Driver state monitoring method and vehicle

Abstract

The invention relates to a driver state monitoring method and a vehicle, and belongs to the technical field of intelligent cabins. The method comprises the steps of firstly obtaining physiological features of a driver, and judging whether the state of the driver is an abnormal state based on the physiological features of the driver; if it is judged that the state of the driver is the abnormal state, obtaining a driving task of the driver and road information; judging whether the driving task executed by the driver in the abnormal state under the road information meets a preset safe driving condition or not; if yes, finally determining that the state of the driver is a safe state; and if not, finally determining that the state of the driver is a dangerous state. Through organic combination of the individual state of the driver and the actual driving scene information, the state of the driver is judged more comprehensively, the accuracy and reliability of the judgment result of the state of the driver are improved, interference of wrong early warning on the driver is avoided, and therefore driving safety is more effectively guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of smart cockpits, and more particularly to a driver status monitoring method and vehicle. Background Technology

[0002] As the executor of the driving task, the driver's physiological state and behavior directly affect driving safety. Any fatigue, distraction, or other dangerous behavior can cause traffic accidents and result in serious consequences.

[0003] Currently, some vehicles are equipped with driver status warning functions, which can promptly issue warnings when they detect that the driver is in a state of fatigue, distraction, or engaging in behaviors such as making phone calls, smoking, drinking water, or frequently turning their head, thereby reducing the risk of traffic accidents caused by these behaviors.

[0004] However, existing methods for judging driver status rely on a single dimension, resulting in inaccurate judgments. Summary of the Invention

[0005] To address the issue of low accuracy in driver status assessment in existing technologies, this application provides a driver status monitoring method and vehicle that improves the accuracy of driver status assessment by integrating driving tasks and road information.

[0006] Firstly, a method for monitoring driver status is provided, comprising the following steps: Acquire the driver's physiological characteristics, and based on these characteristics, determine whether the driver's state is abnormal. If the driver's status is determined to be abnormal, then the driver's driving task and road information are obtained. Determine whether the driver's performance of a driving task under abnormal conditions meets the preset safe driving conditions; If the conditions are met, the driver's condition is ultimately determined to be safe. If the conditions are not met, the driver's condition will ultimately be determined to be dangerous.

[0007] In this embodiment, the driver's state is first preliminarily judged based on the driver's physiological characteristics. After the driver's state is determined to be abnormal, the current driving task and road information are then obtained to assess whether the driver's current abnormal state meets the preset safe driving conditions when performing the current driving task under the current road information, so as to obtain the final judgment result. By organically combining the driver's individual state with the actual driving scenario information, the driver's state is judged more comprehensively. This not only effectively solves the problem of normal driving operations being misjudged as dangerous behavior in real road information, but also improves the accuracy and reliability of the driver state judgment result, avoids interference to the driver caused by false warnings, and thus more effectively ensures driving safety.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, determining whether the driver's state is abnormal based on the driver's physiological characteristics includes: Judging whether a driver is focused based on physiological characteristics; If so, the driver's condition is determined to be safe. If not, the driver's status is determined to be abnormal.

[0009] In this embodiment, a bifurcated processing strategy is adopted for the driver's state in a focused or unfocused state: when the driver is determined to be focused, the driver's state is directly determined to be safe; only when the driver is determined to be unfocused is subsequent complex scene fusion analysis initiated. This avoids unnecessary and computationally resource-intensive multi-dimensional evaluations when the driver is clearly focused, achieving reasonable allocation of computing resources, accelerating system response speed, and reducing unnecessary potential interference to the driver. It also avoids the current state being judged as dangerous due to dangerous driving behavior caused by the driver's own reasons. For example, if a driver is overtaking while focused, although the current scenario is very dangerous, the driver insists on overtaking. The driver's current state may not meet the preset safe driving conditions under the current road information, but at this time the driver clearly knows that he is performing a dangerous operation. It is obviously inappropriate to judge the driver's current focused state as dangerous.

[0010] In conjunction with the first aspect, in certain implementations of the first aspect, determining whether preset safe driving conditions are met includes: Determine whether the driving task is a straight-line driving task and whether the road traffic volume is less than or equal to a set threshold; If the driving task is a straight-line driving task and the road traffic volume is less than or equal to the set threshold, then the preset safe driving conditions are met.

[0011] In this embodiment, when the driver is not focused, if the driving task is a simple straight-line driving and the surrounding traffic flow is very small, the overall driving risk is still at a controllable low level. Setting this scenario as meeting the "safe driving conditions" reflects the concept of dynamic risk assessment, avoids triggering unnecessary alarms due to slight fluctuations in the driver's state in a safe environment, makes the system judgment more humane and more adaptable to the scenario, and improves the user experience.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, determining whether the preset safe driving conditions are met also includes: Determine whether the driving task is an overtaking task, whether the road traffic volume exceeds the set threshold, and whether the driver's head posture is turning left or right; If the driving task is an overtaking task, and the traffic volume on the road is greater than the set threshold and the driver's head posture is turning left or right, then the preset safe driving conditions are met.

[0013] In this embodiment, the preset safe driving conditions also include the driving task being an overtaking task, the road traffic volume being greater than a set threshold, and the driver's head posture being left or right turning. This can accurately distinguish between normal head turning behavior performed by the driver to observe the surrounding road conditions and abnormal head turning behavior caused by distraction or dangerous operation in the high-risk driving scenario of overtaking. This fundamentally solves the technical defect in the prior art that misjudges necessary safety observation actions during lane changing and overtaking as distraction or dangerous driving behavior, and significantly improves the accuracy and reliability of driver state judgment in complex traffic scenarios.

[0014] In conjunction with the first aspect, some implementations of the first aspect also include determining whether physiological characteristics are valid; If the obtained physiological characteristics are deemed invalid, a message will be displayed indicating that the driver's status cannot be determined. If the acquired physiological characteristics are deemed valid, then the driver's state is determined to be abnormal based on the valid physiological characteristics.

[0015] In this embodiment, an additional step is added to determine the validity of physiological characteristics, thus preventing inaccurate assessments of the driver's state due to factors such as eye obstruction. When the acquired physiological characteristics are deemed invalid, the driver is notified that a driver state assessment cannot be performed. This allows for timely feedback to the driver on their current state when the system lacks accurate identification capabilities, preventing blind assessments based on missing information and improving the safety and transparency of the assessment process. Simultaneously, the system guides the driver to remove obstructions to restore normal state monitoring, ensuring the stable and reliable operation of the driver state assessment function.

[0016] In conjunction with the first aspect, in certain implementations of the first aspect, determining whether physiological characteristics are valid includes: Determine if the driver's eyes are obstructed; If so, the obtained physiological characteristics are deemed invalid; If not, the obtained physiological characteristics are deemed valid.

[0017] In this embodiment, the validity of the acquired physiological features is determined by whether the driver's eyes are obstructed. When the eyes are obstructed, the physiological features are deemed invalid; when the eyes are not obstructed, the physiological features are deemed valid and subsequent status judgments continue. This method can promptly identify invalid judgment conditions when key features such as the driver wearing sunglasses with low light transmittance, hats obstructing the eyes, or bangs obscuring the eyes are missing. This avoids misjudgments or omissions in status judgments due to incomplete collection of key facial features, thereby improving the stability and robustness of the entire driver status judgment process.

[0018] In conjunction with the first aspect, in certain implementations of the first aspect, the driver's driving task is obtained, including: Acquire driver's driving operations and vehicle operating parameters; If the driver's driving operation and vehicle operating parameters meet the first preset conditions, then the driver's driving task is determined to be a straight-line driving task. If the driver's driving operation and vehicle operating parameters meet the second preset conditions, then the driver's driving task is determined to be an overtaking task.

[0019] In this embodiment, the driver's driving operations and vehicle operating parameters are acquired, and the acquired driver's driving operations and vehicle operating parameters are compared with preset conditions to analyze and obtain the current driving task. Compared with the method of manually setting or subjectively judging the driving task, the driving task obtained by analyzing the driver's driving operations and vehicle operating parameters can more realistically and in real time restore the current driving conditions and ensure the accuracy of the driving task analysis results.

[0020] In conjunction with the first aspect, in certain implementations of the first aspect, determining whether the first preset condition is met includes: Determine whether the steering wheel angle is less than the first threshold, whether the vehicle speed is higher than the second threshold, and whether the driver has pressed the brake pedal; If the steering wheel angle is less than the first threshold and the vehicle speed is higher than the second threshold, or if the steering wheel angle is less than the first threshold, the vehicle speed is lower than the second threshold and the driver does not press the brake pedal, then the first preset condition is determined to be met.

[0021] In this embodiment, by using steering wheel angle, vehicle speed, and brake pedal depressing status as the judgment criteria, and employing a multi-dimensional, scenario-based combination judgment logic, it is possible to accurately identify the vehicle's straight-line driving condition, effectively distinguishing straight-line driving from other driving states such as turning and low-speed braking, thus providing a reliable basis for the accurate determination of vehicle driving tasks.

[0022] In conjunction with the first aspect, in certain implementations of the first aspect, determining whether the second preset condition is met includes: Determine whether the steering wheel angle is greater than or equal to the first threshold and less than or equal to the third threshold, and whether the vehicle acceleration is greater than or equal to the fourth threshold and less than or equal to the fifth threshold; If the steering wheel angle is greater than or equal to the first threshold and less than or equal to the third threshold, and the vehicle acceleration is greater than or equal to the fourth threshold and less than or equal to the fifth threshold, then the second preset condition is satisfied.

[0023] In this embodiment, by using dual-range threshold constraints of steering wheel angle and vehicle acceleration, overtaking condition features are matched to quickly and reliably identify vehicle overtaking driving tasks, effectively distinguish similar driving conditions, and thus analyze the vehicle's driving tasks.

[0024] Secondly, a vehicle, the vehicle including Memory, which stores executable program code; The processor is used to call and run executable program code from memory, enabling the vehicle to perform the driver state monitoring method of the first aspect.

[0025] In this embodiment, the vehicle has the ability to autonomously and in real-time monitor and judge the driver's status. This enables the vehicle to dynamically collect multi-source data such as driver actions, driving operations, and road information during driving, and to complete status judgment and warning according to preset logic. This does not require reliance on external equipment or platforms, and the driver status judgment technology can be transformed into actual safety functions of the vehicle, effectively improving the vehicle's active safety performance. It can provide timely warnings of dangerous driver conditions, reduce the risk of traffic accidents, and provide strong technical support for the intelligent and safe upgrade of vehicles.

[0026] The beneficial effects of the technical solutions provided in this application include at least the following: The driver status monitoring method and vehicle provided in this application improve the accuracy of driver status judgment and reduce frequent false alarms by judging the driver's status by combining the driver's physiological characteristics with the current driving task and road information.

[0027] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious, specific embodiments of this application are given below. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the driver status monitoring method according to an embodiment of this application.

[0030] Figure 2 This is a schematic diagram of the driver status determination system according to an embodiment of this application.

[0031] Figure 3 This is a schematic diagram of the vehicle architecture according to an embodiment of this application. Detailed Implementation

[0032] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0033] The prefixes such as "first" and "second" used in this application embodiment are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this application embodiment does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not constitute unnecessary restrictions due to the use of such prefixes. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0034] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0035] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0036] Driving task, road information, and driver state are important factors affecting driving hazard. The level of driving hazard varies with changes in these key factors. Specifically, when the driver state and road information are consistent, performing different driving tasks will lead to differences in the degree of driving hazard; when the driver state and driving task are consistent, different road information will lead to differences in the degree of driving hazard; and when road information and driving task are consistent, different driver state will also lead to differences in the degree of driving hazard.

[0037] Driving tasks encompass a series of operational behaviors and decision-making activities that a driver must complete during vehicle operation, directly related to vehicle control and driving objectives. These tasks are objectively existent, their specific content determined by the driving scenario and objectives, and do not change due to the driver's subjective perception. Driving tasks primarily include basic vehicle control, such as maintaining lane position and stable speed, overtaking, changing lanes, and navigating intersections. Different driving tasks inherently differ in operational complexity, required attention intensity, and potential risk exposure. For example, driving straight at a constant speed on a closed highway and making a left turn at a busy pedestrian intersection in a busy urban area present vastly different objective challenges and levels of danger.

[0038] Road information encompasses all objective elements related to the road itself and the surrounding traffic environment that a driver can perceive during driving. Road information is an objectively existing category, and its types and states are determined by actual road conditions and the traffic environment, unaffected by the driver's subjective judgment. Road information includes the condition of the road's infrastructure, such as lane layout, road surface quality, and curve design; traffic control facilities, such as traffic lights, signs, markings, and lighting conditions; real-time traffic flow status, such as traffic volume, speed distribution, and the behavior of surrounding vehicles; and environmental conditions such as weather, visibility, and temporary construction zones or accident scenes are also crucial road information. These elements collectively define the objective risk level of a road or scenario; the level of danger for the same vehicle driving on a wet, dark, and sharply curved mountain road differs from that on a dry, bright, and straight urban road.

[0039] Driver state is a comprehensive manifestation of a driver's physiological and psychological state during driving. It falls under the subjective category and influences a driver's ability to perceive the environment, understand information, make judgments, and ultimately execute actions, directly impacting driving safety. Specific manifestations of driver state include physiological fatigue levels, concentration levels, and emotional fluctuations. Notably, in this embodiment, driver state also includes the driver's state when performing dangerous actions. When performing dangerous actions, it is difficult for a driver to maintain focused driving.

[0040] Since driving tasks and road information are relatively objective, observable, or pre-programmable, while the driver's state is intrinsic, dynamic, and subjective, accurate judgment and intervention regarding this subjective factor of driver state are particularly important for driving safety. Currently, this judgment is mainly achieved through technical means and behavioral monitoring. For example, in-vehicle sensors are used to monitor the driver's fatigue signs (such as eyelid closure frequency and head posture), signs of inattention (such as the duration of gaze deviating from the road), and operational compliance (such as sudden changes in steering and braking behavior). Combined with artificial intelligence algorithms, abnormal states are identified and warned of. Thus, when the driver's subjective ability declines, system prompts, assisted interventions, or even autonomous driving takeover can be used to compensate for the driver's deficiencies, maintain the overall safety balance of the "human-vehicle-road" system, and prevent risks caused by fluctuations in the driver's subjective state.

[0041] However, camera-based driver assessment technologies have significant drawbacks. They rely solely on facial features, head and hand movements, failing to consider crucial contextual information such as vehicle operating conditions and driving maneuvers. This leads to a one-sided interpretation of driver behavior, easily misjudging normal driving actions as dangerous ones. For example, during overtaking, to ensure lane change safety, drivers frequently check their side mirrors to assess the position and speed of vehicles behind and to the side. During this process, drivers will noticeably turn their heads left and right. While this head movement resembles head movements defined as distraction or dangerous driving in existing technologies, considering the driver's actual driving maneuvers (such as using turn signals, lightly pressing the accelerator / brake, and minor steering wheel adjustments) and the driving conditions, this head movement is a necessary safety maneuver during overtaking and constitutes normal driving behavior, not a dangerous one. Similarly, certain head movements and hand actions by drivers when crossing intersections or avoiding obstacles are also easily misjudged by existing technologies.

[0042] Based on the above application scenarios, this application proposes a driver state monitoring method. By organically combining the individual driver state with actual driving scenario information, the driver state can be judged more comprehensively, improving the accuracy and reliability of the driver state judgment results, avoiding interference caused by false warnings to the driver, and thus more effectively ensuring driving safety.

[0043] Figure 1 This is a schematic flowchart illustrating a driver state monitoring method provided in this embodiment. The driver state monitoring method includes the following steps: S1. Obtain the driver's physiological characteristics; S2. Based on the driver's physiological characteristics, determine whether the driver's state is abnormal; S3. If the driver's status is determined to be abnormal, then obtain the driver's driving task and road information. S4. Determine whether the driver meets the preset safe driving conditions when performing the current driving task under the current abnormal state and current road information. S5. If satisfied, the driver's condition is ultimately determined to be safe. S6. If the condition is not met, the driver's condition will be determined to be dangerous.

[0044] In this embodiment, an initial judgment of the driver's state is made based on the driver's physiological characteristics. Then, a second judgment is made to determine whether the driver's current state in the current road information meets preset safe driving conditions, resulting in a final judgment. This organically combines the driver's individual state with actual driving scenario information for a more comprehensive assessment of the driver's state. This effectively solves the problem of normal driving operations being misjudged as dangerous behavior in real-world road information, improving the accuracy and reliability of driver state judgment results, avoiding interference from false warnings, and thus more effectively ensuring driving safety.

[0045] It should be noted that in this application, a safe state means that the driver is in a state where the risk of driving is low and the probability of an accident is low; a dangerous state means that the driver is in a state where the risk of driving is high and the probability of an accident is high. However, a driver being in a safe state does not mean that an accident will not occur; similarly, a driver being in a dangerous state does not mean that an accident will necessarily occur.

[0046] In step S1, the driver's physiological characteristics include at least facial features, head posture, and hand movements. By observing the driver's facial features, head posture, and hand movements, it can be determined whether the driver is in a normal state of focused driving, or in an abnormal state such as fatigue, distraction, or performing dangerous actions.

[0047] In practical applications, drivers may wear sunglasses or hats while driving, and some drivers may have long bangs. When the light transmittance of sunglasses is less than 15%, hats cover the eyes, bangs cover the eyes, or there is heavy eyelash makeup, the acquisition of the driver's facial features through cameras and other devices may result in inaccurate physiological characteristics or failure to effectively identify the driver's state, leading to incorrect judgment of the driver's state or an inability to judge the driver's state.

[0048] Based on this, in this embodiment of the application, the validity of the obtained physiological characteristics is judged to ensure the accuracy of the physiological characteristics, thereby ensuring the accuracy of the judgment result when judging the driver's state based on the physiological characteristics.

[0049] When the obtained physiological features are deemed valid, step S2 is then performed to avoid inaccurate acquisition of physiological features due to factors such as the driver's eyes being obscured or other factors. This also avoids making blind judgments when key features are missing, thereby preventing misjudgments or omissions due to incomplete facial feature collection.

[0050] When the acquired physiological characteristics are deemed invalid, a prompt is displayed indicating that driver status cannot be determined. This allows the system to promptly inform the driver of their current status when it lacks accurate identification capabilities, preventing the system from making blind judgments due to missing information. This improves the safety and transparency of the judgment process and guides the driver to remove obstructions to restore normal status monitoring, ensuring the stable and reliable operation of the driver status judgment function.

[0051] In some embodiments, determining whether a physiological characteristic is valid includes: Determine if the driver's eyes are obstructed; If so, the obtained physiological characteristics are deemed invalid; If not, the obtained physiological characteristics are deemed valid.

[0052] Furthermore, determining whether the driver's eyes are obstructed includes: Extract the driver's eye features from the facial features, and determine whether the eye features include occlusion features. If so, determine that the driver's eyes are occluded.

[0053] In step S2, based on the driver's physiological characteristics, the driver's state is categorized into a normal state and an abnormal state. A normal state refers to a driver's state that meets the relevant regulations regarding safe driving; driving in a normal state poses a lower risk and a lower probability of an accident. An abnormal state refers to a driver's state that does not meet the relevant regulations regarding safe driving; driving in an abnormal state poses a higher risk and a higher probability of an accident. In simpler terms, a normal state represents a driver's safe driving condition, while an abnormal state represents a driver's potential for dangerous driving.

[0054] It should be noted that in the embodiments of this application, the normal state is necessarily a safe state, but the abnormal state is not necessarily a dangerous state, and may also be a safe state.

[0055] In some embodiments, the normal state typically includes a focused state, which refers to the combined physiological and psychological state of a driver who, during driving, is able to clearly grasp key aspects such as their own driving operations, vehicle conditions, and road information. In a focused state, a driver's ability to perceive the environment, understand information, make judgments, and execute actions is at a stable and efficient level, and they have a clear subjective understanding of driving-related factors.

[0056] It's important to note that a state of focused attention does not directly equate to low driving risk. While a driver is clearly aware of their driving actions, vehicle condition, and road information when focused, the risk of driving is still influenced by factors such as the driving task and road conditions. Therefore, the risk of driving while focused may not necessarily be minimal. For example, if a driver is focused on overtaking, and given the current road conditions, the overtaking maneuver is highly dangerous, but the driver insists on doing so, judging the driver's state based solely on whether preset safe driving conditions are met might classify it as dangerous. However, since the driver is clearly aware of the danger they are undertaking, classifying their focused state as dangerous is clearly inappropriate.

[0057] Based on this, in this embodiment of the application, when it is determined that the driver is in a focused state, the driver's state is determined to be a safe state, without the need for a second judgment. This not only saves the judgment process and avoids the waste of resources, but also avoids misjudgment due to the driver's subjective operation.

[0058] In some embodiments, abnormal states typically include inattentive states, which refer to a state in which the driver's subjective control over driving behavior is weak due to problems such as physiological fatigue, distraction, or performing dangerous behaviors.

[0059] It should be noted that while driving, a driver may be focused on using their mobile phone. In this case, the driver is focused on the phone, but not on the road ahead. Therefore, focus is relative to a specific object. In this embodiment, the states of focus and non-focus refer to the degree of focus on the road ahead.

[0060] When not focused, a driver's perception, judgment, and operational abilities are affected to varying degrees. Relying solely on individual behavioral characteristics such as facial features, head and hand movements is insufficient to accurately determine the safety of their state. It is necessary to combine driving tasks (such as overtaking, driving in a straight line) and road information (such as traffic flow, road conditions, and environmental conditions) for a scenario-based integrated judgment. Only by assessing the driving hazards in this state can a reasonable final state judgment result be obtained.

[0061] In some embodiments of this application, the unfocused state includes a fatigued state, a distracted state, and a dangerous behavior state.

[0062] Fatigue status is primarily assessed by monitoring the driver's eye-opening and closing status. A driver's closed-eye state is typically defined as sleep, and the duration of eye closure is used to determine the sleep level; the longer the eyes are closed, the deeper the sleep and the greater the risk to driving safety. Simultaneously, typical fatigue behaviors such as drowsy nodding and yawning are detected to aid in assessing the driver's condition. Fatigue is mainly determined by monitoring whether the driver's eyes are closed, combined with other typical fatigue behaviors. After feature extraction and algorithmic analysis of real-time images, key facial features and head posture angles are obtained. Eye position is used to determine if the eyes are closed, mouth position is used to determine if yawning is occurring (mouth opening / closing), and head posture angle is used to determine if the driver is drooping, thus determining whether the driver is fatigued.

[0063] It should be noted that, based on the physiological and behavioral characteristics exhibited by drivers during driving, fatigue can be divided into three levels: mild fatigue, moderate fatigue, and severe fatigue. Mild fatigue is characterized by brief eyelid drooping, occasional yawning, or slight head shaking, but the driver can still maintain basic driving focus and operational coordination. Moderate fatigue is characterized by more frequent eye closure, a persistent feeling of heaviness in the eyelids, obvious head nodding, and a decrease in reaction speed. At this point, the driver's attention has begun to wander, and operations may become sluggish. Severe fatigue is characterized by prolonged eye closure, uncontrollable drowsiness and nodding, blurred vision, or complete inability to remain awake. The driver almost loses effective control of the vehicle, which can easily lead to serious traffic accidents.

[0064] Distracted driving refers to a driver's gaze deviating from the road ahead and their lack of focus on driving. Distracted driving is primarily determined by monitoring the driver's gaze direction; typically, a deviated gaze is accompanied by a change in head posture. By extracting features from real-time images and analyzing the algorithm, the vector value of the driver's gaze direction and the head's posture angle relative to the camera are obtained to determine whether the driver's gaze has deviated from the road ahead.

[0065] The head's attitude angles relative to the camera include yaw, roll, and pitch. A spatial Cartesian coordinate system is established with the camera as the reference. The pitch angle represents the pitch angle around the X-axis, the roll angle represents the longitudinal roll angle around the Y-axis, and the yaw angle represents the yaw angle around the Z-axis.

[0066] In some embodiments, when the pitch angle is less than -5° or greater than 15°, or the yaw angle is greater than ±15°, or the roll angle is greater than ±15°, it is considered that the driver's head is deviating from the normal driving position, resulting in a lack of focus on the road ahead.

[0067] Distraction states are further divided into long-term distraction and short-term distraction. Long-term distraction refers to a single, prolonged period in which the driver's gaze deviates from the road ahead, while short-term distraction refers to multiple short-term deviations from the road ahead within a specific time window, with the cumulative duration of the deviation exceeding a certain threshold.

[0068] In some embodiments, prolonged distraction refers to a single instance where the driver's gaze deviates from the road ahead for more than 3 seconds; short-term distraction refers to multiple instances within a 30-second time window where the driver's gaze deviates from the road ahead for a cumulative duration exceeding 10 seconds, with each instance of deviation lasting no more than 3 seconds.

[0069] Dangerous behavior refers to a driver engaging in a dangerous act that threatens driving safety. Common dangerous behaviors include making phone calls, smoking, and drinking water. The identification of dangerous behaviors is prior art in this field and will not be elaborated upon further.

[0070] Dangerous driving behaviors also include drivers turning their heads left or right. Turning heads left or right is a typical action easily misjudged. In normal driving, when changing lanes, drivers usually look left and right in their rearview mirrors to observe the situation of vehicles behind them and seek a suitable opportunity to change lanes; this is a necessary observation behavior for safe overtaking. However, turning heads left or right can also be an action taken when the driver is distracted.

[0071] When a driver is fatigued, if they attempt to overtake in a situation with traffic volume exceeding a set threshold, the driving risk is already high. The fatigue further exacerbates this risk, making the driver's fatigue state a dangerous condition in this scenario. However, if the driver is driving in a straight line in a situation with traffic volume less than or equal to the set threshold, the driving risk is lower. Therefore, although the driver is fatigued, this fatigue state can still be considered a safe condition.

[0072] It should be noted that traffic volume exceeding the set threshold indicates high traffic volume on the road, while traffic volume less than or equal to the set threshold indicates low traffic volume.

[0073] It should also be noted that while this application classifies fatigue as a safe state in certain driving scenarios, it does not encourage or advocate for drivers to drive while fatigued. In practical applications, drivers may become fatigued due to the inability to rest in time, such as in traffic jams in city centers or while driving on highways.

[0074] Similarly, when a driver turns their head left or right, if the driver is overtaking, the purpose of turning their head left or right is to check the traffic flow in the adjacent lane in order to find a suitable opportunity to overtake. At this time, the driver has a clear understanding of their own behavior. If the road information is not suitable for overtaking, that is, the driver judges the driving behavior to be more dangerous based on the driving task and road information, but obviously, the driver turning their head left or right is not a dangerous behavior, but a safe behavior.

[0075] In step S3, when the driver's state is determined to be abnormal, it indicates that the driver may be in danger. However, due to the complexity of the actual driving process, the driver's physiological characteristics alone cannot fully evaluate the driver's state. For example, in overtaking situations, to ensure lane change safety, the driver needs to frequently observe the left and right rearview mirrors to check the driving positions and speeds of vehicles behind and to the sides. During this process, the driver will make obvious left and right head-turning movements. From the perspective of head behavior characteristics, the driver may be in an abnormal state. However, combined with the driver's actual driving operations (such as using turn signals, lightly pressing the accelerator / brake, and making minor steering wheel adjustments) and the current driving conditions, it can be seen that this head-turning action is a necessary safety operation during overtaking and belongs to normal driving behavior, not dangerous behavior. Therefore, when the driver's state is determined to be abnormal, the driver's driving task and road information are obtained to further determine whether the driver's state is dangerous.

[0076] In some embodiments, determining whether the driver's state is abnormal includes: Identify whether a driver is attentive based on physiological characteristics; If so, the driver's condition is determined to be safe. If not, the driver's status is determined to be abnormal.

[0077] When not focused, drivers have less subjective control, and the safety and risk of their behavior are highly dependent on the driving task and road information. Therefore, it is necessary to make further judgments based on the driving task and road information to determine the final state.

[0078] Based on this, in this application, when it is determined that the driver is in a state of inattentiveness, the driver's state is further judged by combining the driving task and road information.

[0079] In this embodiment, a bifurcation processing strategy is adopted for the driver's state in a focused state and an unfocused state: the focused state is directly determined to be safe, and only the unfocused state is subjected to subsequent complex scene fusion analysis. This avoids unnecessary and computationally intensive multi-dimensional evaluation when the driver is obviously focused, realizes reasonable allocation of computing resources, speeds up the system response speed, and also reduces unnecessary potential interference to the driver, avoiding dangerous driving behavior caused by the driver's own reasons that leads to the state being judged as dangerous.

[0080] In step S3, there are many ways to obtain driving tasks and road information, such as obtaining them from the cloud or the internet, or manually inputting them by the driver.

[0081] In this embodiment, the driver's driving tasks are obtained by acquiring the driver's driving operations and vehicle operating parameters, and then analyzed based on these operations and parameters. Driving operations and vehicle operating parameters directly reflect the driver's intentions, such as acceleration and lane-changing maneuvers during overtaking, and constant speed and straight-line driving status. Compared to manually setting or subjectively judging driving tasks, analyzing driving tasks by acquiring driver operations and vehicle operating parameters provides a more realistic and real-time reconstruction of driving conditions, ensuring the accuracy of the driving task analysis results.

[0082] In some embodiments, obtaining the driver's driving task includes: Acquire driver's driving operations and vehicle operating parameters; If the driver's driving operation and vehicle operating parameters meet the first preset conditions, then the driver's driving task is determined to be a straight-line driving task. If the driver's driving operation and vehicle operating parameters meet the second preset conditions, then the driver's driving task is determined to be an overtaking task.

[0083] By acquiring the driver's driving operations and vehicle operating parameters, and comparing these parameters with preset conditions, the current driving task can be analyzed. Compared to manually setting or subjectively judging driving tasks, the driving task obtained through analysis of the driver's driving operations and vehicle operating parameters can more realistically and in real time restore the current driving conditions, ensuring the accuracy of the driving task analysis results.

[0084] In this embodiment of the application, the vehicle operating parameters include vehicle gear, steering wheel angle, vehicle speed / acceleration, reversing, and whether assisted driving is activated.

[0085] In practical applications, when the speed is low and the steering wheel angle is increased, the driver is most likely turning; when the steering wheel angle is increased and the vehicle acceleration is increased, the driver is most likely overtaking.

[0086] The first preset conditions include: the steering wheel angle is less than the first threshold and the vehicle speed is higher than the second threshold, or the steering wheel angle is less than the first threshold, the vehicle speed is lower than the second threshold and the driver has not pressed the brake pedal.

[0087] In some embodiments, the first threshold is 15° and the second threshold is 30 km / h; that is, when the steering wheel angle is less than 15° and the vehicle speed is higher than 30 km / h, or when the steering wheel angle is less than 15°, the vehicle speed is lower than 30 km / h and the driver does not press the brake pedal, it is determined that the driver is performing a straight-line driving task.

[0088] The second preset condition includes: the steering wheel angle is greater than or equal to the first threshold and less than or equal to the third threshold, and the vehicle acceleration is greater than or equal to the fourth threshold and less than or equal to the fifth threshold. In some embodiments, the third threshold is 45° and the fourth threshold is 1 m / s. 2 The fifth threshold is 3 m / s 2 That is, the steering wheel angle is between 15-45°, and the acceleration is 1m / s². 2 -3m / s 2 At that time, it was determined that the driver was performing an overtaking task.

[0089] Furthermore, if the driver's driving operation and vehicle operating parameters meet the third preset condition, then the driver's driving task is determined to be a steering task.

[0090] The third preset conditions include: the steering wheel angle is greater than or equal to the third threshold, and the vehicle speed is lower than the sixth threshold.

[0091] In some embodiments, the third threshold is 45° and the sixth threshold is 20km / h; that is, when the vehicle speed is below 20km / h and the steering wheel angle is greater than or equal to 45°, it is determined that the driver is performing a steering task.

[0092] It should be noted that driving tasks include, but are not limited to, turning, overtaking, and driving in a straight line; driving in a straight line does not mean that the vehicle travels in a straight line in the narrow sense, but rather that the vehicle moves forward in the lane. During the driving process, the steering wheel may be adjusted slightly, but the vehicle's trajectory remains within the lane.

[0093] In some embodiments of this application, safe driving conditions include: the driving task is an overtaking task, the road traffic volume is greater than a set threshold, and the driver's head posture is turning left or right.

[0094] In high-risk conditions such as overtaking and heavy traffic, the introduction of head posture (whether it involves turning the head left or right) as a key criterion can effectively distinguish between normal driving behavior—actively observing road conditions—and abnormal actions caused by distraction or dangerous behavior. When the driver turns their head left or right to observe surrounding vehicles, the system classifies it as a safe state, avoiding misjudging reasonable and necessary driving observations as dangerous states. This significantly reduces the misjudgment rate in complex driving scenarios and improves the accuracy, reliability, and intelligence of driver state judgment.

[0095] When the driver turns their head left or right, if the driving task is a straight-line driving task and the road traffic volume is greater than a set threshold, the driver's state is ultimately determined to be dangerous; if the driving task is a straight-line driving task and the road traffic volume is less than or equal to the set threshold, the driver's state is ultimately determined to be safe.

[0096] In other embodiments of this application, the safe driving conditions further include: the driving task is a straight-line driving task, and the road traffic volume is less than or equal to a set threshold.

[0097] For the relatively stable and low-load driving scenario of straight-line driving, a different judgment logic is set up compared to the overtaking scenario. When the driving task is straight-line driving and the road traffic volume is less than or equal to a set threshold, the driver's state is ultimately judged as safe. This setting can dynamically adjust the judgment criteria according to the degree of danger of the driving scenario, avoiding misjudgments due to slight deviations in movement or brief fluctuations in attention in low-risk driving environments, reducing unnecessary warning interference, making the driver state judgment more consistent with actual driving scenarios, and improving the rationality, adaptability, and user acceptance of the system's judgment.

[0098] In other embodiments of this application, safe driving conditions are used to characterize the maximum risk that a driver is allowed to take when performing a driving task under current road information.

[0099] By assessing the danger of a driver performing a driving task on the road in their current state, and determining whether the danger meets the conditions for safe driving, a secondary assessment of the driver's condition is made.

[0100] In this embodiment, for ease of description, driving tasks and road information are collectively referred to as driving scenarios. The level of risk associated with driving behavior varies across different driving scenarios. For example, the driving hazard of overtaking is generally higher than that of maintaining a straight-line driving position on the road. In practical applications, overtaking is not prohibited simply because it is relatively more dangerous; instead, drivers are chosen to overtake in scenarios or at times with relatively lower risks. Therefore, the assessment of driving hazard is scenario-dependent; the judgment criteria are the same for the same driving scenario, but different for different driving scenarios.

[0101] Based on this, in this application, corresponding safe driving conditions are set according to the driving task and road information; when the driver's risk in the driving scenario does not meet the safe driving conditions, it indicates that the driver's driving risk is greater than the maximum risk and the probability of an accident is relatively high; when the driver's risk in the driving scenario meets the safe driving conditions, it indicates that the driver's driving risk is less than or equal to the maximum risk and the probability of an accident is relatively low.

[0102] If the danger level does not meet the conditions for safe driving, the driver's condition is ultimately determined to be dangerous; if the danger level meets the conditions for safe driving, the driver's condition is ultimately determined to be safe.

[0103] In this embodiment, by setting corresponding safe driving conditions based on driving tasks and road information, different threshold standards are established for different driving tasks and road information. This allows the driver state monitoring method to accurately match the safety requirements of different operating conditions such as overtaking and straight driving, making the judgment logic more rigorous and the judgment results more objective. This ensures more accurate warnings of dangerous states and more reasonable judgments of safe states. Safe driving conditions characterize the maximum risk allowed for a driver to perform a driving task under the current road information. By using whether the risk exceeds the threshold as the basis for the final judgment of the driver's state, a standardized and quantifiable judgment criterion is provided for the final judgment of the driver's state, avoiding subjectivity and ambiguity in the judgment process.

[0104] In some embodiments of this application, the criteria for determining when a vehicle changes lanes to overtake on a road with traffic volume exceeding a set threshold are as follows: the distance between the vehicle and the vehicle in front in the lane it is about to enter is a first set value, and the distance between the vehicle and the vehicle behind in the lane it is about to enter is a second set value. If the distance between the vehicle and the vehicle in front in the lane it is about to enter is greater than the first set value, and the distance between the vehicle and the vehicle behind in the lane it is about to enter is greater than the second set value, then the risk is relatively low; if the distance between the vehicle and the vehicle in front in the lane it is about to enter is less than the first set value, or the distance between the vehicle and the vehicle behind in the lane it is about to enter is less than the second set value, then the risk is relatively high.

[0105] In other embodiments of this application, the magnitude of the hazard is quantified to increase the accuracy of the hazard assessment results.

[0106] Specifically, the driver's initial assessment state is represented by a score, and the driving task and road information are also represented by scores. Different weights are assigned to the driver's initial assessment state, driving task, and road information. A hazard score is calculated for the driver during driving, and this hazard score is compared with the safe driving conditions determined based on the driving task and road information to ultimately determine the driver's state. Taking an initial assessment of mild fatigue, a straight-line driving task, and clear weather, highway, and low traffic volume (no vehicles in adjacent lanes) as an example, the final driver state determination method is described. The weights for driver state, driving task, and road information are 0.4, 0.3, and 0.3 respectively.

[0107] The final driver status assessment method is as follows: the driver is initially assessed as being in a state of mild fatigue, with a driver status score of 75 points; the driving task is straight driving, with a driving task complexity score of 10 points; the road information is clear weather, highway, and low traffic volume (no vehicles in adjacent lanes), with a road information complexity score of 20 points; the hazard score is 0.4×75+0.3×10+0.3×20=39 points, and the hazard-safe driving condition of the scenario is set at 70 points. Since 39 points < 70 points, the driver status is ultimately determined to be safe.

[0108] Taking an initial assessment that the driver is slightly fatigued, the driving task is changed to overtaking, and the road information is clear weather, an urban expressway, and heavy traffic (two vehicles in adjacent lanes, with close spacing) as an example, this paper introduces the final driver status determination method. The driver status has a weight of 0.4, the driving task has a weight of 0.3, and the road information has a weight of 0.3.

[0109] The final driver status assessment method is as follows: the driver is initially assessed as being in a state of mild fatigue, with a driver status score of 70 points; the driving task changes to overtaking, with a driving task complexity score of 80 points; the road information is clear weather, an urban expressway, and heavy traffic, with a road information complexity score of 75 points; the hazard score = 0.4 × 75 + 0.3 × 80 + 0.3 × 75 = 76.5 points, the scenario's hazard-safe driving conditions are set at 40 points, and since 76.5 points > 40 points, the driver status is ultimately determined to be in a dangerous state.

[0110] It should be noted that the methods for determining safe driving conditions are conventional techniques in this field and will not be elaborated here.

[0111] It should also be noted that the safe driving conditions are the same in the same driving scenario and do not change due to different driver states. This ensures the consistency of driver state monitoring methods and avoids arbitrary adjustments to judgment criteria due to differences in individual driver states (such as fatigue, distraction, etc.). This ensures that the state judgment of all drivers in the same scenario is based on a unified risk measurement benchmark, eliminating subjectivity and standard imbalance in the judgment process. It ensures that the judgment logic and risk assessment scale are consistent for drivers in different states in the same driving scenario. This not only improves the objectivity and credibility of the judgment results, but also makes the entire judgment method more standardized and repeatable, providing a consistent and reliable decision-making basis for subsequent safety prompts and interventions.

[0112] It should be noted that in this embodiment, when the driver's state is ultimately determined to be dangerous, a safety warning is given to the driver. The warning can be given via a pop-up window on the instrument panel to alert the user, or via voice announcement or alarm sound, or simultaneously via voice announcement and pop-up window on the instrument panel.

[0113] It should also be noted that when the vehicle is in assisted driving mode, the autonomous driving system will take over some driving tasks, which will slightly reduce the driver's level of concentration required. The sensitivity of the driver status monitoring can be adjusted according to the actual situation to reduce the driving annoyance caused by frequent announcements to the user. This will not be elaborated on here.

[0114] This application provides a driver status judgment system. This system can realize multi-dimensional data fusion and collaborative decision-making of driver's individual status, driving task and road information. It integrates the scattered information collection and analysis functions, ensures efficient data interaction of each module and orderly execution of judgment logic, and enables the system to have accurate judgment capabilities in complex scenarios.

[0115] Figure 2 The diagram shows the structure of the driver state assessment system. The system includes a detection unit, a driving task analysis unit, a road condition perception unit, and a fusion decision unit. The detection unit acquires the driver's facial features, head posture, and hand movements, and analyzes these features to determine the driver's initial state. The driving task analysis unit acquires the driver's driving operations and vehicle operating parameters, and analyzes these parameters to determine the driver's driving task. The road condition perception unit acquires road information and traffic conditions. The fusion decision unit is connected to the detection unit, driving task analysis unit, and road condition perception unit. The fusion decision unit is configured to: assess the risk of the driver's driving based on the initial driver state assessment result, combined with vehicle operating conditions, road information, and traffic flow conditions, and ultimately determine the driver's state based on the risk assessment result.

[0116] The driver status judgment system in this embodiment of the application sets up four functional modules: a detection unit, a driving task analysis unit, a road condition perception unit, and a fusion decision unit. The detection unit completes the collection and initial judgment of the driver's action characteristics, the driving task analysis unit and the road condition perception unit provide objective data on driving conditions and road information, respectively, and the fusion decision unit performs risk assessment and final judgment based on multi-source data.

[0117] In this embodiment, the detection unit includes an image acquisition module, an image processing module, a feature extraction module, and a detection algorithm module. The image acquisition module is used to acquire facial images of the driver. The image processing module is connected to the image acquisition module and is used to process the images acquired by the image acquisition module. The feature extraction module is connected to the image processing module and is used to extract key facial features of the driver from the images processed by the image processing module. The detection algorithm module is connected to the feature extraction module and is used to analyze and determine the driver's fatigue, distraction, and other state information based on the information extracted by the feature extraction module, and transmit this information to the fusion decision unit.

[0118] In some embodiments, the image acquisition module is a camera in the driver's cab. The image acquisition module, image processing module, feature extraction module, and detection algorithm module are existing technologies in the field and will not be described in detail here.

[0119] In this embodiment, the driving task analysis unit includes a vehicle condition perception module and a vehicle condition analysis module. The vehicle condition perception module is used to collect information such as the vehicle's gear position, steering angle, driving speed and acceleration, reversing status, and the operating status of the driver assistance functions. The vehicle condition analysis module is connected to the vehicle condition perception module. The vehicle condition analysis module judges the driver's driving operation based on the information collected by the vehicle condition perception module, thereby analyzing the driving task and sending the analysis results to the fusion decision unit.

[0120] It should be noted that the vehicle condition perception module and the vehicle condition analysis module are existing technologies in this field and will not be described in detail here.

[0121] In this embodiment, the road condition perception unit includes a road condition perception module and a road condition analysis module. The road condition perception module is a surround-view camera, LiDAR, millimeter-wave radar, etc., used to collect real-time environmental information such as obstacles, lane lines, and the positions of surrounding vehicles on the road. The road condition analysis module is connected to the road condition perception module, and the road condition analysis module obtains road condition information based on the information collected by the road condition perception module and outputs it to the fusion decision unit.

[0122] It should be noted that the worse the weather, the lower the visibility, the more complex the road conditions, the more vehicles in the left and right lanes, the faster the speed, and the denser the surrounding pedestrians, the higher the level of driving danger and the higher the requirements for the driver's concentration. When visibility is high, the road is flat, there are few vehicles and pedestrians around, and the speed is slow, driving is relatively safe, and the requirements for the driver's concentration can be slightly reduced.

[0123] In this embodiment, the fusion decision unit includes a decision module and an information processing module. The decision module receives three types of perception data from the driver, vehicle, and road, performs comprehensive analysis and decision-making, and generates the final state judgment result. The information processing module is connected to the decision module and performs standardized processing on the judgment result generated by the decision module.

[0124] In some embodiments, the decision module stores a model. The model, based on a pre-learned driver state monitoring method, integrates signals of different modalities and results uploaded by the detection unit, driving task analysis unit, and road condition perception unit to make a decision and output a comprehensive analysis result of the driver state. Based on this comprehensive analysis result, it determines whether a pop-up window and a warning to the user are needed.

[0125] In this embodiment of the application, the driver status judgment system also includes a management unit. The management unit receives information sent by the information processing module. After receiving the information, the management unit will execute status response actions on the one hand, record status history information on the other hand, and transmit warning signals, prompt texts, voice commands, etc. to terminal output devices such as instrument pop-up reminders, speaker alarms, and voice broadcasts through the message distribution function, so as to realize real-time reminders and interventions for the driver.

[0126] In some embodiments, the management unit includes a response module, a storage module, and a distribution module. The response module is used to perform status response actions; the storage module is used to record historical status information; and the distribution module is used to distribute messages.

[0127] The workflow of the driver status assessment system is as follows: First, the image acquisition module continuously captures facial images of the driver and transmits the raw image data to the image processing module for preprocessing. Then, the feature extraction module locates and extracts key facial feature points (such as eye and mouth contours) and head posture angles from the processed images. The detection algorithm module then uses a preset algorithm model to analyze whether the driver is closing their eyes, yawning, having their gaze deviated, or holding objects, based on these feature information. This allows for an initial judgment as to whether the driver is in a focused state or in a non-focused state such as fatigue, distraction, or dangerous behavior, and the initial judgment result is output.

[0128] Meanwhile, the vehicle condition perception module collects real-time operating parameters such as vehicle gear position, steering wheel angle, vehicle speed / acceleration, reversing signal, and the status of driver assistance functions. Based on these dynamic parameters, the vehicle condition analysis module analyzes the driver's intentions through acceleration, lane changes, and other driving maneuvers to determine the ongoing driving task (such as overtaking, straight-line driving, or turning), and sends the task analysis results out. The road condition perception unit works synchronously, using surround-view cameras, LiDAR, millimeter-wave radar, and other autonomous driving sensors, combined with high-precision map data, to collect and process real-time environmental information such as lane lines, obstacles, the position and distance of surrounding vehicles, weather conditions, and traffic flow, generating a comprehensive road information report.

[0129] Then, the decision-making module receives three types of data: the initial judgment of the driver's state from the detection unit, the driving task from the driving task analysis unit, and the road information from the road condition perception unit. Based on a preset risk assessment model and safe driving conditions that match the driving scenario, it quantitatively assesses and comprehensively analyzes the danger of the driver's driving under the given task and road information, generating the final driver state judgment result. The information processing module standardizes and encapsulates this judgment result and sends it to the management unit.

[0130] Finally, after receiving the result, the management unit records it as status history data and executes the corresponding status response based on the judgment result: if the status is determined to be dangerous, it displays a warning message in the dashboard pop-up window and plays an alarm sound or voice prompt through the vehicle speaker through the message distribution function, thereby completing the safety warning and intervention for the driver.

[0131] This application also provides a computer-readable storage medium storing program code that is executed by one or more processors. When the program code runs on the processor, it causes a device including one or more processors to perform the driver state monitoring method described in the above embodiments. The processor running this computer-readable storage medium can be installed in a vehicle system.

[0132] It should be understood that when the modules or units described herein are implemented using software, they can be implemented in whole or in part as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, they produce, in whole or in part, the processes or functions according to the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transferred from one computer-readable storage medium to another. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0133] This application provides a vehicle with autonomous, real-time driver status monitoring and judgment capabilities. During operation, the vehicle dynamically collects multi-source data, including driver actions, driving operations, and road information, and performs status judgment and early warning according to preset logic. This eliminates reliance on external devices or platforms and transforms driver status judgment technology into actual vehicle safety functions, effectively improving the vehicle's active safety performance. It provides timely warnings of dangerous driver conditions, reduces the risk of traffic accidents, and offers strong technical support for the intelligent and safe upgrade of vehicles. For example, see Figure 3 The vehicle includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it enables the processor to implement the driver state monitoring method described in the above embodiments.

[0134] The vehicle integrates driver status monitoring methods with the vehicle itself, enabling the vehicle to autonomously and in real-time monitor and judge driver status. During driving, the vehicle dynamically collects multi-source data such as driver actions, driving operations, and road information, and completes status judgment and warning according to preset logic without relying on external equipment or platforms. Moreover, the driver status judgment technology can be transformed into actual safety functions of the vehicle, effectively improving the vehicle's active safety performance. It can promptly warn of dangerous driver conditions, reduce the risk of traffic accidents, and provide strong technical support for the intelligent and safe upgrade of vehicles.

[0135] Those skilled in the art will recognize that the modules, units, and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring driver status, characterized in that, Includes the following steps: Acquire the driver's physiological characteristics, and based on the driver's physiological characteristics, determine whether the driver's state is abnormal; If the driver's state is determined to be abnormal, then the driver's driving task and road information are obtained. Determine whether the driver performing the driving task under the road information in an abnormal state meets the preset safe driving conditions; If the conditions are met, the driver's condition is ultimately determined to be safe. If the conditions are not met, the driver's condition will ultimately be determined to be dangerous.

2. The driver status monitoring method according to claim 1, characterized in that, Determining whether the driver's state is abnormal includes: Determine whether the driver is in a state of focus based on the aforementioned physiological characteristics; If so, the driver's state is determined to be normal. If not, the driver's state is determined to be abnormal.

3. The driver status monitoring method according to claim 1, characterized in that, Determining whether the preset safe driving conditions are met includes: Determine whether the driving task is a straight-line driving task and whether the road traffic volume is less than or equal to a set threshold. If the driving task is a straight-line driving task and the road traffic volume is less than or equal to a set threshold, then the preset safe driving conditions are met.

4. The driver status monitoring method according to claim 1, characterized in that, Determining whether the preset safe driving conditions are met further includes: Determine whether the driving task is an overtaking task, whether the traffic flow on the road is greater than the set threshold, and whether the driver's head posture is turning left or right; If the driving task is an overtaking task, and the traffic volume on the road is greater than the set threshold and the driver's head posture is turning left or right, then the preset safe driving conditions are met.

5. The driver status monitoring method according to claim 1, characterized in that, It also includes determining whether the physiological characteristics are valid; If the obtained physiological characteristics are deemed invalid, a message will be displayed indicating that the driver's status cannot be determined. If the acquired physiological characteristics are determined to be valid, then a determination is made as to whether the driver's state is abnormal based on the valid physiological characteristics.

6. The driver status monitoring method according to claim 5, characterized in that, Determining whether the physiological characteristics are valid includes: Determine whether the driver's eyes are obstructed; If so, the obtained physiological characteristics are deemed invalid; If not, the obtained physiological characteristics are deemed valid.

7. The driver status monitoring method according to claim 1, characterized in that, Obtaining the driver's driving task includes: Obtain the driver's driving operations and vehicle operating parameters; If the driver's driving operation and vehicle operating parameters meet the first preset conditions, then the driver's driving task is determined to be a straight-line driving task. If the driver's driving operation and vehicle operating parameters meet the second preset conditions, then the driver's driving task is determined to be an overtaking task.

8. The driver status monitoring method according to claim 7, characterized in that, Determining whether the first preset condition is met includes: Determine whether the steering wheel angle is less than the first threshold, whether the vehicle speed is higher than the second threshold, and whether the driver has pressed the brake pedal; If the steering wheel angle is less than the first threshold and the vehicle speed is higher than the second threshold, or if the steering wheel angle is less than the first threshold, the vehicle speed is lower than the second threshold and the driver does not press the brake pedal, then the first preset condition is determined to be met.

9. The driver status monitoring method according to claim 7 or 8, characterized in that, Determining whether the second preset condition is met includes: Determine whether the steering wheel angle is greater than or equal to a first threshold and less than or equal to a third threshold, and whether the vehicle acceleration is greater than or equal to a fourth threshold and less than or equal to a fifth threshold; If the steering wheel angle is greater than or equal to the first threshold and less than or equal to the third threshold, and the vehicle acceleration is greater than or equal to the fourth threshold and less than or equal to the fifth threshold, then the second preset condition is satisfied.

10. A vehicle, characterized in that, include: Memory, which stores executable program code; A processor is configured to call and run the executable program code from the memory, causing the vehicle to perform the driver state monitoring method as described in any one of claims 1 to 9.

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