Drunk driving prevention and early warning system integrated with AI algorithm

Abstract

The invention belongs to the technical field of traffic transportation safety, and discloses a drunk driving prevention and early warning system integrated with an AI algorithm, and the system comprises the steps: collecting the multi-mode state data of a driver; identifying a compensatory prudent operation mode of the driver based on the multi-modal state data and extracting a hidden risk indicator; according to the historical multi-mode state data and the current compensatory prudent operation mode, the alcohol influence potential vulnerability of the driver is evaluated; generating a personalized early warning intervention scheme based on the hidden risk index, the current compensatory cautious operation mode and the alcohol influence potential vulnerability; according to a personalized early warning intervention scheme, drunk driving prevention and early warning of the driver are realized; and the safety of road driving is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of transportation safety technology, and more specifically, to a drunk driving prevention and early warning system integrating AI algorithms. Background Technology

[0002] With the integrated development of vehicle networking technology and intelligent sensors, driver status monitoring based on multimodal data has become an important component of the proactive traffic safety protection system. The identification of driving behavior under the influence of alcohol is a key link in preventing major traffic accidents, and its accuracy and real-time performance are directly related to the life safety of road users. To achieve accurate prediction of drunk driving risks and provide effective intervention, intelligent early warning systems integrating physiological signal detection, behavioral pattern analysis, and environmental perception technologies have been widely studied in recent years. These systems collect multi-dimensional features such as the driver's operating trajectory, eye tracking data, and vehicle control stability in real time, and combine them with machine learning algorithms to construct risk assessment models, thereby achieving automatic identification and early warning of abnormal driving states.

[0003] However, existing drunk driving detection technologies have significant blind spots in identifying the masking effect of physiological compensatory behaviors. This problem is particularly insidious in risk assessments during the mild to moderate alcohol-induced stages. Specifically, when a driver is in the early stages of alcohol influence, their nervous system activates an adaptive compensation mechanism to counteract the decline in ability. This compensation manifests as excessive caution, such as the driver actively reducing speed, increasing following distance, decreasing lane change frequency, and deliberately extending observation time. For example, in normal urban driving scenarios, drivers under the influence of alcohol may attempt to maintain a seemingly normal driving state by continuously gripping the steering wheel tightly, rigidly maintaining the center of the lane, and driving at a speed below the required road conditions. These compensatory actions make abnormal indicators such as lane departure frequency, speed fluctuation amplitude, and sudden changes in steering angle, which are the focus of conventional monitoring systems, appear to be better than normal. This physiological compensatory masking not only causes the warning system to misjudge high-risk drivers as safe, but also exposes fatal vulnerabilities when emergency road conditions require a quick response. When encountering emergencies such as pedestrians crossing the road or vehicles braking suddenly, the compensation mechanism, which originally relied on subjective will, collapses instantly, revealing the true slow reaction and misjudgment. The current mainstream approach to solving this problem is to set standardized thresholds for abnormal behavior and monitor significant signals that deviate from normal driving patterns, triggering warnings by identifying obvious abnormalities such as operational errors and distraction. However, this identification logic based on deviation detection not only fails to capture the compensatory risk characteristics hidden behind overly standardized behavior, but also lacks the ability to deeply assess the driver's operational rigidity, stress reserve capacity, and the naturalness of behavioral patterns. This makes it difficult to penetrate the appearance of compensatory behavior to identify the essential risks, and it cannot cope with the complex situation where drivers temporarily conceal their ability deficiencies through subjective efforts, and there is a risk of systemic missed detection. Ultimately, this limits the effectiveness of drunk driving prevention technology in scenarios with hidden risks.

[0004] In view of this, the present invention proposes a drunk driving prevention and early warning system integrating AI algorithms to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a drunk driving prevention and early warning system integrating AI algorithms, characterized in that it comprises: Data acquisition module: Collects multimodal state data of the driver; Compensatory Behavior Recognition Module: Based on multimodal state data, this module identifies the driver's compensatory cautious operating mode and extracts hidden risk indicators. Risk assessment module: Based on historical multimodal state data and current compensatory cautious operating patterns, assess the driver's potential vulnerability to alcohol-related effects; Intervention strategy construction module: Based on hidden risk indicators, current compensatory cautious operating patterns, and potential vulnerability to alcohol effects, it generates personalized early warning and intervention plans; Early warning execution module: Implements driver drunk driving prevention early warning according to personalized early warning intervention plans.

[0006] Furthermore, based on multimodal state data, the driver's compensatory cautious operating patterns are identified and hidden risk indicators are extracted, including: Separate subsets of operational trajectory, visual dynamics, and physiological response from multimodal state data; For a subset of the operation trajectory, we track abnormal matches between the duration of steering wheel grip and the frequency of steering fine-tuning. When the grip duration exceeds the normal threshold and the fine-tuning frequency is lower than the expected frequency, it is marked as an indication of over-stability compensation. For a dynamic subset of gaze, analyze the unnatural extension of gaze duration and shift interval. If the gaze duration exceeds the preset road condition requirements and the shift interval is evenly distributed, it is marked as a sign of deliberate observation compensation. For a subset of physiological responses, latent fluctuations in heart rate variability and skin conductance were detected. If heart rate variability showed low frequency and skin conductance showed a continuous increase, it was marked as a sign of compensatory effort. By cross-validating and integrating signs of overly stable compensation, signs of deliberate observation of compensation, and signs of compensation efforts, hidden risk indicators are generated.

[0007] Furthermore, subsets of operational trajectories, visual dynamics, and physiological responses are separated from the multimodal state data, including: Multimodal state data is divided into real-time operational flow, visual attention flow, and biological signal flow; The real-time operation flow is analyzed by trajectory layering, and the speed maintenance segment, following distance extension segment, and lane change avoidance segment are isolated as subsets of the operation trajectory. Dynamic path mapping is performed on the visual attention flow to isolate the forward gaze cluster, the side-view scanning cluster, and the rearview mirror inspection cluster as dynamic subsets of the gaze. The biosignal stream was subjected to response peak-valley separation to isolate the baseline offset of heart rate, the cumulative peak of skin conductance, and the smoothing of respiratory rhythm as a subset of physiological responses.

[0008] Furthermore, based on historical multimodal state data and current compensatory cautious operating patterns, assess the driver's potential vulnerability to alcohol-related effects, including: Historical multimodal state data is grouped into scenarios to identify compensation pattern sequences in past driving situations; Extract the decay points of emergency response delay and judgment accuracy in each compensation mode sequence to form a vulnerability evolution chain; By combining the current compensatory and cautious operating model with the vulnerability evolution chain under similar historical scenarios, we can infer the probability of collapse under sudden events; The ratio of the inferred collapse probability to the driver's emergency threshold is used as the potential vulnerability to alcohol effects, which characterizes the risk of failure of compensatory mechanisms under stress.

[0009] Furthermore, personalized early warning and intervention plans are generated, including: Based on hidden risk indicators, drivers are classified into levels of compensatory strength and potential vulnerabilities, and intervention priority sequences are formulated according to the current compensatory cautious operation mode. By integrating the potential vulnerability to alcohol-related effects and the intervention priority sequence, we can analyze the timing and intensity of early warning triggers and construct personalized early warning and intervention plans. These personalized plans include voice prompt parameters and vehicle auxiliary control parameters.

[0010] Furthermore, based on hidden risk indicators, drivers are classified into levels of compensation intensity and potential vulnerabilities, including: The hidden risk indicators are classified into three levels of compensation intensity: if the hidden risk indicator exceeds the first intensity threshold, it is classified as high-intensity compensation level; if the hidden risk indicator is between the second intensity threshold and the first intensity threshold, it is classified as medium-intensity compensation level; and if the hidden risk indicator is below the second intensity threshold, it is classified as low-intensity compensation level. Based on the driver's historical response patterns, the hidden risk indicators are personalized and modified to obtain the modified hidden risk indicators. The modified hidden risk indicators are sorted from high to low, and the behavioral points corresponding to the top N indicators are selected as potential vulnerability points and their intensity levels are marked.

[0011] Furthermore, according to the personalized early warning and intervention plan, the driver's drunk driving prevention and early warning can be realized, including: control of voice prompts, execution of vehicle auxiliary control, and early warning adjustment based on road conditions.

[0012] Furthermore, control over voice prompts includes: Define the range of intonation variations and interval repetition rules for voice prompts; Based on the voice prompt parameters in the personalized early warning and intervention plan, the current risk level is matched with the prompt template; the matched prompt template is decomposed into progressive information units, transitioning from general reminder units to specific risk units; Each information unit is played sequentially, and the driver's feedback response is monitored during playback. If the feedback response shows signs of being ignored, an emphasis unit is inserted.

[0013] Furthermore, the execution of vehicle auxiliary controls includes: Set the activation threshold and duration limit for vehicle assistance controls; Based on the vehicle auxiliary control parameters in the personalized early warning and intervention plan, identify the matching degree between the target control type and the current vehicle state; The target control type is decomposed into auxiliary sequence steps, starting from the speed limiting step and extending to the steering assist step; each auxiliary sequence step is activated sequentially, and the stability changes after each step is activated are monitored. When the stability improvement reaches the preset standard, the auxiliary sequence steps are gradually deactivated to restore driver control.

[0014] Further, road condition-adaptive early warning adjustments include: Detect current road conditions for vehicles, including traffic density, road surface type, and weather conditions; Based on road condition factors, assess the exposure risk and intervention appropriateness of compensation behavior, and adjust the early warning parameters based on the exposure risk and intervention appropriateness; When high-density traffic conditions are detected, increase the frequency of voice prompts and the depth of vehicle assistance control; When a low-risk road surface type is detected, the warning intensity is reduced to minimize unnecessary intervention and avoid driver fatigue, thus achieving a preventive warning against drunk driving.

[0015] The technical effects and advantages of the drunk driving prevention and early warning system integrating AI algorithms of the present invention are as follows: This invention tracks abnormalities in the duration and frequency of maneuvering, unnatural extensions and uniform shifts in gaze dynamics, and latent fluctuations in physiological responses, and performs cross-validation fusion to extract hidden risk indicators. This allows for the identification of the true risk of alcohol-induced effects hidden behind excessive caution, penetrating seemingly normal driving behavior. By retrospectively identifying emergency response delays and accuracy decay points in historical compensation pattern sequences, a vulnerability evolution chain is constructed. Combined with current compensatory cautious driving patterns, the probability of collapse under sudden events is inferred, assessing the potential vulnerability to alcohol-induced effects. This quantifies the failure risk of compensation mechanisms under stress, reducing systematic underestimation caused by relying solely on superficial stability judgments. The invention also examines the different levels of compensation intensity and potential vulnerabilities of different drivers in real-world situations. Significant individual differences exist at each point, and changes in road conditions affect the degree of risk exposure and the appropriateness of intervention. By personalizing and grading the intensity of hidden risk indicators based on historical response patterns, and combining the potential vulnerability of alcohol effects, a personalized early warning and intervention plan including trigger timing and intensity levels is constructed. The early warning parameters are dynamically adjusted based on road condition factors such as traffic density and road surface type, adaptively matching driver characteristics and environmental needs. This avoids misjudgment of standardized thresholds in compensatory masking scenarios and driver fatigue caused by excessive intervention. As a result, the early warning system can accurately identify the true capability deficiency state hidden under compensatory behavior, provide effective, graded, and gradual intervention before the compensatory mechanism collapses, and improve the accuracy of identifying hidden risks of drunk driving and the timeliness of prevention and early warning. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a drunk driving prevention and early warning system integrating AI algorithms according to the present invention; Figure 2 This is a schematic diagram of a drunk driving prevention and early warning method integrating AI algorithms according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1:

[0019] Please see Figure 1 As shown in the figure, this embodiment of a drunk driving prevention and early warning system integrating AI algorithms includes: The system comprises a data acquisition module, a compensatory behavior identification module, a risk assessment module, an intervention strategy construction module, and an early warning execution module. Through data flow and functional collaboration, these modules enable real-time monitoring and preventative intervention of drunk driving behavior, effectively addressing the problems of traditional drunk driving detection methods that rely on single physiological indicators and are easily spoofed.

[0020] Data acquisition module: Collects multimodal state data of the driver; By integrating multiple sensors and monitoring devices into the vehicle system, a comprehensive driver status perception network is formed, which collects various data related to drunk driving risks in real time. This data collectively constitutes the driver's operational behavior, physiological reactions, and environmental characteristics, providing reliable data support for subsequent AI algorithm analysis.

[0021] Preferably, in some possible implementations of the embodiments of the present invention, the multimodal state data collection includes: collecting driver operation trajectory data, such as steering wheel rotation angle and accelerator pedal pressure, through an in-vehicle camera and gyroscope to obtain the operation trajectory; collecting the driver's gaze focus position and eyelid closure frequency through an eye tracker to constitute gaze dynamics; collecting heart rate, skin conductance, and respiratory rate through contact physiological sensors to constitute physiological responses; collecting vehicle speed, following distance, and lane change frequency through the vehicle CAN bus to constitute vehicle dynamics; and recording driver voice tone changes and environmental noise levels through a microphone array to constitute voice behavior.

[0022] It should be noted that the eye tracker uses an infrared high-frame-rate camera, positioned above the dashboard, with a sampling frequency of 30 times per second; physiological sensors are integrated into the steering wheel grip area and seat contact surface to capture latent physiological fluctuations; vehicle CAN bus data is acquired via the OBD interface to ensure time synchronization with the operation trajectory. The collected raw data undergoes noise filtering and standardization processing to form a unified multimodal state data stream for subsequent module processing.

[0023] In one implementation of this invention, the data acquisition period for various types of data is set to 500 milliseconds to ensure that the system can capture subtle changes in driving behavior in a timely manner. For example, when driving on urban roads, the system records two gaze shifts per second to avoid missing abnormal stops.

[0024] Compensatory Behavior Recognition Module: Based on multimodal state data, this module identifies the driver's compensatory cautious operating mode and extracts hidden risk indicators. When driving under the influence of alcohol, drivers often mask impaired cognition by operating with excessive caution. While this compensatory behavior may appear normal, it conceals the risks associated with the effects of alcohol. Identifying these patterns using AI algorithms can expose potential dangers at an early stage.

[0025] Preferably, in some possible implementations of the embodiments of the present invention, identifying the driver's compensatory cautious operating mode and extracting hidden risk indicators based on multimodal state data includes: separating an operating trajectory subset, a gaze dynamic subset, and a physiological response subset from the multimodal state data; for the operating trajectory subset, tracking abnormal matching between the duration of steering wheel grip and the frequency of steering fine-tuning, and marking it as an over-stabilization compensation sign when the grip duration exceeds a normal threshold and the fine-tuning frequency is lower than expected; for the gaze dynamic subset, analyzing the unnatural extension of the fixation point dwell time and the shift interval, and marking it as a deliberate observation compensation sign when the fixation point dwell time exceeds the road condition requirements and the shift interval is evenly distributed; for the physiological response subset, detecting latent fluctuations in heart rate variability and skin conductance, and marking it as a compensatory effort compensation sign when the heart rate variability shows low frequency dominance and skin conductance shows a continuous increase; and cross-validating and fusing the over-stabilization compensation sign, the deliberate observation compensation sign, and the compensatory effort compensation sign to generate hidden risk indicators, wherein the over-stabilization compensation sign is given priority verification weight to highlight the core of behavioral camouflage.

[0026] Preferably, in some possible implementations of the embodiments of the present invention, separating the operation trajectory subset, the gaze dynamics subset, and the physiological response subset from the multimodal state data includes: dividing the multimodal state data into real-time operation flow, visual attention flow, and biosignal flow; performing trajectory hierarchical analysis on the real-time operation flow to isolate the speed maintenance segment, the following distance extension segment, and the lane change avoidance segment as the operation trajectory subset; performing dynamic path mapping on the visual attention flow to isolate the forward gaze cluster, the side-view scanning cluster, and the rearview mirror inspection cluster as the gaze dynamics subset; performing response peak-valley separation on the biosignal flow to isolate the heart rate baseline offset portion, the skin conductance peak accumulation portion, and the respiratory rhythm smoothing portion as the physiological response subset; and ensuring that time synchronization markers are retained during the separation of each subset to support the temporal consistency of subsequent cross-validation. By tracking abnormal matching of driving trajectories, unnatural extension of visual dynamics, and implicit fluctuations in physiological responses, and performing cross-validation fusion, hidden risk indicators are extracted. This allows the system to penetrate the surface-level normal driving behavior and identify the real risk of alcohol effects hidden behind excessive caution, overcoming the blind spots of traditional systems due to the masking effect of physiological compensatory behavior.

[0027] The standard thresholds for the operational trajectory subset are: a single steering wheel grip duration exceeding 5 seconds (based on industry statistics showing an average grip duration of 3 seconds for normal drivers; this embodiment uses 5 seconds to cover mild drunk driving disguise); and a steering fine-tuning frequency lower than the expected value of 2 times / minute (the expected value is 5 times / minute under normal road conditions; this embodiment uses 2 times / minute to highlight conservative deviations). For the visual dynamic subset, the road condition-required dwell time is set to a forward gaze exceeding 4 seconds (based on experimental data showing a standard gaze duration of 2 to 3 seconds on highways; this embodiment uses 4 seconds to identify signs of prolonged gaze); and a uniform distribution of shift intervals is defined as a standard deviation of less than 0.5 seconds (the normal shift standard deviation is 1.2 seconds; this embodiment uses 0.5 seconds to capture deliberate uniformity). For the physiological response subset, low-frequency heart rate variability is dominated by an LF / HF ratio exceeding 3 (based on a physiological baseline of 1.5 in conscious condition; this embodiment uses 3 to mark compensating for fluctuations); and a sustained increase in skin conductance is defined as an increase exceeding 20% ​​of baseline (10% under normal stress; this embodiment uses 20% to expose latent effort). Cross-validation fusion employs a time-alignment mechanism, with the weight of overstability compensation indicators set to 0.5.

[0028] In one implementation of this invention, for a driver driving on a city road at night, the system separates the operation trajectory subset showing a holding time of up to 7 seconds and a fine-tuning frequency of only 1 time / minute, marking signs of over-stability compensation; the gaze dynamic subset shows a gaze dwell of 5.2 seconds and a shift interval standard deviation of 0.3 seconds, marking signs of deliberate observation compensation; the physiological response subset shows a heart rate LF / HF ratio of 3.8 and a skin conductance increase of 25%, marking signs of compensatory effort compensation; after fusion, the generated concealment risk index value is 0.75, indicating a moderate camouflage risk.

[0029] Risk assessment module: Based on historical multimodal state data and current compensatory cautious operating patterns, assess the driver's potential vulnerability to alcohol-related effects; Historical data analysis helps reveal the evolution of drivers' behavior under the influence of alcohol. By matching it with current patterns, it can predict the failure point of compensatory mechanisms and achieve forward-looking risk assessment.

[0030] Preferably, in some possible implementations of the embodiments of the present invention, assessing the driver's potential vulnerability to alcohol effects based on historical multimodal state data and the current compensatory cautious operating mode includes: performing scenario retrospective grouping on historical multimodal state data to identify compensatory mode sequences in past driving situations; extracting the decay points of emergency response delay and judgment accuracy in each compensatory mode sequence to form a vulnerability evolution chain; combining the current compensatory cautious operating mode with the vulnerability evolution chain in similar historical situations to infer the probability of collapse under sudden events; and using the comparison between the inferred collapse probability and the driver's emergency threshold as the potential vulnerability to alcohol effects, whereby the potential vulnerability to alcohol effects characterizes the risk of failure of the compensatory mechanism under stress.

[0031] The scenario retrospective grouping uses timestamp clustering to divide historical data into scenarios such as urban congestion and rural highways, with each group being 10 minutes long (based on traffic engineering standards for driving behavior cycles; this embodiment uses 10 minutes to ensure sequence integrity). The emergency response delay decay point is set at 1.5 seconds beyond the normal response time (the normal value is 0.8 seconds; this embodiment uses 1.5 seconds to mark alcohol-induced lethargy), and the judgment accuracy decay point is set at below 85% (95% in a sober state; this embodiment uses 85% to highlight vulnerability evolution). The driver emergency threshold is set to a collapse probability of 0.3 based on personalized historical averages (extracted from the database; this embodiment uses 0.3 to reflect individual differences). By identifying emergency response delays and judgment accuracy decay points in historical compensation pattern sequences through scenario retrospective analysis, a vulnerability evolution chain is constructed. Combined with the current compensatory cautious operating mode, the collapse probability under emergencies is inferred, thereby quantifying the failure risk of the compensation mechanism under pressure. This reduces the risk of systemic omissions caused by judging only superficial stability and improves the system's early warning foresight in sudden road conditions.

[0032] In one implementation of this invention, for a driver with a 3-month driving record, historical data backtracking shows that in similar urban scenarios, the emergency delay in the compensation sequence reaches 2.1 seconds and the accuracy drops to 78%, forming a vulnerability evolution chain. After current pattern matching, the probability of sudden event collapse is inferred to be 0.45. The inference of the collapse probability is obtained based on the analysis of time series prediction models (such as ARIMA and LSTM) constructed from historical collapse event data. Compared with the emergency threshold, the potential vulnerability value of alcohol influence is generated as 0.65, indicating a medium to high failure risk.

[0033] Intervention strategy construction module: Based on hidden risk indicators, current compensatory cautious operating patterns, and potential vulnerability to alcohol effects, it generates personalized early warning and intervention plans; In reality, there are significant individual differences in the level of compensation and potential vulnerabilities among different drivers, and changes in road conditions affect the degree of risk exposure and the appropriateness of intervention. Intervention plans need to be developed based on individual differences to ensure that warnings are timely without interfering with normal driving. By integrating multi-source indicators, adaptive strategies can be constructed to maximize the preventive effect.

[0034] Preferably, in some possible implementations of the embodiments of the present invention, generating a personalized early warning intervention plan includes: classifying the driver's compensation intensity level and potential vulnerability based on hidden risk indicators, and formulating an intervention priority sequence according to the current compensatory cautious operation mode; integrating the potential vulnerability to alcohol effects and the intervention priority sequence to calculate the early warning triggering timing and intensity level, and constructing a personalized early warning intervention plan; the personalized early warning intervention plan includes voice prompt parameters and vehicle auxiliary control parameters.

[0035] Preferably, in some possible implementations of the embodiments of the present invention, classifying the driver's compensation intensity level and potential vulnerability points based on hidden risk indicators includes: classifying the hidden risk indicators into intensity levels and setting three levels of compensation intensity: if the hidden risk indicator exceeds a first intensity threshold, it is classified as a high-intensity compensation level; if the hidden risk indicator is between a second intensity threshold and a first intensity threshold, it is classified as a medium-intensity compensation level; if the hidden risk indicator is below a second intensity threshold, it is classified as a low-intensity compensation level; applying personalized corrections to the hidden risk indicators based on the driver's historical response patterns to obtain corrected hidden risk indicators; sorting the corrected hidden risk indicators from high to low, selecting the behavioral points corresponding to the top N indicators as potential vulnerability points, and labeling their intensity levels.

[0036] This invention personalizes and grades the intensity of hidden risk indicators based on historical response patterns, and constructs a personalized early warning and intervention scheme that includes trigger timing and intensity levels by combining the potential vulnerability of alcohol effects. This achieves adaptive matching of driver characteristics and environmental needs, avoiding misjudgment of standardized thresholds in compensatory masking scenarios and driver fatigue caused by excessive intervention.

[0037] The first intensity threshold is set to 0.8 (based on the statistical average of indicators in high-risk drunk driving cases; this embodiment uses a value of 0.8 to classify emergency levels), the second intensity threshold is set to 0.5 (based on the clinical threshold of moderate camouflage; this embodiment uses a value of 0.5 to cover progressive risk), and the N value is set to 3 (based on driving behavior studies with a number of typical behavioral points; this embodiment uses a value of 3 to focus on core vulnerabilities). The historical response pattern correction factor is set to 0.9 based on past intervention acceptance rates (1.0 for high responders; this embodiment uses a value of 0.9 to adjust for individual adaptability). The intervention priority sequence is determined according to the compensation pattern, for example, prioritizing voice-assisted control after high-intensity levels.

[0038] In one implementation of this invention, the hidden risk index of 0.75 exceeds the second threshold but is lower than the first threshold, and is classified as medium-intensity compensatory level; after correction, the value is 0.68, and the top 3 points (steering fine-tuning, gaze persistence, and heart rate fluctuation) are selected and marked as potential vulnerability points; after integrating the vulnerability of 0.65, the triggering time is set to risk duration of 2 minutes, the intensity level is medium, and the scheme includes a voice prompt frequency of once every 30 seconds and an auxiliary control speed limit of 5km / h.

[0039] Early warning execution module: Implements driver drunk driving prevention early warning according to personalized early warning intervention plans; Traditional warning systems often intervene abruptly, causing driver resentment. This invention employs a layered, gradual approach to ensure smooth and effective intervention, thereby improving the success rate of prevention. Warning execution includes controlling voice prompts, implementing vehicle assistance controls, and adjusting warnings based on road conditions.

[0040] Preferably, in some possible implementations of the embodiments of the present invention, a driver drunk driving prevention warning is implemented according to a personalized warning intervention scheme, including: control of voice prompts, execution of vehicle auxiliary control, and warning adjustment based on road condition adaptability.

[0041] Preferably, in some possible implementations of the embodiments of the present invention, the control of voice prompts includes: defining the tone variation range and interval repetition rules of the voice prompts; matching the current risk level with a preset prompt template according to the voice prompt parameters in the personalized warning intervention plan; decomposing the matched prompt template into progressive information units, transitioning from general reminder units to specific risk units; sequentially playing each progressive information unit to achieve progressive delivery of voice prompts; monitoring the driver's feedback response during playback, and if the feedback response shows signs of ignoring, inserting an emphasis unit to strengthen the warning effect.

[0042] The pitch variation range is set to a 20% increase to enhance attention, and the repetition interval is once every 20 seconds (twice the normal conversation interval; in this embodiment, it is set to 20 seconds to avoid fatigue). Preset prompt templates include low-level "Pay attention to road conditions," medium-level "Suggest rest," and high-level "Stop immediately." Feedback response monitoring is performed through eye movement or speech recognition; ignored signs are defined as gaze deviation exceeding 3 seconds (normal response is 1 second; in this embodiment, it is set to 3 seconds).

[0043] In one implementation of this invention, for medium-risk situations, the matching template is decomposed into "Keep Focused" (general unit, played for 5 seconds) and transitioned to "Abnormal Operation Detected" (risk unit, played for 10 seconds). After sequential playback, if the driver's gaze deviates for 4 seconds, it is ignored, and an emphasis unit "Please Respond Immediately" is inserted, with the total duration controlled within 30 seconds.

[0044] Preferably, in some possible implementations of the embodiments of the present invention, the execution of vehicle auxiliary control includes: setting an activation threshold and duration limit for vehicle auxiliary control; identifying the matching degree between the target control type and the current vehicle state based on the vehicle auxiliary control parameters in the personalized warning intervention scheme; decomposing the target control type into auxiliary sequence steps, extending from the speed limit step to the steering assistance step; sequentially activating each auxiliary sequence step to achieve smooth intervention of vehicle control; monitoring the stability changes after each step is activated, and gradually releasing control to restore driver control when the stability improvement reaches a preset standard. By dynamically adjusting the warning parameters based on road condition elements such as traffic density and road surface type, the system can adaptively match driver characteristics and environmental needs, thereby providing effective, graded, and gradual intervention before the compensation mechanism collapses, improving the accuracy of identifying hidden risks of drunk driving and the timeliness of prevention and warning.

[0045] The activation threshold is set to a risk level of medium or higher (based on ADAS system standards; this embodiment uses a medium level to ensure safety), and the duration is limited to 2 minutes (to prevent over-reliance; this embodiment uses 2 minutes). Matching accuracy is determined by comparing vehicle speed with the solution parameters; target types include speed limiting and steering assist. The preset standard is a 15% decrease in yaw rate (normal driving fluctuations; this embodiment uses 15% to indicate stability).

[0046] In one implementation of this invention, after the scheme parameters are matched, the process is decomposed into a speed limiting step (reducing speed to 80km / h, activating for 10 seconds) and extended to a steering assist step (fine-tuning the angle by 2 degrees, activating for 20 seconds); after sequential activation, the yaw rate is monitored to decrease by 18%, and control is gradually released after the standard is reached.

[0047] Preferably, in some possible implementations of the embodiments of the present invention, the warning adjustment based on road condition adaptability includes: detecting the current road condition elements of the vehicle, including traffic density, road surface type, and weather conditions; assessing the exposure risk and intervention suitability of compensatory behavior based on the road condition elements, and adjusting the warning parameters based on the exposure risk and intervention suitability; when high-density traffic conditions are detected, enhancing the immediacy of the warning, increasing the frequency of voice prompts, and deepening the vehicle assistance control; when low-risk road surface types are detected, mitigating the warning intensity, reducing unnecessary intervention to avoid driver fatigue, and achieving a warning for preventing drunk driving.

[0048] Traffic density is detected by vehicle-mounted radar, with high density defined as vehicle spacing less than 20 meters (urban peak hour standard, 20 meters in this embodiment). Road surface type is identified via GPS and sensors, with low risk defined as dry asphalt road. When the exposure risk assessment is high, the immediate enhancement interval is reduced to 10 seconds (normally 20 seconds, 10 seconds in this embodiment), and the depth enhancement assists control to the speed limit of 10 km / h.

[0049] In one implementation of this invention, under high-density traffic (15-meter spacing), the voice frequency is adjusted to every 10 seconds and the assist depth is increased to 3 degrees of steering assist; under low-risk dry road conditions, the intensity is reduced to a voice interval of 40 seconds and the assist speed is limited to 15 km / h to avoid fatigue.

[0050] It should be noted that after each warning is issued, the system records the intervention effect and the driver's response, continuously optimizes the strategy parameters, and forms an adaptive learning mechanism, so that the accuracy of prevention improves with the use of the system.

[0051] In other embodiments of the present invention, multimodal state data can also be input into a pre-trained neural network model, and the model can directly output an intervention plan, simplifying the process and improving real-time performance.

[0052] This invention tracks abnormalities in the duration and frequency of maneuvering, unnatural extensions and uniform shifts in gaze dynamics, and latent fluctuations in physiological responses, and performs cross-validation fusion to extract hidden risk indicators. This allows for the identification of the true risk of alcohol-induced effects hidden behind excessive caution, penetrating seemingly normal driving behavior. By retrospectively identifying emergency response delays and accuracy decay points in historical compensation pattern sequences, a vulnerability evolution chain is constructed. Combined with current compensatory cautious driving patterns, the probability of collapse under sudden events is inferred, assessing the potential vulnerability to alcohol-induced effects. This quantifies the failure risk of compensation mechanisms under stress, reducing systematic underestimation caused by relying solely on superficial stability judgments. The invention also examines the different levels of compensation intensity and potential vulnerabilities of different drivers in real-world situations. Significant individual differences exist at each point, and changes in road conditions affect the degree of risk exposure and the appropriateness of intervention. By personalizing and grading the intensity of hidden risk indicators based on historical response patterns, and combining the potential vulnerability of alcohol effects, a personalized early warning and intervention plan including trigger timing and intensity levels is constructed. The early warning parameters are dynamically adjusted based on road condition factors such as traffic density and road surface type, adaptively matching driver characteristics and environmental needs. This avoids misjudgment of standardized thresholds in compensatory masking scenarios and driver fatigue caused by excessive intervention. As a result, the early warning system can accurately identify the true capability deficiency state hidden under compensatory behavior, provide effective, graded, and gradual intervention before the compensatory mechanism collapses, and improve the accuracy of identifying hidden risks of drunk driving and the timeliness of prevention and early warning.

[0053] Example 2:

[0054] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. This embodiment provides a method for preventing and warning drunk driving using an integrated AI algorithm, including: Step S1: Collect multimodal state data of the driver; Step S2: Identify the driver's compensatory cautious operating mode based on multimodal state data and extract hidden risk indicators; Step S3: Assess the driver's potential vulnerability to alcohol-induced effects based on historical multimodal state data and current compensatory cautious operating patterns; Step S4: Based on hidden risk indicators, current compensatory cautious operating patterns, and potential vulnerability to alcohol effects, generate personalized early warning and intervention plans; Step S5: Implement a driver's drunk driving prevention and early warning system according to the personalized early warning and intervention plan.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A drunk driving prevention and early warning system integrating AI algorithms, characterized in that, include: Data acquisition module: Collects multimodal state data of the driver; Compensatory Behavior Recognition Module: Based on the multimodal state data, identify the driver's compensatory cautious operating mode and extract hidden risk indicators; Risk assessment module: Based on historical multimodal state data and current compensatory cautious operating patterns, assess the driver's potential vulnerability to alcohol-related effects; Intervention strategy construction module: Based on the aforementioned hidden risk indicators, current compensatory cautious operating mode, and potential vulnerability to alcohol effects, generate personalized early warning and intervention plans; Early warning execution module: Implements driver drunk driving prevention early warning according to the personalized early warning intervention plan.

2. The drunk driving prevention and early warning system integrating AI algorithms according to claim 1, characterized in that, The process of identifying the driver's compensatory cautious operating mode and extracting hidden risk indicators based on the multimodal state data includes: The operation trajectory subset, the line-of-sight dynamic subset, and the physiological response subset are separated from the multimodal state data; For a subset of the operation trajectory, we track abnormal matches between the duration of steering wheel grip and the frequency of steering fine-tuning. When the grip duration exceeds the normal threshold and the fine-tuning frequency is lower than the expected frequency, it is marked as an indication of over-stability compensation. For a dynamic subset of gaze, analyze the unnatural extension of gaze duration and shift interval. If the gaze duration exceeds the preset road condition requirements and the shift interval is evenly distributed, it is marked as a sign of deliberate observation compensation. For a subset of physiological responses, latent fluctuations in heart rate variability and skin conductance were detected. If heart rate variability showed low frequency and skin conductance showed a continuous increase, it was marked as a sign of compensatory effort. By cross-validating and fusing the aforementioned signs of overly stable compensation, signs of deliberate observation of compensation, and signs of compensation efforts, a hidden risk indicator is generated.

3. The drunk driving prevention and early warning system integrating AI algorithms according to claim 2, characterized in that, The separation of the operation trajectory subset, gaze dynamics subset, and physiological response subset from the multimodal state data includes: Multimodal state data is divided into real-time operational flow, visual attention flow, and biological signal flow; The real-time operation flow is analyzed by trajectory layering, and the speed maintenance segment, following distance extension segment, and lane change avoidance segment are isolated as subsets of the operation trajectory. Dynamic path mapping is performed on the visual attention flow to isolate the forward gaze cluster, the side-view scanning cluster, and the rearview mirror inspection cluster as dynamic subsets of the gaze. The biosignal stream was subjected to response peak-valley separation to isolate the baseline offset of heart rate, the cumulative peak of skin conductance, and the smoothing of respiratory rhythm as a subset of physiological responses.

4. The drunk driving prevention and early warning system integrating AI algorithms according to claim 1, characterized in that, The assessment of a driver's potential vulnerability to alcohol-related influences, based on historical multimodal state data and current compensatory cautious operating patterns, includes: Historical multimodal state data is grouped into scenarios to identify compensation pattern sequences in past driving situations; Extract the decay points of emergency response delay and judgment accuracy in each compensation mode sequence to form a vulnerability evolution chain; By combining the current compensatory and cautious operating model with the vulnerability evolution chain under similar historical scenarios, we can infer the probability of collapse under sudden events; The ratio of the inferred collapse probability to the driver's emergency threshold is used as the potential vulnerability to alcohol effects, which characterizes the risk of failure of compensatory mechanisms under stress.

5. The drunk driving prevention and early warning system integrating AI algorithms according to claim 1, characterized in that, The generation of personalized early warning and intervention plans includes: Based on the aforementioned hidden risk indicators, drivers are classified into levels of compensatory strength and potential vulnerabilities, and intervention priority sequences are formulated according to the current compensatory cautious operation mode. By integrating the potential vulnerability to alcohol-related effects and the intervention priority sequence, an analysis of the early warning triggering timing and intensity level is conducted to construct a personalized early warning and intervention plan. The personalized early warning and intervention plan includes voice prompt parameters and vehicle auxiliary control parameters.

6. The drunk driving prevention and early warning system integrating AI algorithms according to claim 5, characterized in that, The classification of drivers' compensation strength levels and potential vulnerabilities based on the aforementioned hidden risk indicators includes: The hidden risk indicators are classified into three levels of compensation intensity: if the hidden risk indicator exceeds the first intensity threshold, it is classified as high-intensity compensation level; if the hidden risk indicator is between the second intensity threshold and the first intensity threshold, it is classified as medium-intensity compensation level; and if the hidden risk indicator is below the second intensity threshold, it is classified as low-intensity compensation level. Based on the driver's historical response patterns, the hidden risk indicators are personalized and modified to obtain the modified hidden risk indicators. The modified hidden risk indicators are sorted from high to low, and the behavioral points corresponding to the top N indicators are selected as potential vulnerability points and their intensity levels are marked.

7. The drunk driving prevention and early warning system integrating AI algorithms according to claim 1, characterized in that, The personalized early warning intervention scheme for preventing drunk driving includes: controlling voice prompts, executing vehicle auxiliary controls, and adjusting warnings based on road conditions.

8. The drunk driving prevention and early warning system integrating AI algorithms according to claim 7, characterized in that, The control of voice prompts includes: Define the range of intonation variations and interval repetition rules for voice prompts; Based on the voice prompt parameters in the personalized early warning and intervention plan, the current risk level is matched with the prompt template; the matched prompt template is decomposed into progressive information units, transitioning from general reminder units to specific risk units; Each information unit is played sequentially, and the driver's feedback response is monitored during playback. If the feedback response shows signs of being ignored, an emphasis unit is inserted.

9. The drunk driving prevention and early warning system integrating AI algorithms according to claim 7, characterized in that, The execution of the vehicle auxiliary control includes: Set the activation threshold and duration limit for vehicle assistance controls; Based on the vehicle auxiliary control parameters in the personalized early warning and intervention plan, identify the matching degree between the target control type and the current vehicle state; The target control type is decomposed into auxiliary sequence steps, starting from the speed limiting step and extending to the steering assist step; each auxiliary sequence step is activated sequentially, and the stability changes after each step is activated are monitored. When the stability improvement reaches the preset standard, the auxiliary sequence steps are gradually deactivated to restore driver control.

10. The drunk driving prevention and early warning system integrating AI algorithms according to claim 7, characterized in that, The road condition-adaptive early warning adjustment includes: Detect current road conditions for vehicles, including traffic density, road surface type, and weather conditions; Based on the aforementioned road condition factors, assess the exposure risk and intervention suitability of the compensation behavior, and adjust the early warning parameters based on the exposure risk and intervention suitability. When high-density traffic conditions are detected, increase the frequency of voice prompts and the depth of vehicle assistance control; When a low-risk road surface type is detected, the warning intensity is reduced to minimize unnecessary intervention and avoid driver fatigue, thus achieving a preventive warning against drunk driving.

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