Driving assistance function dynamic suppression method, driving assistance system and domain controller

Abstract

The invention provides a driving assistance function dynamic suppression method, a driving assistance system and a domain controller. The driving assistance function dynamic suppression method comprises the steps that the rear load of a main vehicle is monitored in real time, the weight of a trailer is estimated based on the rear load, and meanwhile the size of the trailer is obtained; then acquiring a current driving scene and a trailer attitude anomaly level, and outputting a risk level based on the trailer weight, the trailer size, the current driving scene and the trailer attitude anomaly level; and further executing a corresponding driving assistance function suppression strategy according to the risk level. According to the method, dynamic, intelligent and refined upgrading of the driving assistance function suppression strategy in the trailer mode is achieved, the problem of function deficiency and potential safety hazards caused by traditional'one-step 'suppression is solved, dynamic balance of safety and usability is achieved through a risk driving mechanism, and remarkable technical advancement and commercial potential are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of driving assistance, in particular to a driving assistance function dynamic inhibition method, a driving assistance system and a domain controller. BACKGROUND

[0002] With the deep popularization of intelligent driving and advanced driver assistance systems (ADAS), the intelligent level of vehicles in complex scenarios has been greatly improved. The current mainstream "towing vehicle auxiliary parking" system can recognize the towing vehicle hook through the camera and radar, predict the towing vehicle trajectory, and provide guidance for the driver.

[0003] However, the current towing vehicle auxiliary parking system usually forcibly closes all driving assistance functions to ensure absolute safety in the towing vehicle scenario. However, suddenly closing all assistance functions will affect the operation logic and driving experience of the driver, and in some scenarios (such as sudden obstacles in front), completely closing the driving assistance function will actually make the vehicle face higher risks. Therefore, how to balance the safety and usability of the vehicle dynamically is a technical problem to be solved. SUMMARY

[0004] The present application provides a driving assistance function dynamic inhibition method, a driving assistance system and a domain controller to solve the above technical problems.

[0005] Specifically, the present application provides a driving assistance function dynamic inhibition method, comprising the following steps: real-time monitoring the rear load of the host vehicle, and estimating the towing vehicle weight based on the rear load, while obtaining the towing vehicle size; obtaining the current driving scenario and the towing vehicle attitude abnormality level, to output the risk level based on the towing vehicle weight, the towing vehicle size, the current driving scenario and the towing vehicle attitude abnormality level; and executing the corresponding driving assistance function inhibition strategy according to the risk level.

[0006] In the above technical solution, the driving assistance function inhibition strategy in the towing vehicle mode is dynamically, intelligently and finely upgraded. It not only solves the functional loss and safety hazards caused by the traditional "one-size-fits-all" inhibition, but also realizes the dynamic balance between safety and usability through the risk driving mechanism, which has significant technical advancement and commercialization potential.

[0007] Further, the estimation of the towing vehicle weight based on the rear load comprises: obtaining the traction force and acceleration data of the host vehicle, and combining the traction force, acceleration data and rear load to estimate the towing vehicle weight.

[0008] In the above-mentioned technical solutions, traditional systems lack accurate consideration of trailer weight, while the method of this application can provide key parameters for subsequent risk assessment. Trailers of different weights have different dynamic characteristics during driving and different needs for driver assistance functions. For example, heavier trailers have greater inertia when braking and need to use the automatic emergency braking function more cautiously. Accurately estimating trailer weight helps to formulate more appropriate mitigation strategies.

[0009] Furthermore, obtaining the trailer size includes: real-time detection of the trailer outline to calculate the trailer size based on the trailer outline; or, real-time detection of trailer features to match the trailer features based on a preset trailer type library and obtain the trailer size based on the matching result; or, communicating with the trailer through a preset communication technology to obtain the trailer size pre-loaded in the trailer tag.

[0010] The above technical solutions provide multiple ways to obtain trailer dimensions, including detecting trailer outlines, feature matching, and communicating with the trailer to obtain dimensions. Traditional systems do not adequately consider trailer dimensions, which affect the overall handling of the vehicle and safety in different scenarios. For example, larger trailers require a larger turning radius when turning. Understanding trailer dimensions can more accurately adjust parameters of functions such as lane keeping, avoiding dangers caused by misjudgment.

[0011] Furthermore, obtaining the trailer attitude anomaly level includes: detecting the trailer attitude; wherein the trailer attitude includes at least the sway angle, sway frequency, and yaw rate; comparing the trailer attitude with the corresponding attitude threshold to obtain the current trailer attitude anomaly level based on the comparison result.

[0012] In the above technical solution, the abnormal attitude level of the trailer (such as abnormal swing angle, swing frequency and yaw rate) reflects the stability of the trailer, which is more conducive to the refinement of the risk level output mechanism.

[0013] Furthermore, before implementing the driving assistance function suppression strategy, the method further includes: performing dual-channel verification based on vehicle sensors; if the verification passes, implementing the corresponding driving assistance function suppression strategy according to the risk level; otherwise, determining that the driving assistance system is faulty, and implementing the driving assistance function suppression strategy corresponding to the lowest risk level.

[0014] The above technical solution improves the reliability of the system, avoids erroneous decisions caused by sensor failure or misjudgment, and executes the strategy corresponding to the lowest risk level when the driver assistance system is determined to be faulty, thus ensuring the basic safety of the vehicle.

[0015] Furthermore, when implementing the driving assistance function suppression strategy, it also includes: displaying the currently suppressed driving assistance function in real time and displaying the current risk level; and, when the risk level increases, executing a voice warning.

[0016] The above technical solutions enhance human-computer interaction, allowing drivers to clearly understand the system's working status and reducing operational confusion and psychological gap; at the same time, voice warnings can promptly remind drivers to pay attention to potential risks, improving driving safety.

[0017] Furthermore, the dynamic suppression method for driving assistance functions also includes: collecting user behavior data in real time, and optimizing the assessment threshold and / or driving assistance function suppression strategy corresponding to the risk level based on the user behavior data.

[0018] In the above technical solution, as users spend more time using the system, the system can be adjusted according to the user's habits and actual situation, further improving the system's adaptability and performance, and achieving a personalized driving assistance experience.

[0019] Based on the same concept, this application also provides a driving assistance system that employs the aforementioned dynamic suppression method for driving assistance functions to implement a corresponding driving assistance function suppression strategy. The driving assistance system includes: a multi-sensor layer for real-time monitoring of the rear load of the vehicle and obtaining the trailer size, current driving scenario, and trailer attitude; a real-time status assessment module for estimating the trailer weight based on the rear load and obtaining the trailer attitude anomaly level based on the trailer attitude; a risk level dynamic assessment engine for outputting a risk level based on the trailer weight, trailer size, current driving scenario, and trailer attitude anomaly level; and a dynamic suppression strategy decision center for executing the corresponding driving assistance function suppression strategy according to the risk level.

[0020] Furthermore, the driving assistance system also includes: a function suppression and cooperative execution module, used to perform dual-channel verification based on vehicle sensors, so as to drive the dynamic suppression strategy decision center based on the verification results; a human-machine interaction and status feedback module, used to display the currently suppressed driving assistance functions in real time, display the current risk level, and execute voice warnings when the risk level increases; and also used to collect user behavior data in real time, so as to optimize the judgment threshold and / or driving assistance function suppression strategy corresponding to the risk level based on the user behavior data.

[0021] In the above technical solution, through the collaborative work of modules such as the multi-sensor layer, real-time status assessment module, risk level dynamic assessment engine, and dynamic suppression strategy decision center, the dynamic, intelligent, and refined upgrade of the driver assistance function suppression strategy in trailer mode is realized. The various modules cooperate with each other to form a complete closed-loop system. From data collection and risk assessment to strategy execution and optimization, it comprehensively solves the problems of traditional systems at the human-machine collaboration level, and has significant technological advancement and commercial potential.

[0022] Based on the same concept, this application also provides a domain controller for implementing the coordinated operation and optimization of various driving assistance functions of the driving assistance system.

[0023] Compared with the prior art, the beneficial effects of this application are as follows: This application realizes a dynamic, intelligent, and refined upgrade of the driving assistance function suppression strategy in trailer mode. It not only solves the functional deficiencies and safety hazards caused by the traditional "one-size-fits-all" suppression, but also achieves a dynamic balance between safety and availability through a risk-driven mechanism, demonstrating significant technological advancement and commercial potential. Attached Figure Description

[0024] Figure 1 This is a flowchart of the dynamic suppression method for driving assistance functions described in this application.

[0025] Figure 2 This is a framework diagram of the main modules of the driving assistance system described in this application.

[0026] Figure 3 This is a framework diagram of the complete module of the driving assistance system described in this application.

[0027] Figure 4 This is a schematic diagram showing the connection relationship between the domain controller and the driving assistance system described in this application. Detailed Implementation

[0028] The following describes in further detail a method for dynamically suppressing driving assistance functions, a driving assistance system, and a domain controller according to this application, with reference to specific embodiments and accompanying drawings.

[0029] Please see Figure 1 This application provides a method for dynamically suppressing driving assistance functions, including the following steps S100-S300.

[0030] In one feasible implementation, a dynamic, intelligent, and refined upgrade of the driver assistance function suppression strategy in trailer mode is achieved by constructing a closed-loop system encompassing "perception-evaluation-decision-execution-feedback." Specifically, air suspension pressure / height sensors monitor changes in the load on the rear of the vehicle and, combined with traction and acceleration data, the trailer weight is inferred from a dynamic model. Then, the trailer profile is detected in real-time using a rear-facing camera and millimeter-wave radar, either through rapid matching based on a pre-defined trailer type library or by pre-loading dimensions using Bluetooth or UWB tags, to obtain the trailer's length and width. The current driving scenario is identified, and the trailer attitude is obtained based on the relative motion between the vehicle and trailer. Kalman filtering or a deep learning model is then used for attitude prediction to determine the trailer attitude anomaly level. Finally, a weighted scoring method is used, combining the trailer weight, trailer size, current driving scenario, and the risk value corresponding to the trailer attitude anomaly level, to output a risk level. Based on pre-defined strategy suggestions, the driver assistance function suppression strategy corresponding to the current risk level is executed.

[0031] The specific implementation methods of steps S100-S300 will be described in detail below.

[0032] Step S100: Monitor the rear load of the main vehicle in real time, estimate the trailer weight based on the rear load, and obtain the trailer dimensions.

[0033] The step of estimating the trailer weight based on the rear load includes: acquiring the traction and acceleration data of the main vehicle, and combining the traction and acceleration data with the rear load to estimate the trailer weight.

[0034] In some embodiments, vehicle suspension sensors (such as air suspension pressure / height sensors) are used to monitor changes in the rear load of the lead vehicle. Combined with traction and acceleration data, the trailer weight is inferred from a dynamic model. The lead vehicle's engine control system can directly measure the output torque, and by combining this with parameters such as the transmission ratio of the drivetrain, the traction force on the lead vehicle's wheels can be calculated. Simultaneously, the lead vehicle's accelerometer can measure the corresponding acceleration. The lead vehicle transmits force to the trailer via a traction device, causing the trailer to follow the lead vehicle's movement. The lead vehicle's acceleration reflects the changes in the motion state of the entire system (including the lead vehicle and trailer). By establishing a dynamic model of the lead vehicle and trailer, using the lead vehicle's traction and acceleration, and combining this with the rear load of the lead vehicle, the trailer weight can be estimated.

[0035] In the above-mentioned technical solutions, traditional systems lack accurate consideration of trailer weight, while the method of this application can provide key parameters for subsequent risk assessment. Trailers of different weights have different dynamic characteristics during driving and different needs for driver assistance functions. For example, heavier trailers have greater inertia when braking and need to use the automatic emergency braking function more cautiously. Accurately estimating trailer weight helps to formulate more appropriate mitigation strategies.

[0036] Furthermore, obtaining the trailer size includes: real-time detection of the trailer outline to calculate the trailer size based on the trailer outline; or, real-time detection of trailer features to match the trailer features based on a preset trailer type library and obtain the trailer size based on the matching result; or, communicating with the trailer through a preset communication technology to obtain the trailer size pre-loaded in the trailer tag.

[0037] In some embodiments, the trailer outline is monitored in real time using a rear-facing camera on the main vehicle and millimeter-wave radar. The rear-facing camera captures visual images of the trailer, providing rich texture and color information to help identify its approximate outline. The millimeter-wave radar accurately measures the distance and speed of target objects. Combined with the camera images, the trailer's outline boundaries can be determined more accurately, thereby calculating the trailer's length and width. This dimension acquisition method does not depend on whether the trailer carries additional equipment, possessing a certain degree of versatility and applicable to various types of trailers. Furthermore, the camera and radar data complement each other, improving the accuracy and reliability of the identification.

[0038] Alternatively, in other embodiments, a trailer type library is pre-established, containing size information for common trailers (such as RVs, trailer trucks, and trailer-trailers). After detecting a trailer, the system compares the identified trailer features with templates in the type library to find the best-matching trailer type, thereby obtaining the length and width information corresponding to that type of trailer. This size acquisition method in this embodiment can reduce the computational load and time of real-time measurement; however, it may fail to identify special trailers not included in the type library.

[0039] Alternatively, in other embodiments, the main vehicle can communicate with the trailer's built-in smart tag via Bluetooth or UWB technology to obtain the trailer's identity information and size data pre-stored in the tag. This embodiment's size acquisition method eliminates the need for complex measurement and identification processes, making it more efficient. However, it places certain requirements on the trailer; the trailer must be equipped with the corresponding smart tag, and the main vehicle must also have the ability to communicate with the tag.

[0040] It should be noted that in practical applications, those skilled in the art can use any one of the above methods or a combination thereof to obtain dimensional information, and can also choose other methods of obtaining dimensional information according to application requirements, and are not limited to these.

[0041] The above technical solutions provide multiple ways to obtain trailer dimensions, including detecting trailer outlines, feature matching, and communicating with the trailer to obtain dimensions. Traditional systems do not adequately consider trailer dimensions, which affect the overall handling of the vehicle and safety in different scenarios. For example, larger trailers require a larger turning radius when turning. Understanding trailer dimensions can more accurately adjust parameters of functions such as lane keeping, avoiding dangers caused by misjudgment.

[0042] Step S200: Obtain the current driving scenario and the abnormality level of the trailer posture, and output the risk level based on the trailer weight, trailer size, current driving scenario and abnormality level of the trailer posture.

[0043] In some embodiments, the current driving scenario is identified using the vehicle's front-view camera, surround view system, high-precision map and navigation information, such as narrow parking spaces (≤3.5m), slopes, curves, tunnels, etc., as well as traffic complexity (such as traffic density and pedestrian activity). It also includes identifying driving modes such as "parking scenario", "urban congestion", and "highway cruising" through AI vision algorithms, thereby obtaining scenario parameters such as parking space width, road type and / or curvature.

[0044] The process of obtaining the trailer attitude anomaly level includes: detecting the trailer attitude; wherein the trailer attitude includes at least the sway angle, sway frequency and yaw rate; comparing the trailer attitude with the corresponding attitude threshold to obtain the current trailer attitude anomaly level based on the comparison result.

[0045] In some embodiments, the trailer attitude is detected based on the relative motion between the master vehicle and the trailer by using the trajectory tracking of the rear-view camera of the master vehicle and the fusion of inertial sensors. Kalman filtering or deep learning models are then used to predict the attitude to obtain the trailer attitude anomaly level, such as stable attitude, slight swaying, or severe swaying.

[0046] In the above technical solution, the abnormal attitude level of the trailer (such as abnormal swing angle, swing frequency and yaw rate) reflects the stability of the trailer, which is more conducive to the refinement of the risk level output mechanism.

[0047] Furthermore, in some embodiments, the risk level assessment dimensions include trailer weight, trailer size, current driving scenario, and trailer posture abnormality level. For example, the trailer weight assessment classification includes light (less than 1.5t), medium (1.5 to 3.5t), and heavy (greater than 3.5t), the trailer size assessment classification includes standard / extra-long / extra-wide, and the driving scenario classification includes parking / highway / city. The risk level is then output by combining the risk values ​​of each dimension, such as L1-L5.

[0048] Low-risk level L1: When all assessment dimensions are in a relatively safe state, it is judged as low risk; for example, in high-speed cruising scenarios, the trailer is lightweight, of standard size, and has a stable posture. In this case, the vehicle has good handling and stability, and the possibility of an accident is low.

[0049] Low risk level L2: Most assessment dimensions are in a safe state, but there is a slight risk in one dimension; for example, when driving on urban roads, the trailer is lightweight, of standard size, and its posture is slightly swaying. Although the trailer posture is somewhat abnormal, the overall risk is still at a low level.

[0050] Medium risk level L3: Some assessment dimensions have certain risks, or multiple dimensions have slight risks; for example, when parking on a slope, the trailer is of medium weight, standard size, and slightly swaying. In this case, the risks of each dimension are superimposed, making the overall risk level medium.

[0051] High-risk level L4: One or more assessment dimensions have high risk; such as parking in a narrow space, heavy trailer weight, or violent swaying posture. Even if the trailer size is standard, the overall risk will increase significantly due to the high-risk factors in other dimensions.

[0052] High-risk level L5: All assessment dimensions are at high risk; such as narrow parking spaces, heavy loads and violent swaying of trailers. In such cases, the probability of an accident is extremely high and the degree of harm is also very high, so it is judged as high risk.

[0053] It should be noted that the assessment criteria for each risk level need to be confirmed by those skilled in the art based on the current driving scenario. The above is just an example and is not limited to this.

[0054] Step S300: Execute the corresponding driving assistance function suppression strategy according to the risk level.

[0055] In some embodiments, such as in low-risk scenarios, some driver assistance functions can be appropriately retained to improve function availability and reduce driver fatigue; for example, on highways, if the risk level is low, adaptive cruise control can be activated; while in high-risk scenarios, the suppression of functions is strengthened to ensure towing safety; the driver assistance function suppression strategies corresponding to each risk level are as follows:

[0056] It should be noted that the dynamic suppression method for driving assistance functions in this application supports phased suppression, such as gradually reducing the availability of the function from L3 to L4, and also supports abnormal trigger enhancement, such as automatically increasing the risk level and temporarily activating a stronger suppression strategy when trailer sway is detected.

[0057] Before implementing the driving assistance function suppression strategy, the method further includes: performing dual-channel verification based on vehicle sensors; if the verification is successful, executing the corresponding driving assistance function suppression strategy according to the risk level; otherwise, determining that the driving assistance system is faulty, and executing the driving assistance function suppression strategy corresponding to the lowest risk level.

[0058] In some embodiments, taking AEB as an example, the system will simultaneously analyze the data collected by different sensors. Only when the data of the two sensors match will the hardware-level detection result be considered valid. Alternatively, the vehicle will use multiple sensors of the same type for redundancy design. By comparing the data collected by the two sensors, the system can determine whether a fault has occurred.

[0059] In addition, it should be noted that the vehicle domain controller enables precise invocation and deactivation of ADAS functions; and at the software level, middleware (such as AUTOSAR) enables atomic control of function start-up and shutdown; and at the hardware level, redundancy and safety of critical paths (such as braking control) are ensured.

[0060] The above technical solution improves the reliability of the system, avoids erroneous decisions caused by sensor failure or misjudgment, and executes the strategy corresponding to the lowest risk level when the driver assistance system is determined to be faulty, thus ensuring the basic safety of the vehicle.

[0061] Furthermore, when implementing the driving assistance function suppression strategy, it also includes: displaying the currently suppressed driving assistance function in real time and displaying the current risk level; and, when the risk level increases, executing a voice warning.

[0062] In some embodiments, the instrument panel / central control screen displays "Tow mode activated" and indicates the risk level with color (green / yellow / red), while also displaying a list of currently suppressed functions (such as "AEB is off").

[0063] Furthermore, in other embodiments, when the risk level increases, a warning sound is emitted, such as "Trailer sway detected, please take control."

[0064] In addition, other embodiments also support voice interaction, such as voice queries: "Which functions are currently available?", "Why is AEB turned off?".

[0065] The above technical solutions enhance human-computer interaction, allowing drivers to clearly understand the system's working status and reducing operational confusion and psychological gap; at the same time, voice warnings can promptly remind drivers to pay attention to potential risks, improving driving safety.

[0066] Furthermore, the dynamic suppression method for driving assistance functions also includes: collecting user behavior data in real time, and optimizing the assessment threshold and / or driving assistance function suppression strategy corresponding to the risk level based on the user behavior data.

[0067] In some embodiments, user behavior data can be collected via OTA, and suppression strategy parameters and algorithm models can be optimized through remote upgrades.

[0068] In the above technical solution, as users spend more time using the system, the system can be adjusted according to the user's habits and actual situation, further improving the system's adaptability and performance, and achieving a personalized driving assistance experience.

[0069] In summary, the dynamic suppression method for driving assistance functions described in this application achieves a dynamic, intelligent, and refined upgrade of the suppression strategy for driving assistance functions in trailer mode. It not only solves the functional deficiencies and safety hazards caused by the traditional "one-size-fits-all" suppression, but also achieves a dynamic balance between safety and availability through a risk-driven mechanism, demonstrating significant technological advancement and commercial potential.

[0070] Based on the same concept, please refer to Figure 2 This application also provides a driving assistance system that employs the aforementioned dynamic suppression method for driving assistance functions to implement a corresponding driving assistance function suppression strategy. The driving assistance system includes: a multi-sensor layer for real-time monitoring of the rear load of the vehicle and obtaining the trailer size, current driving scenario, and trailer attitude; a real-time status assessment module for estimating the trailer weight based on the rear load and obtaining the trailer attitude anomaly level based on the trailer attitude; a risk level dynamic assessment engine for outputting a risk level based on the trailer weight, trailer size, current driving scenario, and trailer attitude anomaly level; and a dynamic suppression strategy decision center for executing the corresponding driving assistance function suppression strategy according to the risk level.

[0071] For further details, please see Figure 3The driving assistance system further includes: a function suppression and cooperative execution module, used to perform dual-channel verification based on vehicle sensors, so as to drive the dynamic suppression strategy decision center based on the verification results; a human-machine interaction and status feedback module, used to display the currently suppressed driving assistance functions in real time, display the current risk level, and execute voice warnings when the risk level increases; and also used to collect user behavior data in real time, so as to optimize the judgment threshold and / or driving assistance function suppression strategy corresponding to the risk level based on the user behavior data.

[0072] It should be noted that the driving assistance system uses the dynamic suppression method for driving assistance functions to achieve its related functions. For details on its implementation, please refer to the dynamic suppression method for driving assistance functions, which will not be elaborated here.

[0073] In the above technical solution, through the collaborative work of modules such as the multi-sensor layer, real-time status assessment module, risk level dynamic assessment engine, and dynamic suppression strategy decision center, the dynamic, intelligent, and refined upgrade of the driver assistance function suppression strategy in trailer mode is realized. The various modules cooperate with each other to form a complete closed-loop system. From data collection and risk assessment to strategy execution and optimization, it comprehensively solves the problems of traditional systems at the human-machine collaboration level, and has significant technological advancement and commercial potential.

[0074] Based on the same concept, please refer to Figure 4 This application also provides a domain controller, which is used to realize the coordinated operation and optimization of various driving assistance functions of the driving assistance system.

[0075] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0078] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0079] 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] Although the description of this application has been made in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.

Claims

1. A method of dynamically suppressing a driving assistance function, characterized by, The method comprises the following steps: monitoring the rear load of the host vehicle in real time, estimating the trailer weight based on the rear load, and obtaining the trailer size; obtaining the current driving scene and the trailer attitude abnormality level, and outputting the risk level based on the trailer weight, the trailer size, the current driving scene, and the trailer attitude abnormality level; and executing the corresponding driving assistance function inhibition strategy according to the risk level.

2. The method of claim 1, wherein, The estimation of the trailer weight based on the rear load comprises: obtaining the traction force and acceleration data of the host vehicle, and estimating the trailer weight in combination with the traction force, the acceleration data, and the rear load.

3. The method of claim 1, wherein, The obtaining of the trailer size comprises: detecting the trailer contour in real time, and obtaining the trailer size according to the trailer contour; or, detecting the trailer features in real time, matching the trailer features based on a preset trailer type library, and obtaining the trailer size according to the matching result; or, communicating with the trailer through a preset communication technology to obtain the trailer size preloaded in the trailer tag.

4. The method of claim 1, wherein, The obtaining of the trailer attitude abnormality level comprises: detecting the trailer attitude, wherein the trailer attitude at least includes the swing angle, the swing frequency, and the yaw rate; comparing the trailer attitude with the corresponding attitude threshold, and obtaining the current trailer attitude abnormality level based on the comparison result.

5. The method of claim 1, wherein, Before executing the driving assistance function inhibition strategy, the method further comprises: performing a double-channel verification based on the vehicle sensors, and when the verification is passed, executing the corresponding driving assistance function inhibition strategy according to the risk level; otherwise, determining that the driving assistance system has a fault, and executing the driving assistance function inhibition strategy corresponding to the lowest risk level.

6. The method of claim 5, wherein, When executing the driving assistance function inhibition strategy, the method further comprises: displaying the current driving assistance function being inhibited and the current risk level in real time; and when the risk level rises, executing a voice warning.

7. The method of claim 1, wherein, The method further comprises: collecting user behavior data in real time, and optimizing the evaluation threshold corresponding to the risk level and / or the driving assistance function inhibition strategy based on the user behavior data.

8. A driving assistance system characterized by comprising: The corresponding driving assistance function inhibition strategy is implemented by using the driving assistance function dynamic inhibition method according to any one of claims 1-7, and the driving assistance system comprises: a multi-element perception layer for monitoring the rear load of the host vehicle in real time, and obtaining the trailer size, the current driving scene, and the trailer attitude; a real-time state evaluation module for estimating the trailer weight based on the rear load, and obtaining the trailer attitude abnormality level based on the trailer attitude; a risk level dynamic evaluation engine for outputting the risk level based on the trailer weight, the trailer size, the current driving scene, and the trailer attitude abnormality level; and a dynamic inhibition strategy decision center for executing the corresponding driving assistance function inhibition strategy according to the risk level.

9. The driving assist system according to claim 8, characterized by, The driving assistance system further comprises: a function inhibition and coordination execution module for performing a double-channel verification based on the vehicle sensors, and driving the dynamic inhibition strategy decision center based on the verification result; A human-machine interaction and state feedback module is configured to display the current inhibited driving assistance function and the current risk level in real time, and to execute a voice warning when the risk level rises; and to collect user behavior data in real time, so as to optimize the evaluation threshold corresponding to the risk level and / or the driving assistance function inhibition strategy based on the user behavior data.

10. A domain controller, characterized by The domain controller is configured to realize the cooperative operation and optimization of each driving assistance function of the driving assistance system according to any one of claims 8-9.

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