Risk identification method and system for operating vehicle, and storage medium

Abstract

The invention provides a risk identification method and system for an operating vehicle and a storage medium, and relates to the technical field of automobile information processing, and the method comprises the steps: obtaining a real-time collection image through connection with an image collection device; a standard driving posture is introduced, images collected in real time are compared, and risk behavior characteristics are recognized; based on the risk behavior characteristics, risk levels are evaluated, and a risk response mechanism is set; driving control parameters of the target operation vehicle are introduced; identifying potential risk indicators according to the driving control parameters; based on a risk response mechanism, a multi-response mechanism is set in combination with a potential risk index, and different levels of safety intervention measures are triggered hierarchically. The technical problem that the real-time performance and accuracy of risk identification are insufficient due to lack of comprehensive integration and grading processing of driving risk information and a multi-level risk response mechanism in the prior art is solved, the real-time performance and the risk response speed of operating vehicle risk identification are improved, and the safety of the driving process is enhanced.

Description

Technical Field

[0001] The present application relates to the field of automobile information processing technology, and in particular to a risk identification method, system and storage medium for operating vehicles. Background Art

[0002] With the rapid development of the transportation industry, operating vehicles play an important role in logistics and public transportation, and their operating safety is directly related to social and economic benefits. In order to ensure the safe operation of operating vehicles, driving monitoring technology has gradually been widely used. These monitoring systems are based on sensor technology, Internet of Things and other technologies to collect driver behavior data (such as fatigue driving, smoking, using mobile phones, etc.) and vehicle operation status data (such as speed, steering angle, braking status, etc.) in real time to preliminarily identify and evaluate driving risks. However, these methods or systems usually rely on a single data source or limited parameters for risk analysis. For example, some methods only focus on abnormal changes in vehicle operation status (such as sudden acceleration and deceleration), while ignoring the direct impact of driver subjective behavior on risk; other methods simply rely on the identification of driver behavior and fail to combine vehicle operation status for comprehensive evaluation. In addition, in terms of risk feedback mechanism, existing technologies mostly use fixed rules to trigger reminders and lack dynamic graded response capabilities based on risk levels. These deficiencies make it difficult for the current system to achieve comprehensive integration and in-depth analysis of risk information, and cannot fully cope with multi-dimensional risk sources in complex driving environments, thereby limiting the timeliness and effectiveness of risk control. Summary of the invention

[0003] The present application provides a risk identification method, system and storage medium for operating vehicles, which solves the technical problem that the existing technology lacks comprehensive integration and hierarchical processing of driving risk information and a multi-level risk response mechanism, resulting in insufficient real-time and accuracy of risk identification, affecting the timeliness and effectiveness of risk control, and achieves the technical effect of improving the real-time nature of risk identification of operating vehicles and the risk response speed, thereby enhancing the safety of the driving process of operating vehicles.

[0004] In view of the above problems, on the one hand, the present application provides a risk identification method for operating vehicles, the method comprising: connecting an image acquisition device to obtain real-time acquired images, the real-time acquired images being the driving position monitoring information of the target operating vehicle; introducing a standard driving posture, comparing the real-time acquired images, and identifying risk behavior characteristics; based on the risk behavior characteristics, assessing the risk level and setting a risk response mechanism; introducing driving control parameters of the target operating vehicle, the driving control parameters including vehicle speed, steering wheel angle, and accelerator pedal opening; identifying potential risk indicators based on the driving control parameters; based on the risk response mechanism and in combination with the potential risk indicators, setting a multiple response mechanism, the multiple response mechanism being used to trigger different levels of safety intervention measures in a hierarchical manner.

[0005] On the other hand, the present application also provides a risk identification system for operating vehicles, the system comprising: an image acquisition module, used to connect to an image acquisition device to acquire real-time acquired images, wherein the real-time acquired images are driving position monitoring information of a target operating vehicle; a risk behavior identification module, used to introduce a standard driving posture, compare the real-time acquired images, and identify risk behavior characteristics; a risk assessment module, used to assess the risk level and set a risk response mechanism based on the risk behavior characteristics; a driving control parameter acquisition module, used to introduce the driving control parameters of the target operating vehicle, wherein the driving control parameters include vehicle speed, steering wheel angle, and accelerator pedal opening; a potential risk identification module, used to identify potential risk indicators based on the driving control parameters; a multiple response module, used to set a multiple response mechanism based on the risk response mechanism and in combination with the potential risk indicators, wherein the multiple response mechanism is used to trigger different levels of safety intervention measures in a hierarchical manner.

[0006] In a third aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned risk identification method for operating vehicles.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the driver's seat monitoring information of the target operating vehicle and comparing it with the standard driving posture, identifying risk behavior characteristics, possible dangerous behaviors such as fatigue driving or distracted driving can be discovered in time, and risk behavior characteristics can be determined. Based on the identified risk behavior characteristics, the risk level is assessed and the corresponding risk response mechanism is set, so that the risk control measures can be dynamically adjusted according to the real-time monitoring data to achieve timely response and effective control of risks. The introduction of driving control parameters and the identification of potential risk indicators further enhance the accuracy and comprehensiveness of risk assessment. Finally, based on the risk response mechanism and potential risk indicators, a multiple response mechanism is set to trigger different levels of safety intervention measures in a hierarchical manner. Through dynamic and multi-level risk response, the timeliness and effectiveness of risk control are improved, thereby enhancing the safety of the driving process of operating vehicles.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a risk identification method for operating vehicles provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a risk identification system for operating vehicles provided in an embodiment of the present application.

[0012] Explanation of reference numerals: image acquisition module 10 , risk behavior identification module 20 , risk assessment module 30 , driving control parameter acquisition module 40 , potential risk identification module 50 , multiple response module 60 . DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a risk identification method, system and storage medium for operating vehicles, thereby solving the technical problems in the prior art that the real-time and accuracy of risk identification are insufficient, affecting the timeliness and effectiveness of risk control due to the lack of comprehensive integration and hierarchical processing of driving risk information and a multi-level risk response mechanism. The present application achieves the technical effect of improving the real-time nature of risk identification of operating vehicles and the risk response speed, thereby enhancing the safety of the driving process of operating vehicles.

[0014] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a risk identification method for operating a vehicle, the method comprising:

[0015] Step S1: connecting an image acquisition device to obtain real-time acquired images, wherein the real-time acquired images are driving position monitoring information of a target operating vehicle.

[0016] Specifically, the image acquisition device is a hardware device used to capture visual information in real time, such as a vehicle-mounted camera. The target operating vehicle can be any vehicle that needs to be identified for risk, such as a taxi, online ride-hailing car, etc.

[0017] The image acquisition device is installed in the cab of the target operating vehicle and can cover the driver's face and upper body to capture key information such as the driver's facial expressions, head postures, and hand movements. The image acquisition device is connected to obtain the image stream captured by the image acquisition device in real time. These images contain instant visual information of the driver's seat for subsequent risk behavior analysis.

[0018] Step S2: introducing a standard driving posture, comparing the real-time collected images, and identifying risk behavior characteristics.

[0019] Specifically, the standard driving posture is a predefined driving posture that is considered safe and normative, including the correct seat position, hand position, sight direction, etc. Risk behavior features refer to the identification of possible driver misbehaviors (such as fatigue, yawning, smoking, talking on the phone, etc.) from images, which may affect driving safety.

[0020] The standard driving posture is introduced as a reference model, and the computer vision algorithm is used to detect the driver's head position, eye closure degree, facial expression, etc. from the real-time collected images, and matched and compared with the standard driving posture to identify possible risk behavior characteristics, such as whether the driver is tired, distracted, or performing other unsafe operations. Through this comparison, the driver's abnormal behavior can be discovered in time, providing a basis for subsequent risk assessment.

[0021] Step S3: Based on the risk behavior characteristics, assess the risk level and set up a risk response mechanism.

[0022] Specifically, the risk level is the risk level divided according to the severity of the risk behavior characteristics, such as low, medium, and high levels. The risk response mechanism refers to the treatment plans and measures set for different risk levels, such as warnings, interventions, or emergency treatments.

[0023] The risk level is assessed based on the risk behavior characteristics identified from the real-time collected images. The risk level assessment can be performed based on the custom preset risk rules through statistical analysis of the risk behavior characteristics. For example, different risk levels correspond to the range of the number of alarms in the same time; or different risk levels are assigned according to the duration of the dangerous behavior. For example, if the driver is slightly fatigued in a short period of time, it is assessed as low risk; if the driver's eyes are closed for a long time and there is obvious head deviation, it is assessed as high risk. Different risk response mechanisms are set according to different risk levels, such as giving voice reminders when the risk is low, starting automatic intervention (such as deceleration) when the risk is medium, and triggering emergency measures when the risk is high.

[0024] Through the assessment of risk levels and the setting of risk response mechanisms, risk behaviors are divided into detailed categories, and the intervention measures to be taken are determined according to the urgency and intensity of the risk behaviors, providing a decision-making basis for risk response.

[0025] Step S4: introducing the driving control parameters of the target operating vehicle, wherein the driving control parameters include vehicle speed, steering wheel angle, and accelerator pedal opening.

[0026] Specifically, driving control parameters include vehicle speed, steering wheel angle, accelerator pedal opening, etc. These parameters reflect the driver's control behavior of the vehicle and the real-time operation status of the vehicle. The interactive vehicle control system collects the vehicle's driving control parameters, including vehicle speed, steering wheel angle, accelerator pedal opening, etc., through the built-in positioning system of the target vehicle and sensors such as gyroscopes. By analyzing these parameters, abnormal modes in vehicle operation can be identified, such as sudden acceleration, sudden deceleration, or unstable steering wheel operation.

[0027] The introduction of driving control parameters of target operating vehicles supplements the driver behavior analysis data. Combining the analysis of driver behavior and vehicle operating status can more accurately identify potential risk situations, provide more comprehensive risk assessment results, and provide richer data support for risk response.

[0028] Step S5: Identify potential risk indicators based on the driving control parameters.

[0029] Specifically, potential risk indicators are indicators that may indicate risks, such as speeding and sharp turns, based on the analysis of driving control parameters. Vehicle speed, steering wheel angle, accelerator pedal opening and other parameters in driving control parameters are monitored and analyzed in real time to identify possible potential risks. For example, if the vehicle suddenly accelerates at a certain moment and the steering wheel angle changes greatly, it means that there may be high-risk behaviors such as sharp turns and sudden braking. By comparing these parameters, direct evidence of the vehicle's operating status can be provided for risk assessment, and a more comprehensive understanding of the current driving environment and potential risks can be obtained.

[0030] Step S6: Based on the risk response mechanism and in combination with the potential risk indicators, a multiple response mechanism is set, and the multiple response mechanism is used to trigger different levels of security intervention measures in a hierarchical manner.

[0031] Specifically, multiple response mechanisms are set up based on risk response mechanisms and potential risk indicators, that is, the specific response measures to be taken are determined based on the risk level and the severity of the risk indicators. For example, for low-risk situations, the system will issue a warning or reminder; for medium-risk situations, the vehicle's automatic deceleration function will be activated; and for high-risk situations, the emergency braking system will be activated or emergency services will be contacted. Through this dynamic, multi-level response mechanism, it is possible to respond flexibly according to actual conditions and take timely measures to reduce risks, thereby more effectively managing and mitigating risks, protecting the safety of drivers and vehicles, and improving driving safety.

[0032] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include immediate reminder measures; when the risk level is at a low level, a corresponding text reminder is generated according to the immediate reminder measures and then converted into a voice reminder to the driver of the target operating vehicle.

[0033] Specifically, when a low-level risk is detected (such as a slight driver distraction or a slight deviation from the standard driving posture), the driver is informed of the potential risk through immediate reminder measures. The reminder is in the form of text information, which is converted into voice through speech synthesis technology and played through the car speakers to increase the driver's awareness of risks. For example, if it is detected that the driver has not paid attention to the road for a long time, a voice reminder of "Please pay attention and stay alert" can be issued.

[0034] The system alerts drivers to potential risks through non-intrusive instant voice reminders, assisting them to adjust their driving behavior in a timely manner before the risks escalate. It also avoids direct intervention in driving operations and maintains driving smoothness.

[0035] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include immediate intervention measures; when at a medium risk level, the automatic deceleration and obstacle avoidance function of the target operating vehicle is activated according to the immediate intervention measures.

[0036] Specifically, when the risk level is assessed as medium (for example, the vehicle is about to enter a more dangerous turn or approach an obstacle), not only will a reminder be issued, but control measures will also be taken to prevent the risk from escalating. For example, the vehicle's automatic deceleration or obstacle avoidance function will be activated to automatically adjust the vehicle's speed or driving trajectory to ensure that the vehicle is in a safe state. Intervention measures are more proactive than simple reminders and can provide additional safety protection when the driver fails to respond to the reminder in time.

[0037] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include emergency intervention measures; when at a high risk level, according to the emergency intervention measures, the emergency contact number is dialed and the collision avoidance system is activated.

[0038] Specifically, when a high level of risk is detected (such as the driver showing obvious fatigue driving, sharp turns, excessive speed and approaching a dangerous area), the most urgent intervention measures are automatically taken to directly trigger an emergency response. This includes calling the emergency contact number and activating the anti-collision system to ensure early rescue and avoid serious accidents. For example, when it is detected that the driver yawns multiple times in a row, closes his eyes, and the vehicle suddenly turns sharply, it is judged that the driver is in a state of severe fatigue. At this time, the preset emergency contact number is automatically dialed, and the anti-collision system is activated to intervene to prevent the vehicle from a serious collision. Through emergency intervention, the damage of the accident can be minimized and the safety of the driver and passengers can be protected.

[0039] This multi-level response mechanism ensures that appropriate measures can be taken at different levels of risk to improve driving safety.

[0040] Furthermore, step S5 of the embodiment of the present application includes:

[0041] Step S51: Setting a safety threshold range.

[0042] Step S52: Based on the driving control parameter, a difference calculation is performed through the safety threshold range to determine the degree of deviation.

[0043] Step S53: Determine a potential risk indicator based on the degree of deviation.

[0044] Specifically, the safety threshold range refers to the safety range of driving control parameters (such as vehicle speed, steering wheel angle, accelerator pedal opening, etc.) during driving. Driving behavior outside this range is considered a potential risk. The degree of deviation refers to the degree of deviation of the driving control parameters from the safety threshold range. The greater the degree of deviation, the more the driving behavior deviates from the standard and the higher the risk.

[0045] First, a set of safety standards is introduced based on the type of target operating vehicles and road driving conditions. These safety standards include the maximum safe speed of the vehicle, the reasonable range of steering wheel angles, the appropriate opening of the accelerator pedal and other driving control parameter ranges. A safety threshold range is set for each vehicle based on these safety standards. Then, the difference between the driving control parameters and the safety threshold range is calculated, the difference between each parameter and the safety standard is quantified, and the multiple calculated differences are de-dimensionalized to determine the degree of deviation.

[0046] Finally, based on the degree of deviation, further determine whether there is a potential risk. If the degree of deviation is large, the system will mark the behavior as a potential risk indicator and prepare to take corresponding intervention measures. For example, if the vehicle continues to exceed the speed limit or the steering wheel turns too far, these behaviors may cause an accident, so they will be marked as potential risk indicators, further triggering subsequent safety interventions.

[0047] Further, step S51 includes:

[0048] A driving safety standard is introduced, and the driving safety standard complies with the operating vehicle type limitation of the target operating vehicle; based on the driving safety standard, matching is performed under the constraints of road driving conditions to determine the safety threshold range.

[0049] Specifically, in order to ensure a more accurate formulation of the safety threshold range, it is necessary to introduce driving safety standards for different vehicle types and ensure that the safety threshold is applicable under road driving conditions. For example, according to traffic management regulations, determine the driving safety standards for different types of vehicles under different types of road conditions. Monitor vehicle driving environment information and road driving conditions (such as weather, road conditions, traffic flow, etc.) through on-board cameras, GPS, radar, etc., and match the corresponding safety threshold range according to driving safety standards.

[0050] The determination of the safety threshold range is the basis for risk assessment and provides a benchmark for subsequent deviation calculation and risk indicator determination. Matching the safety threshold range according to different vehicle types and driving environments ensures the accuracy and adaptability of risk assessment and improves the sensitivity of risk identification.

[0051] Further, step S53 includes:

[0052] Based on the degree of deviation, the behavioral state of the target operating vehicle is identified and valid information is screened; based on the valid information, the potential risk indicators are marked in comparison with preset risk scenarios, wherein the preset risk scenarios are used to characterize that the target operating vehicle and / or the driver of the target operating vehicle is in an abnormal driving state.

[0053] Specifically, the behavior state refers to the actual driving conditions of the target operating vehicle, including the comprehensive performance of driving control parameters such as speed, steering, and throttle operation. Effective information refers to data information in real-time collected images and driving control parameter information that deviates from safety standards.

[0054] In step S52, the degree of deviation is calculated and the behavior state of the target operating vehicle is identified by the degree of deviation. These reflect the actual driving operation of the driver, and the existence of dangerous behavior is determined by screening the effective information related to potential risks. These effective information are compared with the preset risk scenarios to further mark the potential risk indicators. The preset risk scenarios provide a set of standardized and known dangerous situations. When the behavior state matches these scenarios, it is considered that there is a potential risk. For example, the target operating vehicle accelerates continuously in a short period of time and the vehicle changes lanes frequently. These behaviors are matched with the preset rapid acceleration scenarios and frequent lane change scenarios, and are marked as potential risk indicators.

[0055] The above steps transform raw data into key information that can be used for risk assessment and compare it with predefined risk scenarios to more accurately identify behavioral patterns that may lead to risks and provide a basis for decision-making in risk response.

[0056] Furthermore, the preset risk scenarios include sudden acceleration scenarios, sudden deceleration scenarios, frequent lane change scenarios, sharp turn scenarios and blind spot braking scenarios corresponding to the target operating vehicles, and yawning scenarios, smoking scenarios, distraction scenarios and phone call scenarios corresponding to the drivers of the target operating vehicles.

[0057] Specifically, the preset risk scenarios cover the abnormal driving states that the target operating vehicle or the driver of the target operating vehicle may be in in most cases, including but not limited to sudden acceleration scenarios, sudden deceleration scenarios, frequent lane change scenarios, sharp turn scenarios and blind spot braking scenarios of the target operating vehicle, as well as yawning scenarios, smoking scenarios, distraction scenarios and phone call scenarios of the driver of the target operating vehicle, providing a reference evaluation framework for risk identification, improving the accuracy of risk identification, and also improving the accuracy of risk response.

[0058] In summary, the risk identification method for operating vehicles provided in the embodiments of the present application has the following technical effects:

[0059] Through the image acquisition device, real-time acquisition images are obtained, the situation of the driving position is monitored in real time, and subjective driving behavior information is obtained to provide basic data for subsequent driver behavior analysis. The real-time acquisition images are compared with the standard driving posture to detect abnormal driving behavior in real time. According to the identified risk behaviors, the risk level is evaluated, and refined risk assessment results are provided, which lays the foundation for the subsequent dynamic risk response mechanism. By real-time monitoring of the vehicle's operating status (such as speed, steering angle, throttle opening), dynamic driving risks are identified, and the driver's behavior and vehicle status are organically combined to improve the comprehensiveness of risk identification. In the process of monitoring the vehicle's operating status, the safety threshold is compared, the degree of deviation is calculated, and the accurate analysis of the vehicle's operating status is achieved. At the same time, by constructing specific driving risk scenarios (such as sudden acceleration and sharp turns) and behavioral risk scenarios (such as yawning and smoking), the risk characteristics are refined to further improve the accuracy of risk identification. Through comprehensive analysis of risk behaviors and vehicle status, a hierarchical dynamic response mechanism is constructed to trigger corresponding intervention measures for different risk levels. Low-level risks are mainly reminders, intermediate risks initiate automatic intervention (such as deceleration and obstacle avoidance), and high-level risks trigger emergency linkage (such as collision avoidance and emergency contact), achieving full-process safety protection from early warning to active control, and improving the pertinence and efficiency of risk control.

[0060] In general, the embodiments of the present application realize the real-time collection and comprehensive evaluation of multi-dimensional risk characteristics by combining driver behavior monitoring with vehicle operation status data. In the risk identification process, the introduction of standard driving posture comparison and vehicle control parameter linkage analysis enhances the comprehensiveness and accuracy of risk identification; in terms of risk response, through risk level assessment and multiple response mechanisms, safety intervention measures adapted to different risk levels are dynamically triggered, achieving full process coverage from early warning to emergency handling. This method can significantly improve the risk perception ability and risk response speed of operating vehicles, and provides an intelligent and efficient technical solution for driving safety.

[0061] Embodiment 2, as Figure 2 As shown, based on the same inventive concept of the risk identification method for operating vehicles in the aforementioned embodiment 1, the present application also provides a risk identification system for operating vehicles, the system comprising:

[0062] The image acquisition module 10 is used to connect to the image acquisition device to acquire real-time acquired images, where the real-time acquired images are the driver's seat monitoring information of the target operating vehicle.

[0063] The risk behavior identification module 20 is used to introduce a standard driving posture, compare the real-time collected images, and identify risk behavior characteristics.

[0064] The risk assessment module 30 is used to assess the risk level and set a risk response mechanism based on the risk behavior characteristics.

[0065] The driving control parameter acquisition module 40 is used to introduce the driving control parameters of the target operating vehicle, and the driving control parameters include vehicle speed, steering wheel angle, and accelerator pedal opening.

[0066] The potential risk identification module 50 is used to identify potential risk indicators according to the driving control parameters.

[0067] The multiple response module 60 is used to set a multiple response mechanism based on the risk response mechanism and in combination with the potential risk indicators. The multiple response mechanism is used to trigger different levels of security intervention measures in a hierarchical manner.

[0068] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include immediate reminder measures; when the risk level is at a low level, a corresponding text reminder is generated according to the immediate reminder measures and then converted into a voice reminder to the driver of the target operating vehicle.

[0069] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include immediate intervention measures; when at a medium risk level, the automatic deceleration and obstacle avoidance function of the target operating vehicle is activated according to the immediate intervention measures.

[0070] Furthermore, the safety intervention measures corresponding to the multiple response mechanisms described in the embodiment of the present application include emergency intervention measures; when at a high risk level, according to the emergency intervention measures, the emergency contact number is dialed and the collision avoidance system is activated.

[0071] Furthermore, the potential risk identification module 50 of the embodiment of the present application is also used to perform the following steps:

[0072] Set a safety threshold range; based on the driving control parameters, calculate the difference through the safety threshold range to determine the degree of deviation; based on the degree of deviation, determine the potential risk indicator

[0073] Furthermore, the potential risk identification module 50 of the embodiment of the present application is also used to perform the following steps:

[0074] A driving safety standard is introduced, and the driving safety standard complies with the operating vehicle type limitation of the target operating vehicle; based on the driving safety standard, matching is performed under the constraints of road driving conditions to determine the safety threshold range.

[0075] Furthermore, the potential risk identification module 50 of the embodiment of the present application is also used to perform the following steps:

[0076] Based on the degree of deviation, the behavioral state of the target operating vehicle is identified and valid information is screened; based on the valid information, the potential risk indicators are marked in comparison with preset risk scenarios, wherein the preset risk scenarios are used to characterize that the target operating vehicle and / or the driver of the target operating vehicle is in an abnormal driving state.

[0077] Furthermore, the preset risk scenarios include sudden acceleration scenarios, sudden deceleration scenarios, frequent lane change scenarios, sharp turn scenarios and blind spot braking scenarios corresponding to the target operating vehicles, and yawning scenarios, smoking scenarios, distraction scenarios and phone call scenarios corresponding to the drivers of the target operating vehicles.

[0078] Through the above detailed description of the risk identification method for operating vehicles in this specification, those skilled in the art can clearly understand the risk identification system for operating vehicles in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0079] Embodiment three, based on the same inventive concept of the risk identification method for operating vehicles in the aforementioned embodiment one, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various steps of the above-mentioned embodiment of the risk identification method for operating vehicles are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A risk identification method for operating vehicles, characterized in that: The method comprises: Connecting to an image acquisition device to obtain real-time acquired images, wherein the real-time acquired images are monitoring information of the driving position of the target operating vehicle; Introducing a standard driving posture, comparing the real-time collected images, and identifying risk behavior characteristics; Based on the risk behavior characteristics, assess the risk level and set up a risk response mechanism; Introducing driving control parameters of the target operating vehicle, wherein the driving control parameters include vehicle speed, steering wheel angle, and accelerator pedal opening; identifying potential risk indicators based on the driving control parameters; Based on the risk response mechanism and in combination with the potential risk indicators, a multiple response mechanism is set up, and the multiple response mechanism is used to trigger different levels of security intervention measures in a hierarchical manner.

2. The method according to claim 1, characterized in that The safety intervention measures corresponding to the multiple response mechanisms include immediate reminder measures; When the risk level is low, a corresponding text reminder is generated according to the immediate reminder measures and then converted into a voice reminder to the driver of the target operating vehicle.

3. The method according to claim 2, characterized in that The safety intervention measures corresponding to the multiple response mechanisms include immediate intervention measures; When at a medium risk level, the automatic deceleration and obstacle avoidance function of the target operating vehicle is activated according to the immediate intervention measures.

4. The method according to claim 3, characterized in that The safety intervention measures corresponding to the multiple response mechanisms include emergency intervention measures; When at a high risk level, emergency intervention measures are taken, emergency contact numbers are dialed, and the anti-collision system is activated.

5. The method according to claim 2, characterized in that According to the driving control parameters, identifying potential risk indicators, the method comprising: Set the safety threshold range; Based on the driving control parameter, performing difference calculation through the safety threshold range to determine the degree of deviation; Based on the degree of deviation, potential risk indicators are determined.

6. The method according to claim 5, characterized in that Setting a safety threshold range, the method comprising: Introducing driving safety standards, wherein the driving safety standards comply with the operating vehicle type restrictions of the target operating vehicles; Based on the driving safety standard, matching is performed under the constraints of road driving conditions to determine the safety threshold range.

7. The method according to claim 6, characterized in that Based on the degree of deviation, a potential risk indicator is determined, the method comprising: Based on the degree of deviation, identifying the behavior state of the target operating vehicle and filtering effective information; Based on the valid information, the potential risk indicator is marked in comparison with a preset risk scenario, wherein the preset risk scenario is used to characterize that the target operating vehicle and / or the driver of the target operating vehicle is in an abnormal driving state.

8. The method according to claim 7, characterized in that The preset risk scenarios include sudden acceleration scenarios, sudden deceleration scenarios, frequent lane change scenarios, sharp turn scenarios and blind spot braking scenarios corresponding to the target operating vehicles, and yawning scenarios, smoking scenarios, distraction scenarios and phone call scenarios corresponding to the drivers of the target operating vehicles.

9. A risk identification system for operating vehicles, characterized in that: The system is used to execute the risk identification system method for operating vehicles according to any one of claims 1 to 8, comprising: An image acquisition module is used to connect to an image acquisition device to acquire real-time acquired images, wherein the real-time acquired images are monitoring information of the driving position of the target operating vehicle; A risk behavior identification module, used to introduce a standard driving posture, compare the real-time collected images, and identify risk behavior characteristics; A risk assessment module, used to assess the risk level and set a risk response mechanism based on the risk behavior characteristics; A driving control parameter acquisition module, used to introduce the driving control parameters of the target operating vehicle, wherein the driving control parameters include vehicle speed, steering wheel angle, and accelerator pedal opening; A potential risk identification module, used to identify potential risk indicators according to the driving control parameters; The multiple response module is used to set up a multiple response mechanism based on the risk response mechanism and in combination with the potential risk indicators. The multiple response mechanism is used to trigger different levels of security intervention measures in a hierarchical manner.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the risk identification method for operating a vehicle as described in any one of claims 1 to 8 are implemented.

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