A multi-modal communication control method for intelligent driving vehicles

Abstract

The present invention provides a multi-modal communication control method for intelligent driving vehicles. Key vehicle dynamic parameters, including steering, braking, and powertrain system state parameters, are collected by on-vehicle sensing terminals. These parameters are transmitted to the control center and stored and analyzed using a cloud database. The control center processes these data using machine learning algorithms to calculate the real-time risk coefficient Rc. The safety threshold Rth is determined by the steering dynamic coefficient, braking dynamic coefficient, and powertrain system risk coefficient through scene adaptive weights. When the real-time calculated risk coefficient exceeds the safety threshold, the system will trigger a risk warning, issue a warning to the driver through the HUD module, and at the same time generate vehicle control instructions to be displayed in the HUD module to reduce risks. This method improves the safety and reliability of intelligent vehicles under various driving conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving vehicles, and in particular to a multimodal communication control method for intelligent driving vehicles. Background Technique

[0002] With the rapid development of artificial intelligence and sensor technology, intelligent driving vehicles have gradually become an important development direction in the automotive industry. During the intelligent driving process, the vehicle needs to continuously perceive its own state and external environment information, and make accurate decisions and controls based on this information to ensure driving safety and efficiency. Therefore, an efficient and reliable communication control method is crucial for intelligent driving vehicles.

[0003] Most of the existing communication control methods for intelligent driving vehicles focus on single-modal information processing, such as only relying on the vehicle operation parameters collected by on-vehicle sensors, or only making decisions based on external environment perception data. This single-modal control method has obvious deficiencies and cannot comprehensively evaluate the driving state of the vehicle, making it difficult to cope with complex and changeable actual driving scenarios. For example, when abnormalities occur in the vehicle's steering, braking, or power system, due to the lack of a comprehensive analysis and dynamic evaluation mechanism for multiple parameters, it is often impossible to accurately judge risks in a timely manner, easily leading to accidents.

[0004] Although some multimodal control methods consider multiple information sources, in the process of data processing and risk assessment, they lack an accurate grasp of the vehicle's dynamic characteristics in different scenarios. The set safety thresholds are usually fixed or only adjusted according to simple rules, and cannot be adaptively optimized according to actual scenarios such as road types and weather conditions, resulting in low accuracy and reliability of risk assessment. At the same time, during the vehicle operation, it is also difficult to dynamically learn and optimize the safety thresholds based on actual feedback, resulting in insufficient adaptability and stability of the system.

[0005] Therefore, there is an urgent need for an intelligent driving vehicle communication control method that can integrate multimodal information, adaptively adjust the safety threshold according to different scenarios, and continuously optimize the control strategy through online learning to improve the safety and reliability of intelligent driving vehicles. Summary of the Invention

[0006] In view of this, the present invention provides a multimodal communication control method for intelligent driving vehicles. The vehicle dynamic parameters are collected by an on-vehicle sensing terminal and stored in the cloud. The control center calculates the correlation coefficients related to the steering, braking, and power systems based on this, and sets the safety threshold Rth. The vehicle dynamic parameters are collected in real time to calculate the risk index Rc, which is compared with the threshold to judge the driving state. When the threshold is exceeded, a warning is given and the threshold is optimized, realizing multimodal data fusion and dynamic risk control, improving the safety and reliability of intelligent driving, and effectively solving the problems raised in the background technique.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] S1. The vehicle-mounted sensing terminal collects the vehicle dynamic parameters of the same series of vehicles, and stores these vehicle dynamic parameter data in the cloud database module. The vehicle dynamic parameter data of each vehicle constructs an independent data subset, forming a data set;

[0009] S2. The control center extracts the above vehicle dynamic parameters in a single data subset from the data set as a training set. The vehicle dynamic parameters include steering dynamic parameters, braking dynamic parameters, and power system state parameters;

[0010] S3. The control center processes the extracted steering dynamic parameters to obtain a steering dynamic coefficient, processes the braking dynamic parameters to obtain a braking dynamic coefficient, processes the power system state parameters to obtain a power system risk coefficient, and sets a safety threshold Rth through scene adaptive weight calculation;

[0011] S4. The vehicle-mounted sensing terminal real-time collects the current vehicle dynamic parameters, and transmits the current vehicle dynamic parameter data to the control center in real time through the real-time communication module. After receiving the current vehicle dynamic parameter data, the control center performs preprocessing and conducts a comprehensive evaluation based on multiple risk factors to calculate the risk index Rc;

[0012] S5. The control center compares the calculated risk index Rc with the preset safety threshold Rth to determine whether the driving state of the vehicle exceeds the safety threshold Rth;

[0013] S6. If the driving state of the vehicle does not exceed the safety threshold Rth, the current driving state is maintained without intervention;

[0014] S7. If the driving state of the vehicle exceeds the safety threshold Rth, while the vehicle control system sends a warning message to the HUD module and emits a sound to remind the vehicle owner according to the control instruction, the vehicle control system records and feeds back the execution result to the control center. The control center dynamically optimizes the safety threshold Rth through online learning based on these recorded and fed-back data, and simultaneously updates the warning prompt of the HUD.

[0015] The technical effects and advantages of the present invention:

[0016] By real-time monitoring of vehicle dynamic parameters such as steering dynamic parameters, braking dynamic parameters, and power system state parameters, and conducting risk assessment based on these parameters, the present invention can significantly improve the driving safety of intelligent driving vehicles. By accurately calculating the risk coefficient Rc and comparing it with the safety threshold Rth, the system can timely identify potential risks and take corresponding measures to avoid accidents;

[0017] The present invention can not only identify and respond to the current risk situation, but also learn from historical data through machine learning algorithms, continuously optimize the risk assessment model, improve the accuracy and adaptability of risk management. By dynamically adjusting the safety threshold Rth and model parameters, the system can adapt to different driving conditions and environments, achieving more refined risk control;

[0018] Through real-time risk assessment and control, the present invention can reduce unnecessary emergency braking or lane changes, provide a smoother driving experience, and improve the comfort of passengers. At the same time, by timely feedback of risk information and control instructions to the driver through the HUD module, it enhances the driver's understanding of the vehicle state and improves the confidence and satisfaction of driving;

[0019] Through precise risk assessment and control, the present invention helps to reduce the vehicle maintenance and repair costs caused by accidents or wrong decisions. The optimized control strategy can also reduce fuel consumption and vehicle wear, thus reducing the long-term operation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0021] Figure 2 It is a flowchart of the overall structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] As shown in the attached Figure 1 A multi-modal communication control method for intelligent driving vehicles includes: an in-vehicle sensing terminal, a vehicle control system, a control center, a cloud database module, and a HUD module;

[0024] The in-vehicle sensing terminal, as the core device for an intelligent driving vehicle to achieve environmental perception and data collection, integrates a variety of high-precision sensors including meteorological sensors, in-vehicle cameras, etc., and real-time collects the vehicle's own dynamic parameters and external environment information, and processes the data into a standardized format, laying the foundation data support for the entire multi-modal communication control system;

[0025] The vehicle control system is the execution center for intelligent driving vehicles to achieve precise control and safe driving. It strictly implements real-time and precise control over key systems such as vehicle power output, braking response, and steering operation according to the instructions issued by the control center, ensuring that the vehicle always operates stably according to the predetermined strategy. At the same time, it has a perfect fault emergency handling mechanism to provide a solid guarantee for driving safety;

[0026] The control center is the core hub of the multi-modal communication control system of intelligent driving vehicles, integrating functions such as data processing, analysis and decision-making, and instruction scheduling. Through in-depth integration and efficient operation of various types of information, it outputs scientific decision-making support for the safe and efficient operation of the vehicle, and coordinates the orderly cooperation of each terminal and module;

[0027] The cloud database module, as the data cornerstone of the multi-modal communication control system of intelligent driving vehicles, undertakes the tasks of storing, managing, calling, and sharing massive amounts of data, providing rich data support for the control center to build a risk assessment model, realize real-time vehicle risk monitoring and dynamic decision-making, and ensuring the stable and efficient operation of the entire system;

[0028] The HUD module is a key device for intelligent driving vehicles to achieve efficient human-machine interaction. It clearly projects important driving information in front of the driver's line of sight, avoiding potential safety hazards caused by line-of-sight transfer, and assisting the driver in decision-making in an intuitive and clear visual way. At the same time, this module shoulders the important responsibility of risk warning, which can significantly improve driving safety and driving experience.

[0029] As attached Figure 2 shown, a multi-modal communication control method for intelligent driving vehicles, the specific implementation method includes the following steps:

[0030] S1. The vehicle-mounted sensing terminal collects the vehicle dynamic parameters of the same series of vehicles, and stores these vehicle dynamic parameter data in the cloud database module. The vehicle dynamic parameter data of each vehicle constructs an independent data subset, forming a data set.

[0031] In this embodiment, it should be specifically explained the specific application of vehicle dynamic parameter data in multi-modal communication control. The vehicle dynamic parameter data will represent the specific situation of the vehicle. Based on these situations, the vehicles are classified. The vehicle classification can be based on different ranges of vehicle dynamic parameters. For example, the steering command response delay can be divided into categories such as less than 0.5 seconds, 0.5 to 1 second, greater than 1 second, etc.

[0032] It should be further noted that the vehicle-mounted sensing terminal reaches the control center through the real-time communication module, supporting real-time data transmission to the cloud database module. By integrating the communication setting function unit in the vehicle-mounted terminal, such as the "data upload" or "synchronization" function, the vehicle-mounted sensing terminal is thus guided to upload dynamic parameter data, where the uploaded data includes the vehicle dynamic parameters collected in real time and the historical collected data. It should be explained that before collecting the vehicle dynamic parameters, the data collection scope and purpose need to be clearly informed in the vehicle-mounted system. For example, an authorization pop-up window is displayed through the in-vehicle infotainment system to request the driver's consent for data collection, so as to reduce the risk of privacy leakage and improve privacy protection.

[0033] S2. The control center extracts the above vehicle dynamic parameters from a single data subset in the dataset as the training set, and the vehicle dynamic parameters include steering dynamic parameters, braking dynamic parameters, and power system state parameters.

[0034] In this embodiment, it should be specifically noted that the control center extracts the vehicle dynamic parameter data from the cloud database module as the training set. The steering dynamic parameters include: the time interval tr from when the driver issues a steering command to when the vehicle actually executes the steering action, which directly reflects the response sensitivity of the steering system to the operation command. The shorter tr is, the more timely the steering system can respond to the driving intention, ensuring the timeliness and safety of operations such as lane change and turning; the time period Te for the vehicle to complete a full steering action, which reflects the working efficiency of the steering system. The shorter Te is, the more obvious the handling advantage of the vehicle when turning on a narrow road or making an emergency avoidance. The braking dynamic parameters include: the time interval td from when the brake pedal is pressed to when the braking system actually responds, and its length directly affects the starting time of emergency braking. The smaller td is, the faster the braking can be started at a dangerous moment, shortening the braking distance; the brake pedal pressure build-up time tp, which reflects how quickly the braking system generates effective braking force. The shorter tp is, the more quickly sufficient braking force can be established, improving the braking efficiency; the efficiency η of the braking system under actual driving conditions, which is the ratio of the actual braking distance to the theoretical braking distance calculated based on vehicle speed, road surface friction coefficient, and slope. The higher η is, the more fully the braking system can perform its performance under actual road conditions, ensuring safe parking. The power system state parameters are: the engine output efficiency ϵ, which represents the efficiency level of the engine converting fuel energy into mechanical energy. The higher ϵ is, the more efficient the fuel utilization is, and the better the vehicle's power performance and economy; the engine coolant temperature T, which is a key indicator for measuring the working thermal state of the engine. An appropriate T can ensure that the engine is in the optimal working temperature range, avoiding failures caused by overheating and maintaining the stability and reliability of power output.

[0035] S3. The control center processes the extracted steering dynamic parameters to obtain the steering dynamic coefficient, processes the braking dynamic parameters to obtain the braking dynamic coefficient, and processes the power system state parameters to obtain the power system risk coefficient. By synthesizing the above coefficients, the safety threshold Rth is set through scene adaptive weight calculation.

[0036] In this embodiment, it is necessary to explain the complete process of the control center setting the safety threshold Rth. For the steering dynamic parameters, according to the formula , the steering dynamic coefficient is calculated. c1 is the steering dynamic coefficient, trmax is the maximum historical steering delay time of the same vehicle model, and Temax is the maximum steering period allowed by the vehicle design. This formula converts the parameters related to the steering response time into the steering dynamic coefficient. The larger the coefficient, the slower the steering system response and the higher the potential risk. For the braking dynamic parameters, the formula is used. c2 is the braking dynamic coefficient, tdnominal and tpnominal are nominal values, η is the ratio of the actual braking distance to the theoretical braking distance, and w1, w2, w3 are dynamically allocated dynamic weights through the correlation analysis of the real-time vehicle speed and braking pressure. This formula comprehensively considers the braking response time and braking efficiency. The higher the coefficient, the worse the performance of the braking system. For the power system state parameters, according to the formula

[0037] , the power system risk coefficient c3 is calculated. Finally, the control center combines the above three coefficients and calculates the safety threshold Rth through the formula , where α, β, γ are scene adaptive weight coefficients. In the highway scene, due to the high vehicle speed, the weights of the steering dynamic coefficient and the braking dynamic coefficient will be increased. In the rainy day scene, considering the influence of the slippery road surface on braking, the weight of the braking dynamic coefficient will be increased to ensure that the safety threshold meets the safety requirements of different scenes.

[0038] S4. The vehicle-mounted sensing terminal continuously collects the current vehicle dynamic parameters and transmits the current vehicle dynamic parameter data to the control center in real time through the real-time communication module. After receiving the current vehicle dynamic parameter data, the control center performs preprocessing and conducts a comprehensive evaluation based on multiple risk factors to calculate the risk index Rc.

[0039] In this embodiment, the dynamic parameters collected by the vehicle-mounted sensing terminal include but are not limited to the time interval tr from when the driver issues a steering command to the start of the actual steering action of the vehicle, the time period Te required for the vehicle to complete a complete steering action, the time interval td from when the brake pedal is depressed to the response of the braking system, the brake pedal pressure establishment time tp, the efficiency η of the braking system under actual driving conditions, the engine output efficiency ϵ, the temperature T of the engine coolant, etc. These parameters are sampled by the vehicle-mounted sensing terminal and packed into a standardized data format.

[0040] It is necessary to explain the process of data transmission to the control center and preprocessing. The on-vehicle sensing terminal transmits the collected data to the control center quickly and stably through the real-time communication module. After receiving the data, the control center first performs data verification to ensure that the data has not been tampered with during transmission through methods such as hash verification; then it performs outlier detection to eliminate error data caused by sensor failures and other reasons; finally, it normalizes the data, mapping different parameters to a suitable numerical range for subsequent analysis and calculation.

[0041] It is necessary to emphasize the application and subsequent processing of the real-time risk assessment results. The control center compares the calculated risk coefficient Rc with the pre-set safety threshold Rth. When Rc≥Rth, the system immediately activates corresponding instruction measures, such as reminding the driver to adjust the power output to limit the vehicle speed and issuing warning prompts to the driver. In addition, the control center will also archive the data and processing results of each real-time risk assessment, conduct in-depth analysis of these data regularly, mine potential risk patterns and rules, dynamically adjust and optimize the safety threshold Rth, and continuously improve the accuracy and effectiveness of the real-time risk assessment to better ensure the safety of vehicle driving.

[0042] S5. The control center compares the calculated risk index Rc with the pre-set safety threshold Rth to determine whether the driving state of the vehicle exceeds the safety threshold Rth.

[0043] In this embodiment, it is necessary to specifically explain the complete process of the control center based on the comparison of the risk coefficient and the safety threshold: The control center, as the decision-making core of the intelligent driving vehicle, receives the risk coefficient Rc calculated from S4 in real time and compares it with the pre-set safety threshold Rth. Based on this comparison result, the control center will conduct risk judgment and generate corresponding control instructions to ensure the safety and stability of vehicle driving.

[0044] It is necessary to further explain the specific method and decision-making logic for comparing the risk coefficient and the safety threshold. The risk coefficient Rc is calculated by comprehensively considering multi-dimensional risk factors such as the real-time dynamic parameters of the vehicle and the driving environment, accurately reflecting the current risk level of the vehicle; the safety threshold Rth is a safety baseline set by the control center based on a large amount of historical data, combined with factors such as vehicle type, road conditions, and weather conditions, and optimized through machine learning algorithms. The control center directly compares Rc with Rth through numerical comparison. This simple and intuitive comparison method provides a clear and definite judgment basis for subsequent risk management decisions.

[0045] S6. If the driving state of the vehicle does not exceed the safety threshold Rth, the current driving state is maintained without intervention.

[0046] In this embodiment, it should be specifically noted that the control center, as the core decision-making unit of the intelligent driving vehicle, after comparing and judging the risk index Rc and the safety threshold Rth, if it is concluded that Rc ≤ Rth, it is determined that the current vehicle driving state is within the safe and controllable range. At this time, the "maintain without intervention" strategy is triggered: the control center sends an instruction of "maintain the current state" to the vehicle control system, and the vehicle continues to operate stably according to the existing power output, steering angle, driving speed and other parameters, while continuously monitoring the real-time data.

[0047] It should be further noted that the safety threshold Rth is a safety benchmark dynamically adjusted by the control center based on historical data training and combined with real-time scenarios. When Rc ≤ Rth, it indicates that the comprehensive evaluation result of the vehicle's real-time dynamic parameters and environmental risk factors does not exceed the safety boundary, and the coordinated operation of the vehicle's steering, braking and power systems is within the design expectation range, and driving safety can be guaranteed without additional intervention. This decision depends on the scenario adaptive safety model constructed in step S3 and the risk index calculated in real time in step S4. The two form a precise quantitative evaluation of the driving state through multi-modal data fusion, providing a scientific basis for the "no intervention" decision.

[0048] It should be explained that "no intervention" does not mean completely stopping monitoring. The on-vehicle sensing terminal still collects data in real time and transmits it to the control center. The control center updates the risk index Rc at a constant frequency to ensure the instant response ability to sudden risks, forming a dynamic balance between safety and efficiency.

[0049] S7. If the vehicle driving state exceeds the safety threshold Rth, while the vehicle control system sends the warning information to the HUD module according to the control instruction and gives a sound reminder to the vehicle owner, the vehicle control system records and feeds back the execution result to the control center. The control center dynamically optimizes the safety threshold Rth through online learning based on these recorded and fed-back data, and at the same time updates the warning prompt of the HUD.

[0050] In this embodiment, it should be specifically noted that the specific processes and logics of control execution and feedback optimization are as follows: the vehicle control system receives control instructions from the control center, including operations such as deceleration, braking, lane change or parking, and displays them in the HUD module to remind the driver; after the driver executes the control instruction, the execution result is recorded, and the vehicle control system feeds back the execution result to the control center for further analysis and optimization; the control center dynamically optimizes the safety threshold Rth through an online learning algorithm using the fed-back data; the control center updates the warning prompt displayed on the HUD according to the optimized safety threshold Rth to provide the driver with the latest risk assessment result.

[0051] It should be noted that through this step, the intelligent driving vehicle can dynamically generate and execute control instructions according to the risk level evaluated in real time, ensuring the safety and reliability of vehicle driving. At the same time, through the feedback and online learning mechanism, the system can continuously optimize the control strategy, improve the performance of intelligent driving, and the optimized weight parameters of the logistic regression model and the safety threshold Rth will be applied to subsequent real-time risk assessment, making the system's risk judgment more accurate and the generation of control instructions more reasonable.

[0052] A multimodal communication control medium for an intelligent driving vehicle, which stores executable program code that, when executed by a processor, implements the above multimodal communication control method for an intelligent driving vehicle.

[0053] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0054] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multimodal communication control method for intelligent driving vehicles, characterized in that, It includes an in-vehicle sensing terminal, a vehicle control system, a control center, a cloud database module, and an HUD module. The in-vehicle sensing terminal, vehicle control system, control center, and HUD module are all installed on the intelligent driving vehicle, and the cloud database module is located in the cloud. The method specifically includes the following steps: S1. The in-vehicle sensing terminal collects the vehicle dynamic parameters of the same series of vehicles and stores these vehicle dynamic parameter data in the cloud database module. The vehicle dynamic parameter data of each vehicle constructs an independent data subset, forming a data set; S2. The control center extracts the above vehicle dynamic parameters in a single data subset from this data set as the training set. The vehicle dynamic parameters include steering dynamic parameters, braking dynamic parameters, and power system status parameters; S3. The control center processes the extracted steering dynamic parameters to obtain the steering dynamic coefficient, processes the braking dynamic parameters to obtain the braking dynamic coefficient, processes the power system status parameters to obtain the power system risk coefficient, and sets the safety threshold Rth through scene adaptive weight calculation; S4. The in-vehicle sensing terminal collects the current vehicle dynamic parameters in real time and transmits the current vehicle dynamic parameter data to the control center in real time through the real-time communication module. After receiving the current vehicle dynamic parameter data, the control center performs preprocessing and conducts a comprehensive evaluation based on multiple risk factors to calculate the risk index Rc; S5. The control center compares the calculated risk index Rc with the preset safety threshold Rth to determine whether the driving state of the vehicle exceeds the safety threshold Rth; S6. If the driving state of the vehicle does not exceed the safety threshold Rth, the current driving state is maintained without intervention; S7. If the driving state of the vehicle exceeds the safety threshold Rth, while the vehicle control system sends a warning message to the HUD module according to the control instruction and emits a sound to remind the vehicle owner, the vehicle control system records and feeds back the execution result to the control center. The control center dynamically optimizes the safety threshold Rth through online learning based on these recorded and fed-back data, and at the same time updates the warning prompt of the HUD.

2. The multimodal communication control method for an intelligent driving vehicle according to claim 1, wherein: The vehicle dynamic parameters include steering dynamic parameters, braking dynamic parameters, and power system status parameters. The steering dynamic parameters include the time interval tr from when the driver issues a steering command to the start of the actual steering action of the vehicle, and the time period Te required for the vehicle to complete a full steering action; The braking dynamic parameters include the time interval td from when the brake pedal is depressed to the response of the braking system, the brake pedal pressure establishment time tp, and the efficiency η of the braking system under actual driving conditions; The power system status parameters include the engine output efficiency ϵ and the temperature of the engine coolant, denoted as T.

3. The multimodal communication control method for an intelligent driving vehicle according to claim 2, wherein: The efficiency η of the braking system under actual driving conditions is the ratio of the actual braking distance to the theoretical braking distance. The theoretical braking distance is calculated based on the real-time vehicle speed, real-time road surface friction coefficient, and slope, and the real-time road surface friction coefficient and slope information are obtained through the in-vehicle sensing terminal.

4. The multimodal communication control method for an intelligent driving vehicle according to claim 1, characterized in that: The control center processes the extracted steering dynamic parameters to obtain the steering dynamic coefficient. The specific processing method is as follows: Calculate the steering dynamic coefficient through the standardization method. The calculation method is as follows: , where c1 is the steering dynamic coefficient, trmax is the maximum historical steering delay time of the same vehicle model, and Temax is the maximum steering cycle allowed by vehicle design.

5. The multimodal communication control method for an intelligent driving vehicle according to claim 1, wherein: The control center processes the extracted braking dynamic parameters to obtain the braking dynamic coefficient. The specific processing method is as follows: Calculate the braking dynamic coefficient through weighted fusion. The calculation method is as follows: ， c2 is the braking dynamic coefficient, tdnominal and tpnominal are nominal values, η is the ratio of the actual braking distance to the theoretical braking distance, and w1, w2, w3 are dynamically allocated dynamic weights through the correlation analysis of the real-time vehicle speed and braking pressure.

6. The multimodal communication control method for an intelligent driving vehicle according to claim 1, characterized in that: The control center processes the extracted power system state parameters to obtain the power system risk coefficient. The specific processing method is as follows: Calculate the power system risk coefficient through threshold segmentation, denoted as c3. The calculation method is as follows: 。 7. The multimodal communication control method for an intelligent driving vehicle according to claim 1, characterized in that: The safety threshold synthesizes the steering dynamic coefficient, braking dynamic coefficient, and power system state parameters and sets the safety threshold Rth through scene adaptive weights. The calculation method is as follows: ， α, β, γ are scene adaptive weight coefficients, which are dynamically adjusted according to the road type identified by the on-vehicle camera in the on-vehicle sensing terminal and the weather condition information obtained by the meteorological sensor.

8. The multimodal communication control method for an intelligent driving vehicle according to claim 1, characterized in that: The risk index Rc is calculated by fusing the steering dynamic coefficient c1, braking dynamic coefficient c2, and power system risk coefficient c3. The specific weights are dynamically allocated according to the real-time driving scenario.

9. The multimodal communication control method for an intelligent driving vehicle according to claim 1, characterized in that: The optimization process of the control center dynamically optimizing the safety threshold Rth through online learning includes: The control center counts the feedback frequency of the vehicle driving state exceeding the safety threshold Rth, and the statistical period is once per hour; If the feedback frequency continuously exceeds the preset threshold in three consecutive statistical periods, it indicates that the current safety threshold Rth is relatively high, and the control center automatically reduces the values of the scene adaptive weight coefficients α, β, γ and narrows the range of the safety threshold Rth; If the feedback frequency continuously is lower than the preset threshold in three consecutive statistical periods, it indicates that the current safety threshold Rth is relatively low, and the control center automatically increases the values of the scene adaptive weight coefficients α, β, γ and expands the range of the safety threshold Rth.

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