driving habit sampling and identification module includes obtaining vehicle status data and corresponding driving control process data;

[0175] Identifying, based on the vehicle status data and the corresponding driving control process data, a correlation between the driving control process data and the vehicle status data;

[0176] The driving habit analysis and evaluation module includes analyzing vehicle status data and corresponding driving control process data, marking the driving control process data to be corrected;

[0177] Setting intervention and correction strategies based on driving control process data marked for correction;

[0178] The driving habit prompt intervention module includes outputting prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention correction strategy;

[0179] The driving habit assessment and improvement module includes analyzing vehicle status data and corresponding driving control process data based on the execution of intervention correction strategies, and evaluating the correction status of driving control;

[0180] The AI ​​driving habit big model training module includes building a big model for driving control correction and integrating data information on traffic regulations, vehicle operation regulations, and biometric adaptation into the big model.

[0181] It is worth noting that although this system only discloses a driving habit sampling and identification module, a driving habit analysis and evaluation module, a driving habit prompt intervention module, a driving habit assessment and improvement module, and an AI driving habit large model training module, it does not mean that this device is limited to the above-mentioned basic functional modules. On the contrary, what the present invention wants to express is that, on the basis of the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the existing technology to form an infinite number of embodiments or technical solutions. In other words, this system is open rather than closed. Just because this embodiment only discloses individual basic functional modules, it cannot be considered that the scope of protection of the claims of the present invention is limited to the above-mentioned basic functional modules.

[0182] Figure 7 This is a structural block diagram of an electronic device for a method for developing vehicle driving habits provided by one or more embodiments of the present invention.

[0183] like Figure 7 As shown, the present application provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0184] A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of a method for forming vehicle driving habits.

[0185] The present application also provides a computer-readable storage medium storing a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of a method for developing vehicle driving habits.

[0186] The present application also provides a vehicle, comprising:

[0187] An electronic device for implementing the steps of the vehicle driving habit formation method;

[0188] a processor that runs a program, and when the program runs, executes steps of the method for developing vehicle driving habits based on data output from the electronic device;

[0189] The storage medium is used to store a program, which, when running, executes the steps of the vehicle driving habit formation method for data output from the electronic device.

[0190] The communication bus mentioned in the electronic device mentioned above may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0191] The electronic device includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control electronic devices through processes, such as the Linux operating system, the Unix operating system, the Android operating system, the iOS operating system, or the Windows operating system. In the embodiments of the present invention, the electronic device can be a handheld device such as a smartphone or a tablet computer, or an electronic device such as a desktop computer or a portable computer, which is not particularly limited in the embodiments of the present invention.

[0192] The execution subject of the electronic device control in the embodiment of the present invention can be an electronic device, or a functional module in the electronic device that can call a program and execute the program. The electronic device can obtain the firmware corresponding to the storage medium. The firmware corresponding to the storage medium is provided by the supplier. The firmware corresponding to different storage media can be the same or different, and is not limited here. After the electronic device obtains the firmware corresponding to the storage medium, it can write the firmware corresponding to the storage medium into the storage medium, specifically, burn the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology and will not be described in detail in the embodiment of the present invention.

[0193] The electronic device can also obtain a reset command corresponding to the storage medium. The reset command corresponding to the storage medium is provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and are not limited here.

[0194] In this case, the storage medium of the electronic device is a storage medium in which the corresponding firmware is written. The electronic device can respond to the reset command corresponding to the storage medium in which the corresponding firmware is written, thereby resetting the storage medium in which the corresponding firmware is written according to the reset command corresponding to the storage medium. The process of resetting the storage medium according to the reset command can be implemented in the existing technology and will not be described in detail in the embodiments of the present invention.

[0195] For the convenience of description, the above devices are described as various units and modules according to their functions. Of course, when implementing this application, the functions of each unit and module can be implemented in the same or multiple software and / or hardware.

[0196] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with those in the context of the prior art and, unless specifically defined, will not be interpreted in an idealized or overly formal sense.

[0197] For simplicity of description, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because certain steps can be performed in other orders or simultaneously according to the embodiments of the present invention. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0198] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application or certain parts of the embodiments.

[0199] Finally, 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for developing vehicle driving habits, characterized in that: The vehicle driving habit forming method comprises: Obtain vehicle status data and corresponding driving control process data; Identifying, based on the vehicle status data and the corresponding driving control process data, a correlation between the driving control process data and the vehicle status data; Analyze vehicle status data and corresponding driving control process data, and mark driving control process data to be corrected; Setting intervention and correction strategies based on driving control process data marked for correction; Output prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention and correction strategy; Analyze vehicle status data and corresponding driving control process data based on the execution of the intervention correction strategy, and evaluate the correction status of the driving control; Build a large model of driving control correction, and integrate data information of traffic regulations, vehicle operation regulations and biometric adaptation into the large model.

2. A vehicle driving habit formation system, characterized in that: The vehicle driving habit cultivation system includes: a driving input and vehicle state sampling and identification module, a driving habit analysis and evaluation module, a bad driving operation prompt intervention module, a driving habit evaluation and improvement module, and a driving habit training and reasoning model module; Driving input and vehicle status sampling and identification module, used for data collection of control behavior and vehicle status; Driving habit analysis and evaluation module, which is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies; An improper driving operation prompt intervention module is used to output prompts and / or intervention information according to the intervention correction strategy; A driving habit assessment and improvement module is used to modify intervention and correction strategies based on output prompts and / or intervention information; The driving habit training and reasoning model module is used to build and train a large model for correcting driving control behavior based on historical data collected from control behavior and vehicle status, and output prompts and / or intervention information.

3. The vehicle driving habit forming system according to claim 2, characterized in that: The driving input and vehicle status sampling and identification module is used to collect data on control behavior and vehicle status and includes: The driving input and vehicle status sampling and identification module includes: an external vehicle environment sensing module, an internal vehicle environment sensing module, and a vehicle controlled sensing module; The vehicle exterior environment sensing module includes an exterior vision module and a parking space radar module; The in-vehicle environment sensing module includes an in-vehicle vision module; The vehicle controlled sensing module includes a steering wheel sensing module, a throttle sensing module, and a brake sensing module; It also includes a center console module, a driving habit sampling and recording module, and a driving habit data recognition module: The center console module is used to record the events of control behavior based on the data of the vehicle's controlled sensing module; The driving habit sampling and recording module is used to record the events of control behavior and the synchronized data of the external and internal vehicle environment states as the basic data for driving habit analysis and evaluation; The driving habit data identification module is used to identify the basic data for driving habit analysis and evaluation, and to filter the habit type data in the data.

4. The vehicle driving habit cultivation system according to claim 3, characterized in that: The driving habit analysis and evaluation module is used to obtain a standard driving behavior model, evaluate driving behavior events based on the standard driving behavior model, and generate intervention and correction strategies, including: The driving input and vehicle status sampling and identification module includes: driving habit AI model interface module, driving habit data analysis module, driving habit comprehensive evaluation module, and driving operation suggestion feedback module; Driving habit AI model interface module, used to obtain standard control behavior model; A driving habit data analysis module is used to evaluate control behavior events according to a standard control behavior model and analyze the behavior deviation of driving habits in the standard control behavior model; A driving habit comprehensive assessment module is used to analyze the behavioral deviations of driving habits in the standard control behavior model and evaluate the risks arising from the behavioral deviations; Risks include traffic safety risks, equipment loss risks, and vital signs risks; The driving operation suggestion feedback module is used to analyze the behavioral deviations of driving habits in the standard control behavior model, evaluate the risks caused by the behavioral deviations, and generate intervention and correction strategies; The intervention and correction strategies include marking behavioral deviation habits and setting them on the human-machine terminal and driving control feedback to output prompts and / or intervention information.

5. The vehicle driving habit forming system according to claim 4, characterized in that: The bad driving operation prompt intervention module is used to output prompts and / or intervention information according to the intervention correction strategy, including: The bad driving operation prompt intervention module includes: a bad driving operation prompt module, a bad driving operation intervention module and a driving operation prompt intervention record storage module; The bad driving operation prompt module is used to control the display or warning terminal and output prompt information according to the intervention and correction strategy; The bad driving operation intervention module is used to control the control end and / or vehicle function response and output intervention information according to the intervention correction strategy; Output intervention information includes the torque and / or control range of the control end; The output intervention information also includes the interval, delay and / or direction of the response feedback of the control vehicle function; Driving operation prompt intervention record storage module, used to record the process data of executing intervention correction strategy; Inputting the process data of executing the intervention correction strategy into the driving habit training and reasoning model module for iterating the preset driving control correction macro model; The process data for executing the intervention and correction strategy includes collecting historical data of control behavior and vehicle status, and outputting prompts and / or intervention information.

6. The vehicle driving habit forming system according to claim 5, characterized in that: The driving habit training and reasoning model module is used to build and train a large model for correcting driving behavior based on historical data collected from driving behavior and vehicle status and outputting prompts and / or intervention information, including: The driving habit training and reasoning model module includes: building an AI large model based on multiple neural networks; combining offline large model training with online real-time reasoning to build a driving habit training and reasoning model module; The driving habit training and reasoning model module inputs historical data of control behavior and vehicle status and outputs prompts and / or intervention information; The driving habit training and reasoning model module also inputs data on traffic regulations, vehicle operation regulations, and biometric adaptation; The driving habit training and reasoning model module iteratively trains the model based on the process data of executing the intervention correction strategy.

7. A vehicle driving habit forming device, characterized in that: The vehicle driving habit formation device includes: a driving habit sampling and identification unit, a driving habit analysis and evaluation unit, a driving habit prompt intervention unit, a driving habit evaluation and improvement unit, and an AI driving habit large model training unit; The driving habit sampling and identification unit includes obtaining vehicle status data and corresponding driving control process data; Identifying, based on the vehicle status data and the corresponding driving control process data, a correlation between the driving control process data and the vehicle status data; The driving habit analysis and evaluation unit includes analyzing vehicle status data and corresponding driving control process data, marking the driving control process data to be corrected; Setting intervention and correction strategies based on driving control process data marked for correction; The driving habit prompt intervention unit includes outputting prompts and / or intervention information on the human-machine terminal and driving control feedback according to the intervention correction strategy; The driving habit assessment and improvement unit includes analyzing vehicle status data and corresponding driving control process data based on the execution of intervention correction strategies, and evaluating the correction status of driving control; The AI ​​driving habit big model training unit includes building a big model for driving control correction and integrating data information on traffic regulations, vehicle operation regulations, and biometric adaptation into the big model.

8. An electronic device, characterized in that: include: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; The memory stores a computer program, which, when executed by the processor, causes the processor to execute the steps of the vehicle driving habit forming method as claimed in claim 1.

9. A computer-readable storage medium, characterized in that A computer program executable by an electronic device is stored. When the computer program is run on the electronic device, the electronic device executes the steps of the vehicle driving habit formation method as claimed in claim 1.

10. A vehicle, characterized in that: include: An electronic device for implementing the steps of the vehicle driving habit formation method according to claim 1; a processor, the processor running a program, and executing the steps of the vehicle driving habit forming method according to claim 1 based on data output from the electronic device when the program is running; A storage medium is used to store a program, which, when running, executes the steps of the vehicle driving habit formation method as claimed in claim 1 for data output from the electronic device.

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