Complex scenario based driving behavior regulation method, system, medium, and device

Abstract

The application discloses a driving behavior specification method and system based on complex scenes, a medium and equipment, and relates to the technical field of vehicle safety management and control. The method comprises the following steps: acquiring driving data and environmental data of multiple drivers in real time; identifying and analyzing the driving data by using a behavior model to obtain driving behavior evaluation results; generating corresponding intervention decisions according to the driving behavior evaluation results, wherein, the non-standard driving behaviors are classified into self-factor influencing behaviors and environmental factor influencing behaviors, the self-factor influencing behaviors are analyzed to generate internal cause intervention decisions, the environmental data when the environmental factor influencing behaviors occur are extracted, and the environmental factor influencing behaviors are combined with the environmental data to generate external cause intervention decisions; and the intervention decisions are used to correct and continuously test the driving behaviors of the drivers. The application can identify multiple driving behaviors of the drivers in real time in complex driving scenes, and effectively correct non-standard driving behaviors through targeted and continuous intervention.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle safety management, and particularly relates to a driving behavior regulation method, system, medium and equipment based on complex scenarios. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] With the increasing complexity of road traffic, the behavior of drivers is crucial to ensure driving safety. However, in actual driving, there are often bad driving habits such as fatigue driving, speeding, not wearing seat belts, and distracted driving, which significantly increase the risk of traffic accidents.

[0004] Traditional driving monitoring methods often rely on manual supervision or simple vehicle-mounted device alarms. These methods can to some extent remind drivers of potential risks, but they usually can only passively warn against specific single risk factors, lack the ability to understand the overall behavior pattern of the driver and actively intervene, and have limited recognition of complex driving scenarios (such as fatigue, distraction, etc. subjective state), making it difficult to achieve precise, efficient, and personalized driving behavior management. Therefore, there is a lack of a method and system in the prior art that can continuously feedback training and precise intervention for different non-standard behaviors of drivers in complex driving scenarios. SUMMARY

[0005] In view of the deficiencies in the prior art, the present application provides a driving behavior regulation method, system, medium and equipment based on complex scenarios, which can identify a variety of driving behaviors of drivers in real time in complex driving scenarios, and effectively correct non-standard driving behaviors through targeted and continuous intervention.

[0006] In order to achieve the above-mentioned purpose, the present application is realized by the following technical solutions:

[0007] The present application provides a driving behavior regulation method based on complex scenarios in the first aspect, comprising the following steps:

[0008] Real-time acquisition of driving data and environmental data of a plurality of drivers, and preprocessing of the driving data and environmental data;

[0009] Using a behavior model to identify and analyze the preprocessed driving data to obtain a driving behavior evaluation result;

[0010] Generate corresponding intervention decisions based on the driving behavior assessment results. Specifically, classify non-standard driving behaviors into behaviors affected by self-factors and behaviors affected by environmental factors. Perform correction analysis on behaviors affected by self-factors to generate internal intervention decisions. Extract environmental data from when environmental factors affect behaviors, and combine this environmental data to generate external intervention decisions on behaviors affected by environmental factors.

[0011] Use intervention decision-making to modify driver behavior and conduct ongoing testing.

[0012] Furthermore, driving data includes the driver's physiological state, sight direction and operating actions.

[0013] Furthermore, the specific steps for using the behavior model to identify and analyze the pre-processed driving data are as follows:

[0014] Use deep learning algorithms to build behavioral models;

[0015] The existing database data is combined with the driver's historical driving data to form a training set, and the behavior model is trained;

[0016] The trained behavior model is used to evaluate the real-time driving data to obtain the driving behavior evaluation results.

[0017] Furthermore, the driving behavior assessment results include standard driving behavior and non-standard driving behavior, and the risk level is judged according to the severity of the non-standard driving behavior.

[0018] Furthermore, environmental data includes weather condition data, road condition complexity data, traffic flow data and potential hazard source data.

[0019] Furthermore, the intervention decision is a staged intervention decision, the number of stages is determined according to the severity of the non-standard driving behavior, and the subsequent number of stages and decision content are adjusted in real time according to the driver's correction feedback.

[0020] Furthermore, continuous testing is used to test the driver's corrective behavior on the implementation effect of the intervention decision at each stage and generate driver correction feedback.

[0021] A second aspect of the present invention provides a driving behavior regulation system based on complex scenarios, comprising:

[0022] a data acquisition module configured to acquire driving data and environmental data of various drivers in real time and pre-process the driving data and environmental data;

[0023] a behavior recognition and evaluation module configured to use the behavior model to identify and analyze the pre-processed driving data to obtain a driving behavior evaluation result;

[0024] A decision-making module is configured to generate corresponding intervention decisions based on the driving behavior assessment results, wherein non-standard driving behaviors are classified into behaviors affected by internal factors and behaviors affected by environmental factors, and correction analysis is performed on behaviors affected by internal factors to generate internal intervention decisions. Environmental data when environmental factors affect behaviors is extracted and combined with the environmental data to generate external intervention decisions for the behaviors affected by environmental factors;

[0025] The correction feedback module is configured to use intervention decisions to correct the driver's behavior and conduct continuous testing.

[0026] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps of the method for regulating driving behavior based on complex scenarios as described in the first aspect of the present invention.

[0027] The fourth aspect of the present invention provides a device comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for regulating driving behavior based on complex scenarios as described in the first aspect of the present invention are implemented.

[0028] One or more of the above technical solutions have the following beneficial effects:

[0029] The present invention designs a method, system, medium and equipment for regulating driving behavior based on complex scenarios, which can realize real-time identification of all-round non-standard driving behaviors through training with large amounts of data. Compared with existing driving behavior monitoring methods, the present invention can also analyze the factors affecting driving behavior in complex driving scenarios, generate more targeted intervention decisions, and greatly improve the effectiveness of correcting driving behavior norms. In order to consolidate its correction effect, the present invention also innovates a continuous phased intervention method. Through adaptive phased testing and correction, it ensures that the driver's behavior is not maintained for a short period of time, allowing the driver to form muscle habits, improve the driving safety factor, and provide a stronger guarantee for safe driving.

[0030] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0032] Figure 1 This is a flow chart of a method for regulating driving behavior based on complex scenarios in Example 1 of the present invention;

[0033] Figure 2 This is a structural diagram of the data collection process in Example 1 of the present invention;

[0034] Figure 3 This is a structural diagram of the behavior recognition and evaluation process in Example 1 of the present invention;

[0035] Figure 4 This is a structural diagram of the decision-making designation process in the first embodiment of the present invention;

[0036] Figure 5 This is a structural diagram of the interactive feedback and guidance process in Example 1 of the present invention;

[0037] Figure 6 This is a structural diagram of a driving behavior regulation system based on complex scenarios in Example 2 of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations;

[0040] Example 1:

[0041] The first embodiment of the present invention provides a driving behavior standardization method based on complex scenarios, such as Figure 1 As shown, the following steps are included:

[0042] Step 1: Acquire driving data and environmental data of multiple drivers in real time and pre-process the driving data and environmental data.

[0043] Driving data includes the driver's physiological state, line of sight, and operating actions. Environmental data includes weather conditions, road complexity, traffic flow, and potential hazard sources.

[0044] In this embodiment, weather conditions and potential hazard source data can be obtained through vehicle body sensors, etc., and road condition complexity data and traffic flow data can be obtained through navigation software.

[0045] During data collection, Figure 2As shown, the driver is the target of data collection. Multiple sensors installed in the vehicle, including but not limited to high-definition cameras, biometric sensors, vehicle motion sensors, and CAN bus interfaces, are used to comprehensively collect multi-dimensional data, including the driver's physiological state, gaze direction, and operational actions. This data is then preprocessed and transmitted to the data fusion and synchronization unit for synchronization and storage. Vehicle motion sensors include accelerometers, gyroscopes, and wheel speed sensors. The driver's physiological state, such as fatigue and concentration, is collected. Operational actions, such as steering, acceleration, and braking force, are collected. Vehicle dynamic information, such as speed, location, heading, and driving path, is collected. This data collection process ensures real-time and accurate information, providing a foundational source for subsequent behavior recognition and evaluation.

[0046] In a specific embodiment, the data collection process includes the following steps:

[0047] 1. Hardware Deployment: Various sensor devices are deployed within the vehicle, including but not limited to high-definition cameras, biometric sensors, vehicle motion sensors, and connections to the vehicle's CAN bus interface to obtain vehicle driving data. The high-definition camera is installed in front of the driver to capture facial expressions and gaze direction. Biometric sensors such as heart rate sensors and eye tracking sensors monitor the driver's physiological state. Vehicle motion sensors such as accelerometers, gyroscopes, and wheel speed sensors capture vehicle dynamic information.

[0048] 2. Data interface configuration: Ensure that the data interface of each sensor device and the vehicle AI assistant system is correctly connected and configured to ensure the stability and real-time performance of data transmission.

[0049] 3. Data preprocessing: Perform necessary preprocessing on the received raw sensor data, such as filtering and denoising, data format conversion, unit unification, etc., to facilitate subsequent module processing.

[0050] 4. Data synchronization and storage: The pre-processed data is transmitted to the processing unit of the AI ​​assistant in real time and stored in the local cache or on-board storage device as needed. At the same time, it is synchronized to the cloud server through the on-board communication module, such as 4G / 5G, Wi-Fi, etc., to support remote management and data analysis.

[0051] Step 2: Use the behavior model to identify and analyze the preprocessed driving data to obtain the driving behavior evaluation results.

[0052] like Figure 3As shown, the data collected in step 1 is input, and features are extracted for driver behavior, vehicle operation, and environmental factors. These features are then input into the constructed behavior model to identify and classify the behaviors. Finally, risk assessment and early warning are performed based on the results. Specifically, this includes:

[0053] Step 2.1: Use deep learning algorithms combined with computer vision, pattern recognition and other technologies to build a behavior model, and use machine learning models such as support vector machines and random forests for classification. In this embodiment, a convolutional neural network is used to build the behavior model.

[0054] Step 2.2: Use the existing database data and the driver's historical driving data to form a training set and train the behavior model.

[0055] Step 2.3: Use the trained behavior model to evaluate the real-time driving data to obtain a driving behavior evaluation result. The driving behavior evaluation result includes standard driving behavior and non-standard driving behavior, and the risk level is determined based on the severity of the non-standard driving behavior.

[0056] The evaluation of real-time driving data using the trained behavior model is divided into the following steps:

[0057] (1) Driver behavior recognition: Analyze the driver's facial expressions, eye movement characteristics, head posture and other information to identify whether the driver is in a state that affects driving safety, such as fatigue, distraction, or emotional fluctuations.

[0058] More specifically, facial expression recognition: Use deep learning models to analyze the driver's facial images captured by the camera in real time to identify facial expressions such as fatigue, distraction, anger, etc. that may affect driving safety.

[0059] In a specific embodiment, the real-time analysis of the driver's facial image captured by the camera using a deep learning model is based on the following aspects:

[0060] 1. Recognition based on eye status.

[0061] 1. Blink frequency:

[0062] Under normal circumstances, a driver's blinking frequency is within a certain range. When a driver becomes fatigued, their blinking frequency may change. Generally speaking, a normal blinking frequency is approximately 10-20 times per minute. A blinking frequency that is too high (e.g., over 30 times per minute, which may be caused by conditions such as dry eyes, but may also be an early sign of fatigue) or too low (e.g., less than 5 times per minute) may be a sign of fatigue. For example, an extremely tired driver may unconsciously close their eyes for extended periods of time. This is the brain's response to fatigue, attempting to rest by reducing visual input.

[0063] 2. Eye closure time:

[0064] This is a key indicator. During a normal blink, the eye closure period is short, typically between 0.1 and 0.4 seconds. When a driver is fatigued, the eye closure period may be prolonged. If the eye closure period exceeds 1.5 seconds, there is a strong suspicion of fatigue driving. Some studies, analyzing extensive experimental data, have found that after a driver has driven continuously for 3-4 hours, the number of times their eyes are closed for more than one second increases significantly.

[0065] 3. Eyelid drooping degree:

[0066] A fatigued driver's eyelids will droop noticeably. By analyzing images of the eye area, the distance or angle between the eyelid and the eyeball can be measured to determine the degree of eyelid droop. For example, using computer vision algorithms, instead of locating eye feature points, the vertical distance between the upper eyelid edge and the center of the eyeball is calculated. Under normal circumstances, this distance is relatively stable. However, when the driver is fatigued, this distance decreases, and the eyelid moves closer to the eyeball, partially obscuring it.

[0067] 4. Yawning:

[0068] Yawning is a clear sign of fatigue. When yawning, the driver's mouth opens wide, and facial muscles stretch in specific movements. Yawning can be detected by monitoring the degree of mouth opening and the movement patterns of facial muscles. Generally, a yawn can open the mouth to approximately 60-90 degrees, accompanied by noticeable stretching of muscles in the cheeks, jaw, and other areas. Yawning is often accompanied by a slight tilt or shake of the head. These combined motion characteristics can be detected using in-vehicle cameras and corresponding image recognition algorithms.

[0069] 2. Recognition based on head posture.

[0070] 1. Nodding action:

[0071] When a driver is fatigued, they may nod their head unconsciously. During normal driving, the head's position is relatively stable. However, when fatigued, the head may nod more frequently and with greater amplitude as the vehicle shakes. Head movement can be tracked using sensors or cameras installed in the vehicle. For example, this can be achieved using an inertial measurement unit (IMU) or vision-based head pose estimation methods. If the head nods up and down multiple times within a short period of time (e.g., 10-15 seconds) and the amplitude exceeds a certain angle (e.g., 10-15 degrees), it may indicate driver fatigue.

[0072] 2. Head tilt degree:

[0073] A fatigued driver may experience a sideways tilt of the head. This occurs because muscle fatigue makes it difficult for the head to maintain a normal upright position. By estimating the three-dimensional head posture, we can calculate the angle between the head's central axis and the vertical. Normally, this angle is small, but it may increase when the driver is fatigued. For example, in some experiments, when the driver's fatigue index reaches a certain level, the angle between the head's central axis and the vertical may exceed 15-20 degrees, resulting in a sideways tilt.

[0074] 3. Recognition based on facial muscle status.

[0075] 1. Degree of facial relaxation:

[0076] A fatigued driver's facial expression is typically relaxed. Under normal circumstances, facial muscles maintain a certain degree of tension, the eyes are bright, and the eyebrows and other facial features are positioned normally. When fatigued, facial muscle tension decreases, the eyebrows may droop, and the corners of the mouth may droop slightly. The degree of facial relaxation can be determined by analyzing the movement and morphology of key facial muscle groups. For example, the Facial Action Coding System (FACS) is used to analyze the action units of facial muscles. If multiple action units associated with a relaxed expression (such as drooping eyebrows and relaxed cheek muscles) are detected simultaneously, it can be inferred that the driver may be fatigued.

[0077] 2. Frowning frequency and degree:

[0078] When drivers are fatigued, they may frown frequently due to factors such as glare or a lack of concentration. Frowning may also be more severe than normal. The degree of frowning can be assessed by analyzing muscle contraction in the glabella area. For example, the depth and length of glabellar wrinkles can be measured to quantify the degree of frowning. Normally, glabellar wrinkles are shallow and short. When a driver is fatigued and frowns, these wrinkles deepen and lengthen. Frowning frequency also increases. While frowning may be rare during normal driving, fatigue can lead to more than 3-5 frowns per minute.

[0079] In order to prevent misjudgment, this embodiment can determine whether the driver is in a fatigue state based on the comprehensive situation of the above-mentioned identification indicators.

[0080] Gaze direction tracking: Through eye tracking technology in computer vision technology or camera-based gaze estimation algorithm, it can determine whether the driver's eyes are focused on the road ahead or the dashboard and identify distraction behavior.

[0081] Physiological status monitoring: Analyze biometric sensor data, such as heart rate and blink frequency, to assess the driver's fatigue level and concentration.

[0082] (2) Driving action analysis: Analyze signals such as steering wheel angle, accelerator / brake pedal pressure changes, and vehicle posture changes to identify whether the driver's operation complies with safe driving principles, such as oversteering, sudden braking and starting, and inappropriate following distance.

[0083] More specifically, vehicle motion state analysis: analyzing sensor data such as accelerometers, gyroscopes, and wheel speed sensors to calculate key parameters such as the vehicle's real-time speed, acceleration, steering angle, and yaw rate.

[0084] Driving behavior pattern recognition: Use machine learning models such as support vector machines and random forests to classify driving action data and identify irregular driving behaviors such as sudden acceleration, sudden braking, oversteering, and inappropriate following distance.

[0085] (3) Environmental factor analysis: Analyze external environmental factors, such as weather conditions, road complexity, traffic flow, potential hazards, and other external environments that may affect driver behavior.

[0086] Risk assessment and classification: Combining the driver's behavioral characteristics and driving action analysis results, the pre-trained risk assessment model is used to quantify the risk level of the current driving behavior and divide it into three levels: low risk, medium risk, and high risk, providing a basis for subsequent decision-making.

[0087] (4) Risk assessment and classification:

[0088] More specifically, risk feature extraction: combining driver behavior characteristics with driving action analysis results to extract a set of risk assessment indicators, such as fatigue index, distraction duration, and frequency of sudden operations.

[0089] Risk model construction: Use historical driving data to train a risk assessment model, such as logistic regression or neural networks. The model input is risk characteristics, and the output is risk level. In this embodiment, the risk level is set as low risk, medium risk, and high risk.

[0090] Real-time risk assessment: The driver behavior and driving action data collected in real time are input into the risk assessment model to obtain the risk level of the current driving behavior.

[0091] In one specific implementation, driver behaviors that are prone to accidents, such as fatigued driving, risky behavior, and haphazard behavior, are classified as high risk. Risky behaviors include speeding, running red lights, and making sharp turns. Hilarious behaviors include failing to maintain a safe distance, changing lanes haphazardly, not using turn signals, and failing to yield to pedestrians. Bad habits such as not using seatbelts, using lights incorrectly, and taking hands off the steering wheel for extended periods are classified as medium risk. If the number of irregular behaviors during a trip does not reach a set threshold, the driver is considered low risk.

[0092] Step 3: Generate corresponding intervention decisions based on the driving behavior evaluation results.

[0093] Specifically, such as Figure 4 As shown, the behavioral assessment results from step 2 are classified into low, medium, and high risk levels. If the risk is determined to be medium or high, a standardized behavioral intervention decision is made and driving recommendations are generated. For medium risk, only voice and visual warnings are provided, while for high risk, in addition to voice and visual warnings, ADAS system intervention is also included.

[0094] In this embodiment, non-standard driving behaviors are classified into behaviors affected by self-factors and behaviors affected by environmental factors. Corrective analysis is performed on behaviors affected by self-factors to generate internal intervention decisions. Environmental data when environmental factors affect behaviors are extracted, and external intervention decisions are generated for behaviors affected by environmental factors in combination with the environmental data.

[0095] Step 3.1: Based on the results of behavior recognition and evaluation, the AI ​​assistant makes corresponding intervention decisions in real time:

[0096] Step 3.1: Early warning.

[0097] Internal intervention decision-making: For medium- and high-risk driving behaviors, the AI ​​assistant outputs customized voice prompts through the in-car speakers, such as "Please keep a safe distance," "It is recommended that you take a proper rest to prevent fatigue driving," "Please give way to pedestrians," "Please obey traffic rules and do not run red lights," etc., and displays corresponding warning icons on the instrument panel, HUD or other display screens.

[0098] 1) Warning rule setting: Set corresponding warning trigger rules according to the risk level, such as "Please keep a safe distance" when the risk is medium, and "It is recommended that you take a proper rest to prevent fatigue driving" when the risk is high.

[0099] 2) Speech synthesis and output: When the warning rule is triggered, the preset voice prompt is converted into an audio signal using speech synthesis technology and played to the driver through the vehicle's speakers.

[0100] 3) Visual warning display: Display corresponding warning icons or text information on the instrument panel, HUD or other display screens to enhance the visual warning effect.

[0101] External factors intervene in decision-making: Reset voice prompts and visual warnings based on environmental factors, such as the voice prompts "The road is slippery in rainy days, please be careful not to make sharp turns" and "The light is dim at night, please turn on the low beam".

[0102] Step 3.2: Assisted driving intervention: When necessary, the AI ​​assistant can automatically adjust the vehicle's assisted driving function parameters (such as adaptive cruise control, lane departure warning, emergency brake assistance, etc.) through deep integration with the vehicle's ADAS system, fine-tune the vehicle's driving status, and guide the vehicle to return to the standard driving trajectory to reduce accident risks.

[0103] More specifically, Figure 5 As shown, it is deeply integrated with the ADAS system:

[0104] 1) AI Assistant is deeply integrated with the vehicle's ADAS system (such as adaptive cruise control, lane keeping, pre-collision warning, etc.) to obtain its control interface permissions.

[0105] 2) Intervention strategy setting:

[0106] Internal intervention decision-making: Set intervention strategies for assisted driving functions based on risk levels and driving scenarios, such as moderately slowing down the vehicle speed, fine-tuning the steering angle, and strengthening lane keeping.

[0107] External intervention decision: Based on the internal intervention decision, the intervention strategy is re-formulated in combination with environmental factors such as rainy days, nights, and narrow roads.

[0108] 3) Real-time command transmission: When assisted driving intervention is required, the AI ​​assistant sends corresponding control commands to the ADAS system through the control interface to guide the vehicle back to the standard driving trajectory.

[0109] Step 3.3: Personalized suggestion push: Based on the driver's historical driving habits, current road conditions, weather conditions and other factors, the AI ​​assistant provides real-time, personalized driving suggestions, such as recommending the appropriate speed, prompting reasonable lane change timing, and reminding precautions on special road sections.

[0110] 1) Driving suggestion generation: Combining real-time traffic conditions, weather information, the driver's historical driving habits and other data, a recommendation algorithm is used to generate personalized driving suggestions, such as recommended speed, lane change timing, precautions for special road sections, etc.

[0111] 2) Suggestion display and broadcast: Driving suggestions are displayed in text or graphic form on the vehicle screen and broadcast to the driver through speech synthesis technology.

[0112] Step 3.4: Generate a test question bank based on the scenarios and specific behaviors of the driver's irregular behavior, and conduct real-time monitoring and early warning for similar scenarios encountered during the rest of the driving process. The early warning includes the methods described in steps 3.1, 3.2, and 3.3.

[0113] Step 4: Use intervention decisions to correct driver behavior and conduct ongoing testing.

[0114] In this embodiment, the intervention decision is a staged one, with the number of stages determined based on the severity of the non-standard driving behavior. The number of subsequent stages and the content of the decision are adjusted in real time based on the driver's corrective feedback. Continuous testing assesses the effectiveness of each stage's intervention decision by testing the driver's corrective behavior and generating feedback.

[0115] In a specific embodiment, score thresholds and stages are set, and the driver is tested at each stage node. For example, if the driver tends to make sharp turns when encountering a bend, it is set to one stage for every ten turns. During the ten turns, if the driver maintains normal speed and angle to turn more than eight times, and the score in the test after one stage reaches a passing score, then one stage of the test is reduced, and only voice prompts can be given in the next stage without intervention of assisted driving. Completion of all stages of the test and passing the score is considered a successful correction, and no advance warning or test is required for the next turn. If the driver's test does not meet the standards, an additional stage of assessment and warning will be added, and assisted driving intervention will be given priority in the next stage.

[0116] The above assessment settings can be customized according to actual conditions.

[0117] If the non-standard driving behavior involves external factors, such as speeding in rainy days, in order to correct the driver as soon as possible, in addition to monitoring in real scenes, simulation training can also be conducted according to different scenes, which is also counted as part of the driver's test.

[0118] The subsequent interactive feedback and guidance of the intervention process are as follows: Figure 5 As shown, a driving behavior report is generated based on the driving behavior data and stored in the established driving knowledge base for the driver to query, and remote monitoring and guidance are provided.

[0119] Step 4.1: Visual feedback: The AI ​​assistant presents a concise and clear driving behavior evaluation and improvement suggestion chart through the in-vehicle display screen, allowing the driver to intuitively understand his or her driving performance and clearly identify the areas that need improvement.

[0120] 1) Driving behavior report generation: Generate driving behavior reports regularly (e.g., at the end of each trip, daily summary), including driver behavior scores, problem behavior statistics, improvement suggestions, etc.

[0121] 2) Driving behavior charting: Use charts (such as bar charts, line charts, radar charts, etc.) to visualize driving behavior data, so that drivers can intuitively understand their own driving performance.

[0122] 3) Report and chart display: Integrate driving behavior reports and charts into a designated interface on the vehicle screen for the driver to review at any time.

[0123] Step 4.2: Interactive training: By integrating a rich driving knowledge base and simulation training resources, AI Assistant can provide customized online tutorials, video explanations, virtual driving exercises and other interactive training content based on the driver's shortcomings or common mistakes, helping drivers improve their driving skills and ability to cope with complex road conditions.

[0124] 1) Construction of driving knowledge base: Collect and organize various driving knowledge, regulations, skills, etc. to form a structured driving knowledge base.

[0125] 2) Online tutorial production: Develop graphic tutorials, short video tutorials for common driving problems, and virtual simulation training software for specific driving skills.

[0126] 3) Training resource push: Based on the driver's driving shortcomings or common mistakes, relevant online tutorials and video resources are pushed through the in-vehicle screen or supporting mobile applications to encourage drivers to participate in learning and practice.

[0127] Step 4.3: Remote Management and Data Sharing: The AI ​​Assistant connects to a cloud server to synchronize, store, and analyze driving data in real time. Vehicle owners and fleet managers can remotely access driver behavior reports, receive warning notifications, and provide remote guidance through a dedicated application. Furthermore, the cloud platform can deeply mine large amounts of driver behavior data, providing traffic management departments with decision-making support such as driving behavior research and risk area identification.

[0128] 1) Cloud service connection: The AI ​​assistant system establishes a stable data transmission channel with the cloud server to realize real-time upload and download of data.

[0129] 2) Driving data storage and management: The cloud server receives and stores driving behavior data from the in-vehicle AI assistant, and supports data query, screening, statistics and other functions.

[0130] 3) Remote monitoring and guidance: Vehicle owners and fleet managers can log in to the cloud platform through a dedicated mobile application to view driver behavior reports, receive early warning notifications, and remotely send guidance information or suggestions to drivers.

[0131] Through the above implementation steps, the AI ​​assistant of the present invention can fully realize the four major functions of data collection, behavior recognition and evaluation, decision making, interactive feedback and guidance, and effectively assist drivers to standardize driving behavior and improve driving safety.

[0132] This paper designs a method for regulating driving behavior based on complex scenarios. By integrating multiple data sources such as in-vehicle cameras, biosensors, and CAN bus data, it constructs a comprehensive, multi-level driver behavior monitoring system. This system accurately captures subtle changes in the driver's physiological state, visual focus, gestures, and other aspects, providing rich material for a deeper understanding of driving behavior. By employing AI technologies such as deep learning algorithms, image recognition technology, and natural language processing, the data collected by the perception module is analyzed and feature extracted in real time to identify the driver's behavior patterns, emotional state, concentration level, and whether there are any illegal driving behaviors. The decision-making module determines whether there are potentially dangerous driving behaviors and determines the corresponding intervention strategy. Finally, through methods such as speech synthesis, tactile feedback, and visual cues, the driver is provided with warning information, corrective instructions, or safety recommendations, guiding him or her to adjust his or her driving behavior. Personalized driving training courses and improvement suggestions are also provided.

[0133] Example 2:

[0134] The second embodiment of the present invention provides a second aspect of the present invention that provides a driving behavior regulation system based on complex scenarios, such as Figure 6 Shown, including:

[0135] The data acquisition module is configured to acquire driving data and environmental data of various drivers in real time and pre-process the driving data and environmental data.

[0136] The data acquisition module utilizes a variety of sensor devices installed in the vehicle, including but not limited to high-definition cameras, biometric sensors, vehicle motion sensors, and CAN bus interfaces, to comprehensively collect multi-dimensional data, including the driver's physiological state, gaze direction, and operational actions. This data is then preprocessed and transmitted to the data fusion and synchronization unit for synchronization and storage. These include vehicle motion sensors such as accelerometers, gyroscopes, and wheel speed sensors. The module also collects information on the driver's physiological state, such as fatigue and concentration. It also collects information on operational actions such as steering, acceleration, and braking force, as well as vehicle dynamics such as speed, position, heading, and travel path. This data acquisition process ensures real-time and accurate information, providing a foundational source for subsequent behavior recognition and evaluation.

[0137] In a specific embodiment, the data acquisition module is configured to:

[0138] 1. Hardware deployment: Various types of sensor devices are deployed inside the vehicle, including but not limited to high-definition cameras, biometric sensors, vehicle motion sensors, and interfaces connected to the vehicle CAN bus to obtain vehicle driving data. Among them, the high-definition camera is installed in front of the driver to capture facial expressions, eye direction, etc., biometric sensors such as heart rate sensors, eye movement tracking sensors, etc., are used to monitor the physiological state of the driver, and vehicle motion sensors such as accelerometers, gyroscopes, wheel speed sensors, etc., are used to capture vehicle dynamic information.

[0139] 2. Data interface configuration: Ensure that the data interfaces of various sensor devices are correctly connected and configured with the vehicle Al assistant system, ensuring the stability and real-time performance of data transmission.

[0140] 3. Data preprocessing: Perform necessary preprocessing on the received raw sensor data, such as filtering and denoising, data format conversion, unit unification, etc., to facilitate subsequent module processing.

[0141] 4. Data synchronization and storage: Real-time transmission of preprocessed data to the processing unit of the Al assistant, and storage in local cache or vehicle storage device as needed, while synchronizing to the cloud server through vehicle communication modules such as 4G / 5G, Wi-Fi, etc., to support remote management and data analysis.

[0142] The behavior recognition and evaluation module is configured to identify and analyze the preprocessed driving data using a behavior model to obtain driving behavior evaluation results.

[0143] The behavior recognition and evaluation module uses advanced technologies such as deep learning, computer vision, and pattern recognition to perform real-time processing and analysis on the collected data.

[0144] The decision-making module is configured to generate corresponding intervention decisions based on the driving behavior evaluation results, wherein non-standard driving behaviors are classified into self-factor influenced behaviors and environmental factor influenced behaviors, self-factor influenced behaviors are analyzed to generate internal intervention decisions, and environmental data when environmental factor influenced behaviors occur is extracted to generate external intervention decisions.

[0145] The decision-making module makes real-time intervention decisions based on the results of behavior recognition and evaluation.

[0146] The correction feedback module is configured to use intervention decisions to correct and continuously test the driver's behavior.

[0147] Based on the correction feedback module, drivers can view detailed driving behavior reports during parking intervals, participate in online training courses, and continuously improve their driving quality. Remote managers can use the cloud platform to effectively supervise and guide driver behavior.

[0148] Example 3:

[0149] A third embodiment of the present invention provides a medium having a program stored thereon. When the program is executed by a processor, the steps of the method for regulating driving behavior based on complex scenarios as described in the first embodiment of the present invention are implemented.

[0150] Example 4:

[0151] Embodiment 4 of the present invention provides a device comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for regulating driving behavior based on complex scenarios as described in embodiment 1 of the present invention are implemented.

[0152] The steps involved in the above embodiments 2, 3 and 4 correspond to those in the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of the embodiment 1.

[0153] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0154] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

[0155] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A driving behavior standardization method based on complex scenarios, characterized by: The following steps are involved: Acquire driving data and environmental data of multiple drivers in real time and pre-process the driving data and environmental data; Use behavioral models to identify and analyze pre-processed driving data to obtain driving behavior evaluation results; Generate corresponding intervention decisions based on the driving behavior assessment results. Specifically, classify non-standard driving behaviors into behaviors affected by self-factors and behaviors affected by environmental factors. Perform correction analysis on behaviors affected by self-factors to generate internal intervention decisions. Extract environmental data from when environmental factors affect behaviors, and combine this environmental data to generate external intervention decisions on behaviors affected by environmental factors. Using intervention decisions to modify driver behavior and conduct ongoing testing; The intervention decision is a staged intervention decision, the number of stages is determined according to the severity of the non-standard driving behavior, and the subsequent number of stages and decision content are adjusted in real time according to the driver's correction feedback.

2. The method for regulating driving behavior based on complex scenarios according to claim 1, wherein: Driving data includes the driver's physiological state, line of sight and operating actions.

3. The method for regulating driving behavior based on complex scenarios according to claim 1, wherein: The specific steps for using behavioral models to identify and analyze pre-processed driving data are as follows: Use deep learning algorithms to build behavioral models; The existing database data is combined with the driver's historical driving data to form a training set, and the behavior model is trained; The trained behavior model is used to evaluate real-time driving data to obtain driving behavior evaluation results.

4. The method for regulating driving behavior based on complex scenarios as claimed in claim 3, characterized in that: The driving behavior assessment results include standard driving behavior and non-standard driving behavior, and the risk level is judged according to the severity of the non-standard driving behavior.

5. The method for regulating driving behavior based on complex scenarios as claimed in claim 1, characterized in that: Environmental data includes weather condition data, road complexity data, traffic flow data and potential hazard source data.

6. The method for regulating driving behavior based on complex scenarios as claimed in claim 1, characterized in that: The continuous test is to test the driver's corrective behavior on the implementation effect of the intervention decision at each stage and generate driver correction feedback.

7. A driving behavior regulation system based on complex scenarios, characterized by: include: a data acquisition module configured to acquire driving data and environmental data of various drivers in real time and pre-process the driving data and environmental data; a behavior recognition and evaluation module configured to use the behavior model to identify and analyze the pre-processed driving data to obtain a driving behavior evaluation result; A decision-making module is configured to generate corresponding intervention decisions based on the driving behavior assessment results, wherein non-standard driving behaviors are classified into behaviors affected by internal factors and behaviors affected by environmental factors, and correction analysis is performed on behaviors affected by internal factors to generate internal intervention decisions. Environmental data when environmental factors affect behaviors is extracted and combined with the environmental data to generate external intervention decisions for the behaviors affected by environmental factors; A correction feedback module configured to use intervention decisions to correct the driver's behavior and conduct continuous testing; The intervention decision is a staged intervention decision, the number of stages is determined according to the severity of the non-standard driving behavior, and the subsequent number of stages and decision content are adjusted in real time according to the driver's correction feedback.

8. A computer-readable storage medium, characterized in that A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the driving behavior standardization method based on complex scenarios according to any one of claims 1-6.

9. A terminal device, characterized in that: The method comprises a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the complex scenario-based driving behavior standardization method according to any one of claims 1 to 6.

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