Vehicle multivariate risk perception and hierarchical automatic takeover system and method

Abstract

The invention discloses a vehicle multi-risk perception and hierarchical automatic takeover system and method, which can integrate multi-dimensional monitoring of vehicle state, driver state and environment information through a condition identification module, cover multiple dangerous scenes, break through the limitation of single function of the existing system, and effectively meet the safety protection requirement under a composite dangerous situation. Through accurate positioning of a tire burst position and a special dynamic control model, a customized stabilizing strategy is executed according to different tire burst conditions of left front tires, right front tires and the like, the out-of-control probability of the vehicle after tire burst is reduced, secondary accidents are reduced, and the emergency control stability is improved; a user-defined security policy is supported, and the system applicability is improved; the air bag is pre-started before collision to shorten the response time, the safety belt is automatically unlocked after collision, passive protection is converted into active front intervention, the passenger injury degree and the secondary disaster risk are effectively reduced, and passenger injury is reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety technology, specifically a vehicle multi-risk perception and graded automatic takeover system and method. Background Technology

[0002] Automotive safety systems are mainly divided into two aspects: active safety systems and passive safety systems. Simply put, active safety is about preventing accidents from happening; while passive safety is about protecting the occupants of the vehicle or other vehicles or pedestrians in the event of an accident.

[0003] Current vehicle safety systems primarily focus on single-function driver assistance or passive safety features, such as lane keeping and collision warning, lacking comprehensive judgment and intelligent takeover mechanisms for various complex and dangerous situations. Especially in complex hazardous scenarios such as tire blowouts, sudden driver illness, and extreme weather, existing systems cannot implement targeted and differentiated emergency driving strategies, nor can they achieve flexible safety strategy management with multiple conditions that can be manually configured. Therefore, this invention provides a vehicle multi-risk perception and graded automatic takeover system and method. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a vehicle multi-risk perception and graded automatic takeover system and method to solve the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a vehicle multi-risk perception and graded automatic takeover system, comprising:

[0006] The condition recognition module is used to monitor vehicle status, driver status and environmental information in real time, and identify multiple dangerous conditions including tire blowout, fatigued driving, lane departure, collision risk, pedestrian approach, misoperation, vehicle skidding, water damage, following too closely, risk of being rear-ended, fire and collision that has already occurred, and to assess the risk level of the identified dangerous conditions.

[0007] The strategy execution module executes corresponding graded driving takeover strategies based on the specific hazard type and its risk level identified.

[0008] The tire blowout differentiation processing module is used to identify the specific location of the blowout when it occurs, and to execute the stability control and driving strategy corresponding to that location.

[0009] The pre-safety protection module pre-activates the airbag inflation procedure when an impending collision is detected.

[0010] Preferably, the condition identification module includes: a multi-factor risk perception unit, a risk grading assessment unit, and a false alarm filtering unit; wherein, the multi-factor risk perception unit integrates vehicle status sensors, driver status monitoring sensors, and environmental perception sensors to monitor the hazardous conditions in real time; the risk grading assessment unit uses a multi-factor weighting algorithm to assess the risk level of the identified hazardous conditions, classifying them into Level 1 risk (minor), Level 2 risk (moderate), and Level 3 risk (severe); the false alarm filtering unit constructs a false alarm identification model through multi-sensor data fusion and time-series analysis to reduce the system's false trigger rate.

[0011] Preferably, the strategy execution module includes: a hierarchical response control unit, a multi-dimensional collaborative control unit, and an execution priority management unit; wherein, the hierarchical response control unit executes differentiated control strategies according to the risk level, with early warning reminders for level 1 risks, partial takeover for level 2 risks, and full takeover for level 3 risks; the multi-dimensional collaborative control unit integrates the steering system, braking system, power system, and stability control system to achieve three-dimensional collaborative control in the lateral, longitudinal, and vertical directions; the execution priority management unit is used to execute control strategies according to preset priority rules when multiple risk conditions coexist.

[0012] Preferably, the tire blowout differentiation processing module includes: a tire blowout location precision positioning unit and a location-specific control strategy library; wherein, the tire blowout location precision positioning unit accurately identifies tire blowout situations at four tire positions (left front, right front, left rear, and right rear) through a tire pressure sensor array; and the location-specific control strategy library stores dedicated vehicle dynamics control models established for different tire blowout locations.

[0013] Preferably, the pre-safety protection module includes a collision prediction unit and an airbag pre-deployment unit; wherein, the collision prediction unit predicts the probability of a collision using radar or camera sensors; and the airbag pre-deployment unit is used to initiate the airbag inflation procedure in advance when an unavoidable collision is predicted.

[0014] Preferably, the pre-safety protection module further includes a seat belt intelligent control unit, which is used to automatically unlock the seat belt buckle when the vehicle is detected to be on fire or a collision has occurred.

[0015] Preferably, it further includes: an intelligent warning module that activates the headlights to flash in advance when a collision risk is predicted; and the intelligent warning module includes a pre-collision warning unit, which is used to activate the headlights to flash in advance when a collision risk is predicted.

[0016] Preferably, it further includes: an emergency communication module, which automatically sends vehicle status information to the vehicle call platform after the vehicle is collided with or in the aforementioned dangerous situation; the emergency communication module consists of an accident detection unit, a multi-channel communication unit, and an information sending unit; wherein, the accident detection unit comprehensively determines that an accident has occurred by using acceleration sensors, collision sensors, and airbag trigger signals; the multi-channel communication unit is used to realize the effective transmission of emergency information; the information sending unit is used to automatically collect vehicle status data, accident scene pictures / videos, and GPS location information, and send them to the vehicle call platform.

[0017] Preferably, it also includes: a manual configuration interface that allows users to manually enable or disable specific takeover conditions according to different driving scenarios, and supports triggering by combination of conditions.

[0018] A method for vehicle multi-risk perception and hierarchical automatic takeover includes the following steps:

[0019] Step 1, Multi-faceted Risk Perception and Grading Assessment: Real-time data collection of vehicle status, driver status, and environmental perception is achieved using vehicle status sensors, driver status monitoring sensors, and environmental perception sensors. This process identifies multiple hazardous conditions, including tire blowout, driver fatigue, lane departure, collision risk, pedestrian approach, driver error, vehicle skidding, water damage, following too closely, rear-end collision risk, fire, and existing collisions. A risk grading assessment unit uses a multi-factor weighting algorithm to classify these hazardous conditions into three risk levels: Level 1 (minor), Level 2 (moderate), and Level 3 (severe). A false alarm filtering unit uses a false alarm identification model constructed through multi-sensor data fusion and time-series analysis to filter false alarms associated with these hazardous conditions.

[0020] Step 2, Execution of Tiered Response Strategy: Based on the risk level, a differentiated control strategy is executed through the tiered response control unit: a warning is issued to the driver for Level 1 risk; partial driver takeover is implemented for Level 2 risk, intervening in some vehicle control systems; full driver takeover is implemented for Level 3 risk, fully controlling the vehicle control system; the steering system, braking system, power system, and stability control system are integrated through the multi-dimensional collaborative control unit; when multiple risk conditions coexist, the corresponding control strategy is executed according to preset priority rules through the priority management unit.

[0021] Step 3, Tire Blowout Differentiation Processing: When a tire blowout hazard is detected, the tire blowout location precision positioning unit uses a tire pressure sensor array to accurately identify the blowout locations of the left front, right front, left rear, and right rear tires; it then calls up the dedicated vehicle dynamics control model corresponding to the blowout location in the location-specific control strategy library to execute stability control and driving strategies.

[0022] Step 4, Pre-safety protection activation: When a collision is predicted to occur by the collision prediction unit, the airbag inflation program is pre-activated; when an accident is detected, the emergency communication module automatically collects vehicle status data, accident scene pictures / videos, and GPS location information, and sends them to the vehicle call platform.

[0023] Step 5, User Interaction Configuration: Allows users to manually enable or disable specific takeover conditions according to different driving scenarios through the manual configuration interface, and supports condition combination trigger settings.

[0024] Beneficial effects

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] The condition recognition module integrates multi-dimensional monitoring of vehicle status, driver status, and environmental information, covering various hazardous scenarios and overcoming the limitations of existing single-function systems. It effectively addresses safety protection needs in complex hazardous situations. Through precise tire blowout location positioning and a dedicated dynamic control model, it implements customized stabilization strategies for different tire blowout situations, such as left front and right front, reducing the probability of vehicle loss of control after a blowout, minimizing secondary accidents, and improving emergency handling stability. It supports user-defined safety strategies, enhancing system applicability. Pre-collision airbag pre-deployment shortens response time, and automatic seatbelt unlocking after a collision transforms passive protection into proactive intervention, effectively reducing occupant injury and the risk of secondary disasters. Attached Figure Description

[0027] Figure 1 This is a three-dimensional structural diagram of a vehicle multi-risk perception and graded automatic takeover system.

[0028] Figure 2 This is a flowchart of the process for a vehicle multi-risk perception and graded automatic takeover method. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see Figure 1 This invention provides a vehicle multi-risk perception and graded automatic takeover system, comprising:

[0031] The condition recognition module is used to monitor vehicle status, driver status, and environmental information in real time, and to identify multiple dangerous conditions, including tire blowout, driver fatigue, lane departure, collision risk, pedestrian approach, misoperation, vehicle skidding, water damage, following too closely, risk of being rear-ended, fire, and collisions that have already occurred. It also assesses the risk level of the identified dangerous conditions. In other words, the condition recognition module can transmit precise trigger signals and risk level information to the subsequent hierarchical takeover execution module.

[0032] The strategy execution module executes corresponding graded driving takeover strategies based on the specific types of hazards and their risk levels, including but not limited to steering control, braking control, acceleration control, and stability control. In other words, under the action of the strategy execution module, the degree of takeover can be flexibly selected according to the differences in risk levels to control the vehicle status as quickly as possible and minimize the occurrence of accidents or the damage caused by accidents.

[0033] The tire blowout differentiation module identifies the specific location of a tire blowout and executes corresponding stability control and driving strategies. Specifically, it provides differentiated dynamic stability interventions for different blowout locations. For example, in a front tire blowout, the focus is on ensuring steering stability with light braking and a stable steering strategy; in a rear tire blowout, the focus is on preventing skidding and loss of control with heavy braking and a differential-limiting strategy; and in the case of a left or right tire blowout, the influence of road cross slope is considered, and asymmetrical braking force distribution is employed. This minimizes the risk of vehicle loss of control after a blowout and ensures the safety of passengers.

[0034] The pre-safety protection module pre-activates the airbag inflation program when an impending collision is detected. This allows the airbags to deploy rapidly at the moment of impact, providing timely cushioning for the head, chest, and other critical areas of the occupants and effectively reducing the severity of injury from the impact. Simultaneously, it can be linked to the pre-tensioning function of the seat belts to further stabilize the occupants' posture, reducing the risk of secondary collisions due to inertia. This comprehensively enhances the passive safety protection level during vehicle collisions, building a solid safety barrier for occupants.

[0035] Specifically, the condition recognition module includes: a multi-factor risk perception unit, a risk grading assessment unit, and a false alarm filtering unit. The multi-factor risk perception unit integrates vehicle status sensors, driver status monitoring sensors, and environmental perception sensors to monitor hazardous conditions in real time. The risk grading assessment unit uses a multi-factor weighting algorithm to assess the risk level of the identified hazardous conditions, classifying them into Level 1 risk (minor), Level 2 risk (moderate), and Level 3 risk (severe). The false alarm filtering unit constructs a false alarm recognition model through multi-sensor data fusion and time-series analysis to reduce the system's false trigger rate.

[0036] The effective combination of a multi-risk perception unit, a risk classification and assessment unit, and a false alarm filtering unit enables the comprehensive capture, scientific classification, and precise filtering of multiple risks during vehicle operation. This provides a reliable basis for decision-making by the subsequent graded automatic takeover unit. Furthermore, the synergistic effect of these three components forms the core capability of the condition recognition module, laying a solid foundation for the efficient operation of the system.

[0037] Specifically, the strategy execution module includes: a hierarchical response control unit, a multi-dimensional collaborative control unit, and an execution priority management unit. The hierarchical response control unit executes differentiated control strategies based on risk levels: Level 1 risks are addressed with early warnings, Level 2 risks are partially managed, and Level 3 risks are fully managed. The multi-dimensional collaborative control unit integrates the steering system, braking system, power system, and stability control system to achieve three-dimensional collaborative control in the lateral, longitudinal, and vertical directions. The execution priority management unit is used to execute control strategies according to preset priority rules when multiple risk conditions coexist.

[0038] Through the effective coordination of the hierarchical response control unit, the multi-dimensional collaborative control unit, and the execution priority management unit, it is possible to achieve precise and differentiated responses to risks of different levels, multi-system collaborative control, and orderly execution in multiple risk conflict scenarios. This constructs the core execution capability of the strategy execution module, providing efficient and reliable execution guarantees for the system to cope with complex driving risks, and further improving the overall functional closed loop of the vehicle multi-risk perception and hierarchical automatic takeover system.

[0039] Specifically, the tire blowout differentiation processing module includes: a tire blowout location precision positioning unit and a location-specific control strategy library; wherein, the tire blowout location precision positioning unit accurately identifies tire blowout situations at four tire positions (left front, right front, left rear, and right rear) through a tire pressure sensor array; the location-specific control strategy library stores dedicated vehicle dynamics control models established for different tire blowout locations.

[0040] By effectively combining the tire blowout location precision positioning unit and the location-specific control strategy library, the system can accurately match the blowout location with the corresponding dynamic control strategy. For scenarios such as sudden steering torque changes when the left front tire blows out, trajectory deviation trends when the right front tire blows out, body roll risks when the left rear tire blows out, and longitudinal dynamic imbalances when the right rear tire blows out, the system can quickly call upon dedicated models from the location-specific control strategy library to achieve active steering system return to center, graded intervention of the braking system, and coordinated torque adjustment of the power system. This effectively suppresses the tendency of the vehicle to lose control at the moment of a blowout, constructing the core handling capability of the differentiated blowout processing module, and further improving the reliability and accuracy of the system in dealing with blowout risks.

[0041] Specifically, the pre-safety protection module includes a collision prediction unit and an airbag pre-deployment unit. The collision prediction unit predicts the likelihood of a collision using radar or camera sensors. The airbag pre-deployment unit initiates the airbag inflation process in advance when an unavoidable collision is predicted. With the effective cooperation of the collision prediction unit and the airbag pre-deployment unit, the collision time and angle can be accurately calculated. When the collision time is less than a preset safety threshold and the collision angle is in a high-risk range, a priority trigger command is immediately sent to the airbag pre-deployment unit. Upon receiving the command, the airbag pre-deployment unit quickly activates the pre-pressurization process of the airbag inflation system, placing the airbag in a semi-inflated standby state, and simultaneously triggering the emergency pre-tensioning module of the seatbelt. By combining the real-time dynamic prediction of the collision prediction unit with the millisecond-level response of the airbag pre-deployment unit, the response delay of the airbag's full deployment can be reduced by more than 30%, ensuring that the airbag has formed an effective buffer barrier at the moment of collision. At the same time, combined with the forced restraint of the seat belt, it greatly reduces the forward displacement of the occupants due to inertia, significantly reducing the severity of head injuries from impacting the steering wheel and chest injuries from compressing the dashboard. This further improves the active pre-protection mechanism of the pre-safety protection module, creating a seamless connection from risk prediction to passive protection for occupants.

[0042] Specifically, the pre-safety protection module also includes: a seat belt intelligent control unit. The seat belt intelligent control unit is used to automatically unlock the seat belt buckle when the vehicle is detected to be on fire or a collision has occurred. By unlocking the seat belt, the driver and passengers can quickly get rid of the restraints so that they can smoothly evacuate the passenger compartment before the fire spreads or the vehicle structure is deformed after the collision, thus avoiding delays in escape due to the seat belt not being able to be unfastened in time.

[0043] By effectively coordinating the collision prediction unit, airbag pre-launch unit, and seatbelt intelligent control unit, the system can accurately cover the entire safety scenario from collision risk prediction to post-accident secondary disaster prevention. It enables multi-unit collaborative linkage for typical working conditions such as emergency braking assistance in frontal collisions, rapid airbag response in side collisions, occupant escape route protection in the event of a vehicle fire, and high-voltage system safety isolation after a collision. This constructs the core protection capability of the pre-safety protection module and improves the vehicle's safety performance in sudden dangerous scenarios.

[0044] Specifically, it also includes: an intelligent early warning module that activates the headlights to flash in advance when a collision risk is predicted; this enables timely transmission of emergency avoidance signals to vehicles and pedestrians in the front, rear, and sides, further improving the efficiency of emergency avoidance in complex traffic scenarios and building a multi-faceted safety barrier for drivers, passengers, and surrounding traffic participants; the intelligent early warning module includes a pre-collision warning unit, which activates the headlights to flash in advance when a collision risk is predicted, thereby forming a conspicuous dynamic warning signal, quickly attracting the attention of drivers of vehicles ahead to slow down and avoid the collision, and reminding pedestrians and non-motorized vehicles to stay away from the danger zone, effectively reducing the risk of multi-vehicle collisions or pedestrians accidentally crossing the road.

[0045] The pre-collision warning unit expands the scope of risk warning, improves the efficiency of warning information transmission and collaborative response capabilities, provides key warning information support for the accurate triggering and execution of subsequent graded automatic takeover decisions, and further strengthens the system's proactive response capability to multiple risks.

[0046] Specifically, it also includes an emergency communication module, which automatically sends vehicle status information to the vehicle call platform after a collision or other dangerous situation described above. This means that the emergency communication module enables emergency contact between the driver / passengers and the platform, effectively reducing the risk. The emergency communication module consists of an accident detection unit, a multi-channel communication unit, and an information transmission unit. The accident detection unit uses acceleration sensors, collision sensors, and airbag trigger signals to comprehensively determine if an accident has occurred. The multi-channel communication unit is used to effectively transmit emergency information. The information transmission unit automatically collects vehicle status data, accident scene images / videos, and GPS location information, and sends them to the vehicle call platform.

[0047] The effective coordination of the accident detection unit, multi-channel communication unit, information transmission unit, and privacy protection unit enables the rapid and accurate initiation of the emergency communication process, ensuring that accident information (including vehicle status, on-site conditions, and location) is efficiently transmitted to the call platform through multiple channels, while ensuring user data security through a privacy protection mechanism.

[0048] Specifically, it also includes: a manual configuration interface that allows users to manually enable or disable specific takeover conditions according to different driving scenarios, and supports triggering by combination of conditions.

[0049] Working principle: The multi-factor risk perception unit in the condition recognition module integrates vehicle status sensors, driver status monitoring sensors, and environmental perception sensors to collect vehicle driving parameters, driver physiological characteristics, and road environment data in real time. The risk grading assessment unit uses a multi-factor weighting algorithm to classify the identified dangerous conditions into three levels: Level 1 (minor), Level 2 (moderate), and Level 3 (severe). The false alarm filtering unit filters invalid signals through multi-sensor data fusion and time-series analysis to ensure accurate and reliable trigger signals. If a tire blowout risk is detected, the blowout location precision positioning unit in the blowout differentiation processing module identifies the blowout location through the tire pressure sensor array, calls the dedicated dynamic model in the location-specific control strategy library, and executes coordinated control of the steering system's active return to center, the braking system's graded intervention, and the power system's torque adjustment.

[0050] To address risks such as collisions and fires, the pre-safety protection module's collision prediction unit uses radar or cameras to predict the likelihood of a collision, activating the airbag pre-deployment unit in advance to shorten response time. In the event of an accident, the seatbelt intelligent control unit automatically unlocks the seatbelt buckle. Simultaneously, the intelligent warning module triggers multi-mode lighting warnings based on the risk level, transmitting avoidance signals to surrounding vehicles and pedestrians. When multiple risks coexist, the strategy execution module's execution priority management unit executes control strategies in an orderly manner according to preset rules. If an accident occurs, the emergency communication module's accident detection unit uses acceleration sensors, collision sensors, etc., to determine the accident status, and the multi-channel communication unit sends the vehicle's location, status, and on-site information to the call platform. Furthermore, users can manually configure takeover conditions according to different driving scenarios through the interface to achieve personalized safety protection.

[0051] Please see Figure 2 The present invention also provides a method for vehicle multi-risk perception and graded automatic takeover, comprising the following steps:

[0052] Step 1, Multi-dimensional Risk Perception and Grading Assessment: Real-time data collection of vehicle status, driver status, and environmental perception is achieved through vehicle status sensors, driver status monitoring sensors, and environmental perception sensors. This identifies multiple hazardous conditions, including tire blowout, driver fatigue, lane departure, collision risk, pedestrian approach, driver error, vehicle skidding, water damage, following too closely, rear-end collision risk, fire, and existing collisions. A risk grading assessment unit uses a multi-factor weighting algorithm to classify these hazardous conditions into three risk levels: Level 1 (minor), Level 2 (moderate), and Level 3 (severe). A false alarm filtering unit uses a false alarm identification model constructed through multi-sensor data fusion and time-series analysis to filter false alarms from hazardous conditions.

[0053] Step 2, Execution of Tiered Response Strategy: Based on the risk level, differentiated control strategies are executed through the tiered response control unit: a warning is issued to the driver for Level 1 risk; partial driver takeover is implemented for Level 2 risk, intervening in some vehicle control systems; full driver takeover is implemented for Level 3 risk, fully controlling the vehicle control system; the steering system, braking system, power system, and stability control system are integrated through the multi-dimensional collaborative control unit; when multiple risk conditions coexist, the corresponding control strategy is executed according to preset priority rules through the priority management unit.

[0054] Step 3, Tire Blowout Differentiation Processing: When a tire blowout hazard is detected, the tire blowout location precision positioning unit uses a tire pressure sensor array to accurately identify the blowout locations of the left front, right front, left rear, and right rear tires; it then calls up the dedicated vehicle dynamics control model corresponding to the blowout location in the location-specific control strategy library to execute stability control and driving strategies.

[0055] Step 4, Pre-safety protection activation: When a collision is predicted to occur by the collision prediction unit, the airbag inflation program is pre-activated; when an accident is detected, the emergency communication module automatically collects vehicle status data, accident scene pictures / videos, and GPS location information, and sends them to the vehicle call platform.

[0056] Step 5, User Interaction Configuration: Allows users to manually enable or disable specific takeover conditions according to different driving scenarios through the manual configuration interface, and supports condition combination trigger settings.

[0057] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vehicle multi-risk perception and graded automatic takeover system, characterized in that, include: The condition recognition module is used to monitor vehicle status, driver status and environmental information in real time, and identify multiple dangerous conditions including tire blowout, fatigued driving, lane departure, collision risk, pedestrian approach, misoperation, vehicle skidding, water damage, following too closely, risk of being rear-ended, fire and collision that has already occurred, and to assess the risk level of the identified dangerous conditions. The strategy execution module executes corresponding graded driving takeover strategies based on the specific hazard type and its risk level identified. The tire blowout differentiation processing module is used to identify the specific location of the blowout when it occurs, and to execute the stability control and driving strategy corresponding to that location. The pre-safety protection module pre-activates the airbag inflation procedure when an impending collision is detected.

2. The vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, The condition identification module includes a multi-factor risk perception unit, a risk grading assessment unit, and a false alarm filtering unit. The multi-factor risk perception unit integrates vehicle status sensors, driver status monitoring sensors, and environmental perception sensors to monitor the hazardous conditions in real time. The risk grading assessment unit uses a multi-factor weighting algorithm to assess the risk level of the identified hazardous conditions, classifying them into Level 1 (minor), Level 2 (moderate), and Level 3 (severe) risks. The false alarm filtering unit constructs a false alarm identification model through multi-sensor data fusion and time-series analysis to reduce the system's false trigger rate.

3. The vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, The strategy execution module includes: a graded response control unit, a multi-dimensional collaborative control unit, and an execution priority management unit; wherein, the graded response control unit executes differentiated control strategies according to the risk level, with early warning reminders for level 1 risks, partial takeover for level 2 risks, and full takeover for level 3 risks; the multi-dimensional collaborative control unit integrates the steering system, braking system, power system, and stability control system; the execution priority management unit is used to execute control strategies according to preset priority rules when multiple risk conditions coexist.

4. The vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, The tire blowout differentiation processing module includes: a blowout location precision positioning unit and a location-specific control strategy library; wherein, the blowout location precision positioning unit accurately identifies the blowout situation at the four tire positions (left front, right front, left rear, and right rear) through a tire pressure sensor array; the location-specific control strategy library stores dedicated vehicle dynamics control models established for different blowout locations.

5. The vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, The pre-safety protection module includes a collision prediction unit and an airbag pre-deployment unit; wherein, the collision prediction unit predicts the probability of a collision using radar or camera sensors; and the airbag pre-deployment unit is used to initiate the airbag inflation procedure in advance when an unavoidable collision is predicted.

6. A vehicle multi-risk perception and graded automatic takeover system according to claim 5, characterized in that, The pre-safety protection module also includes a seat belt intelligent control unit, which is used to automatically unlock the seat belt buckle when the vehicle is detected to be on fire or a collision has occurred.

7. The vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, Also includes: The intelligent warning module activates the headlights to flash in advance when a collision risk is predicted; Furthermore, the intelligent warning module includes a pre-collision warning unit, which is used to activate the vehicle lights to flash in advance when a collision risk is predicted.

8. A vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, It also includes an emergency communication module, which automatically sends vehicle status information to the vehicle call platform after a vehicle is collided with or in the aforementioned dangerous situations. The emergency communication module consists of an accident detection unit, a multi-channel communication unit, and an information sending unit. The accident detection unit uses acceleration sensors, collision sensors, and airbag trigger signals to comprehensively determine that an accident has occurred. The multi-channel communication unit is used to achieve effective transmission of emergency information. The information sending unit is used to automatically collect vehicle status data, accident scene pictures / videos, and GPS location information, and send them to the vehicle call platform.

9. A vehicle multi-risk perception and graded automatic takeover system according to claim 1, characterized in that, Also includes: The manual configuration interface allows users to manually enable or disable specific takeover conditions according to different driving scenarios, and supports triggering by combination of conditions.

10. A method for vehicle multi-risk perception and graded automatic takeover, comprising the vehicle multi-risk perception and graded automatic takeover system as described in any one of claims 1-9, characterized in that, Includes the following steps: Step 1, Multi-faceted Risk Perception and Grading Assessment: Through vehicle status sensors, driver status monitoring sensors, and environmental perception sensors, real-time vehicle status data, driver status data, and environmental perception data are collected to identify multiple dangerous conditions, including tire blowout, fatigue driving, lane departure, collision risk, pedestrian approach, misoperation, vehicle skidding, water damage, following too closely, risk of being rear-ended, fire, and collisions that have already occurred. The risk grading and assessment unit uses a multi-factor weighting algorithm to classify the risk level of the hazardous conditions, resulting in Level 1 risk (minor), Level 2 risk (moderate), and Level 3 risk (severe). The false alarm filtering unit uses a false alarm identification model constructed by multi-sensor data fusion and time series analysis to filter false alarm information in the hazardous conditions. Step 2, Execution of Tiered Response Strategy: Based on the risk level, a differentiated control strategy is executed through the tiered response control unit: a warning is issued to the driver for Level 1 risk; partial driver takeover is implemented for Level 2 risk, intervening in some vehicle control systems; and full driver takeover is implemented for Level 3 risk, fully controlling the vehicle control system. The multi-dimensional collaborative control unit integrates the steering system, braking system, power system, and stability control system; when multiple risk conditions coexist, the execution priority management unit executes the corresponding control strategy according to the preset priority rules. Step 3, Tire Blowout Differentiation Processing: When a tire blowout hazard is detected, the tire blowout location precision positioning unit uses a tire pressure sensor array to accurately identify the blowout locations of the left front, right front, left rear, and right rear tires; it then calls up the dedicated vehicle dynamics control model corresponding to the blowout location in the location-specific control strategy library to execute stability control and driving strategies. Step 4, Pre-safety protection activation: When a collision is predicted to occur by the collision prediction unit, the airbag inflation program is pre-activated; when an accident is detected, the vehicle status data, accident scene pictures / videos, and GPS location information are automatically collected through the emergency communication module and sent to the vehicle call platform. Step 5, User Interaction Configuration: Allows users to manually enable or disable specific takeover conditions according to different driving scenarios through the manual configuration interface, and supports condition combination trigger settings.

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