Steering system experimental verification method and system, electronic equipment and computer readable medium

Abstract

The invention provides an experimental verification method for a steering system, and belongs to the field of automobiles, and the method comprises the steps: analyzing an identification risk through HARA, building an experimental platform according to the identification risk, judging whether an experimental condition is met or not based on the HARA analysis result and the state of the experimental platform, and carrying out the verification experiment of the steering system through the experimental platform when the experimental condition is met. The HARA analysis and the experiment platform are optimized according to the verification experiment data; according to the method, through HARA analysis, an experimental platform is built, and dynamic risk assessment data under the real vehicle working condition are combined, so that an analysis-test-optimization closed-loop verification system is realized, and the controllability and safety of the EPS system under the large-corner working condition are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method, system, electronic device, and computer-readable medium for experimental verification of a steering system. Background Technology

[0002] The safety performance of intelligent driving vehicles is inseparable from the reliable operation of the steering control system. As a core component of vehicle steering control, the controllability of the electric power steering (EPS) system under large steering angles directly affects the vehicle's driving safety level. However, traditional EPS control strategies have certain shortcomings, exhibiting nonlinear fluctuations in steering torque under large steering angles, leading to a decrease in control accuracy.

[0003] While the existing ISO 26262 Hazard Analysis and Risk Assessment (HARA) method is widely used in automotive safety analysis, it is largely based on theoretical models and lacks dynamic risk assessment data support under real-world vehicle conditions. Furthermore, existing testing protocols fail to establish a quantitative mapping relationship between steering failure modes and road scenarios, resulting in insufficient coverage of testing scenarios. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method and system for experimental verification of steering systems.

[0005] In a first aspect, the present invention provides an experimental verification method for a steering system, comprising:

[0006] Risks are identified through HARA analysis;

[0007] An experimental platform was built based on the identified risks.

[0008] Determine whether the experimental conditions are met, and if they are met, conduct a verification experiment on the steering system using an experimental platform;

[0009] The HARA analysis and experimental platform were optimized based on the validation experimental data.

[0010] In some embodiments, the risk identification via HARA analysis includes:

[0011] Determine the risk level based on key risk parameters;

[0012] For different risk levels, determine the corresponding reversal compensation strategy;

[0013] A risk matrix for large turning angle conditions is constructed based on the steering compensation strategy.

[0014] In some embodiments, the step of building the experimental platform based on the identified risks includes:

[0015] The hardware includes an EPS actuator, a torque sensor array, and an inertial navigation unit. The EPS actuator is used to provide power steering, the torque sensor array is used to monitor steering torque fluctuations, and the inertial navigation unit is used to provide real-time vehicle attitude, acceleration, and position data.

[0016] Software is developed to fuse hardware-in-the-loop simulation data with real vehicle test data. The fused data is then used to verify the real-time risk prediction model.

[0017] In some embodiments, determining whether the experimental conditions are met includes:

[0018] Assess the risks of real-vehicle testing. If the risk level is low, conduct real-vehicle testing; if the risk level is high, conduct bench testing to determine the feasibility.

[0019] Determine the feasibility of bench testing. If the feasibility is met, conduct the bench test; if the feasibility is not met, indicate that there are no test conditions in the risk matrix of the large turning angle condition.

[0020] In some embodiments, optimizing the HARA analysis and the experimental platform based on validation experimental data includes:

[0021] Analyze and verify the experimental data to evaluate the accuracy of the experimental platform's status;

[0022] Optimize the risk matrix and compensation strategy parameters;

[0023] Based on the optimization results, the HARA analysis was updated.

[0024] In some embodiments, the key risk parameters include vehicle speed and road surface adhesion coefficient, and the combination of vehicle speed and road surface adhesion coefficient is mapped to a risk level.

[0025] In some embodiments, the determination of whether the experimental conditions are met further includes:

[0026] When experimental conditions are not met, the risk matrix indicates that there are no test conditions.

[0027] Secondly, the present invention also provides a steering system experimental verification system, comprising:

[0028] The risk module is used to identify risks through HARA analysis;

[0029] The experimental platform module is used to build an experimental platform based on the identified risks.

[0030] The judgment module determines whether the experimental conditions are met, and if they are met, the experimental platform is used to conduct a verification experiment on the steering system.

[0031] The optimization module optimizes the HARA analysis and the experimental platform based on the validation experimental data.

[0032] Thirdly, the present invention also provides an electronic device, comprising:

[0033] One or more processors;

[0034] Memory, used to store one or more programs;

[0035] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods.

[0036] Fourthly, the present invention also provides a computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps in any of the methods described.

[0037] The experimental verification method for steering systems provided by this invention identifies risks through HARA analysis, builds an experimental platform based on the identified risks, and determines whether to conduct a verification experiment based on the results of the HARA analysis and the state of the experimental platform. If so, the HARA analysis and the experimental platform are optimized based on the verification experiment data. This invention effectively solves the problem that existing HARA analyses are mostly based on theoretical models and lack support from real vehicle conditions by using HARA analysis and building an experimental platform, combined with dynamic risk assessment data under real vehicle conditions. By proposing a closed-loop verification system of "analysis-testing-optimization", it achieves the organic integration of HARA analysis, real vehicle testing, and control strategy optimization, improving the controllability and safety of EPS systems under large steering angle conditions. This method effectively solves the problems of traditional EPS, provides dynamic risk assessment and quantitative mapping relationships, and is suitable for the development and verification of intelligent driving systems. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the steps of an embodiment of the experimental verification method for the steering system provided by the present invention;

[0039] Figure 2 This is a schematic diagram of the HARA analysis process for identifying risks in the experimental verification method for steering systems provided by this invention.

[0040] Figure 3 This is a flowchart illustrating the optimization of the HARA analysis and experimental platform for the steering system experimental verification method provided by this invention.

[0041] Figure 4 This is a schematic diagram of the structure of an embodiment of the steering system experimental verification system of the present invention;

[0042] Figure 5This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0044] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0045] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0046] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0047] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0048] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0049] In related technologies, there are problems such as nonlinear fluctuations in steering torque in traditional electric power steering systems under large steering angle conditions, lack of dynamic risk assessment data support under real vehicle conditions in existing HARA analysis, and failure of existing test schemes to establish a quantitative mapping relationship between steering failure modes and road scenarios.

[0050] This technical solution mainly involves keywords and terms such as "functional safety", "HARA", "controllability", "simulation model" and "functional safety test".

[0051] Functional safety: In this patent, functional safety of road vehicles refers to addressing the unreasonable risks that could lead to personal injury caused by the failure of the electronic and electrical systems of road vehicles.

[0052] HARA analysis: Hazard analysis and risk assessment identifies and classifies hazardous events caused by functional abnormalities in relevant items. It determines safety objectives and corresponding ASIL levels to prevent hazardous events from occurring or to mitigate their severity by determining S (Severity of Harm), E (Exposure), and C (Controllability) values, in order to avoid unreasonable risks.

[0053] Controllability: To determine the controllability level of a given hazard, it is necessary to estimate the likelihood that a representative driver or other involved personnel could influence the situation to avoid harm. This likelihood estimate includes the probability that a representative driver could maintain or regain control of the vehicle if the given hazard were to occur, or the probability that individuals within the hazard's vicinity could avoid the hazard through their actions. This consideration is based on the assumption that individuals in the hazard scenario would take necessary control actions to maintain or regain control of the current situation, and that the involved drivers would take representative driving actions. According to the ISO 26262 standard, the classification is as follows: C0: Controllable; C1: Simple Controllable; C2: Generally Controllable; C3: Difficult to Control or Uncontrollable.

[0054] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a real-vehicle experimental verification method. Figure 1 A flowchart illustrating the steps of an experimental verification method for a steering system provided in an embodiment of the present invention.

[0055] like Figure 1 As shown, the experimental verification method for this steering system includes the following steps:

[0056] Step S10: Identify risks through HARA analysis.

[0057] It should be noted that this invention provides a theoretical basis for subsequent experiments by systematically assessing the risk level under large turning angle conditions and formulating corresponding steering compensation strategies. The steps include:

[0058] Step 101: Determine the risk level based on key risk parameters.

[0059] Specifically, vehicle speed and road surface adhesion coefficient are selected as key risk parameters because these parameters directly affect vehicle stability and steering controllability.

[0060] Vehicle speed is divided into three levels: low speed (less than 40 km / h), medium speed (40-80 km / h), and high speed (greater than 80 km / h).

[0061] Road surface adhesion coefficient is divided into three levels: dry road surface (greater than 0.8), wet and slippery road surface (0.3-0.8), and icy and snowy road surface (less than 0.3).

[0062] Risk level classification: The combination of vehicle speed and road surface adhesion coefficient is mapped to a risk level (e.g., low, medium, high). For example, high speed combined with icy or snowy road surface is high risk, while low speed combined with dry road surface is low risk. Risk level can be initially determined through experience or simulation data.

[0063] Step 102: Determine the corresponding reversal compensation strategy for different risk levels.

[0064] Specifically, this embodiment designs different EPS control strategies based on the risk level to offset nonlinear fluctuations under large turning angles.

[0065] High-risk levels (such as highways and icy roads): Activate dynamic damping compensation (increase steering resistance to suppress oscillations) and enable advance control in conjunction with ESC (electronic stability control) (adjust steering in advance using forward path information).

[0066] Medium risk level (e.g., medium speed + slippery road surface): Increase the steering damping coefficient (to improve steering stability).

[0067] Low-risk levels (e.g., low speed + dry road surface): may not require special compensation or may only use basic damping.

[0068] It is understandable that each compensation strategy includes adjustable parameters (such as damping coefficient, aiming time, etc.), with initial values ​​set based on simulation or historical data.

[0069] Step 103: Construct a risk matrix for large turning angle conditions based on the steering compensation strategy.

[0070] Specifically, the risk matrix for large corner driving conditions includes: creating a two-dimensional table where rows represent vehicle speed levels and columns represent road surface adhesion coefficient levels, with each cell containing the corresponding risk level and compensation strategy. For example:

[0071] It is understandable that this matrix is ​​embedded in the software model as a query table triggered by real-time risk prediction and compensation strategies.

[0072] It is understood that in this embodiment, step 103 integrates the outputs of steps 101 and 102 to form a complete HARA analysis module, which serves as the benchmark for subsequent experimental system construction and optimization.

[0073] Step S20: Based on the identified risks, build an experimental platform, which specifically includes two parts: building hardware and building software.

[0074] It is understood that this embodiment constructs a platform capable of performing verification experiments, with hardware used for data acquisition and execution control, and software used for real-time risk assessment and decision-making, specifically including the following steps:

[0075] Step 201: Set up the hardware, which includes a dual redundant EPS actuator, a six-dimensional torque sensor array, and a high-precision inertial navigation unit.

[0076] Specifically, the dual-redundant EPS actuator has two systems, a primary system and a backup system. When the primary system fails, the backup system automatically takes over, improving safety. The dual-redundant EPS actuator is mounted on the steering column to provide power steering.

[0077] A six-dimensional torque sensor array, consisting of six sensors placed at key points in the steering system, measures forces and torques in three directions (six-dimensional data) to monitor nonlinear fluctuations in steering torque.

[0078] The high-precision inertial navigation unit provides real-time vehicle attitude (such as yaw angle and roll angle), acceleration, and position data with an accuracy of centimeter level.

[0079] Furthermore, all hardware is connected via a CAN bus to ensure real-time data transmission and synchronization.

[0080] It is understandable that the hardware system provides real-vehicle data input to the software model and executes compensation strategies. Hardware redundancy design ensures experimental safety and is the foundation of real-vehicle testing.

[0081] Step 202: Build the software, which includes a real-time risk prediction model based on Bayesian networks.

[0082] Specifically, the model algorithm: the risk score formula is: Risk_Score = α*(δ / δ_max) + β*(V / V_max) + γ*(μ / μ_min), where:

[0083] δ is the current steering angle, and δ_max is the maximum steering angle;

[0084] V represents the current vehicle speed, and V_max represents the maximum design speed.

[0085] μ is the current road surface adhesion coefficient, and μ_min is the minimum adhesion coefficient;

[0086] α, β, and γ are weighting coefficients, with initial values ​​based on historical data or simulation fitting (e.g., α=0.5, β=0.3, γ=0.2).

[0087] As can be understood, this embodiment employs a Bayesian network to handle the uncertainty of sensor data and updates the risk score through probabilistic inference. The network structure includes nodes (such as vehicle speed and adhesion coefficient) and a conditional probability table to calculate the risk level in real time. The model is embedded in a real-time operating system, reads data from the hardware, calculates the Risk_Score, and queries the risk matrix to trigger compensation strategies.

[0088] It is understandable that the software model uses hardware data to assess risk in real time, serving as the execution layer for the risk matrix in step 103. The model output directly controls the EPS actuator, achieving closed-loop control.

[0089] Step 203: Use a time synchronization algorithm to fuse the hardware-in-the-loop simulation data and the verification experimental data. The fused data is used to verify the real-time risk prediction model.

[0090] Specifically, time synchronization algorithms (such as GPS timestamps) are used to align hardware-in-the-loop (HIL) simulation data with validation experimental data. HIL simulations model extreme scenarios (such as low-adhesion road surfaces), while real-vehicle data from validation experiments provides realistic operating conditions. The fused data is used to validate the risk prediction model, for example, by comparing HIL simulation risk scores with real-vehicle risk scores and calibrating model parameters.

[0091] It is understandable that communication between the HIL simulator and the actual vehicle system is achieved through a data bus (such as Ethernet) to ensure real-time data exchange.

[0092] It should be noted that step 203 enhances the reliability of the model in step 202 by integrating simulation and real vehicle data, thereby improving the accuracy of risk assessment and providing multi-source data support for the optimization of subsequent steps.

[0093] It is understood that this embodiment establishes a quantitative mapping relationship between steering failure modes and road scenarios by constructing a hardware-in-the-loop (HIL) and verification experiment synchronous data fusion technology, thereby improving the coverage and efficiency of extreme scenario testing.

[0094] Step S30: Determine whether the experimental conditions are met, and if they are met, use the experimental platform to conduct a verification experiment on the steering system.

[0095] Specifically, this includes: assessment of the risks of real-vehicle testing and assessment of the feasibility of bench testing, with the assessment of the risks of real-vehicle testing having a higher priority than the assessment of the feasibility of bench testing.

[0096] Risk assessment of real vehicle testing: The risk assessment is based on the risk matrix of the large turning angle condition, wherein the risk level of the large turning angle condition risk matrix is ​​determined according to key risk parameters including vehicle speed, turning speed and turning angle; when the risk level is low, real vehicle testing is conducted; when the risk level is high, bench driving test feasibility assessment is performed.

[0097] Specifically, experiments involving large turns (180 degrees or more) at high speeds exceeding 60 kph could easily cause vehicle instability and injuries in real vehicles, and the process of finding the boundary value may not be easy to achieve safely.

[0098] Feasibility assessment of bench testing: The assessment is based on the hardware and software status of the experimental platform, such as whether bench testing resources are available, whether the bench testing model can be built, and whether the real vehicle data has practical reference value. If bench testing is feasible, bench testing is conducted, and the bench testing data is used to optimize the HARA analysis and the experimental platform. If bench testing is not feasible, the process returns to the step of identifying risks through HARA analysis, and no testing conditions are noted in the large turning angle condition risk matrix.

[0099] Step S40: Optimize the HARA analysis and the experimental platform based on the validation experimental data.

[0100] Specifically, this embodiment utilizes validation experimental data to verify and improve the HARA analysis module and EPS control strategy, forming an iterative optimization. The specific steps include:

[0101] Step 401: Analyze and verify the experimental data (real vehicle experimental data and bench test data) to evaluate the accuracy of the experimental platform status.

[0102] Specifically, in real-vehicle tests, data such as vehicle speed, steering angle, adhesion coefficient, and vehicle response (e.g., yaw rate) are recorded.

[0103] Furthermore, compare the risk level predicted by the model with the actual risk level (actual risk is based on vehicle behavior indicators, such as whether there is a tendency to lose control). Calculate the deviation rate; if the deviation rate exceeds a threshold (e.g., 10%), the model needs to be optimized.

[0104] Step 402: Optimize the risk matrix and compensation strategy parameters.

[0105] Specifically, this includes: risk matrix optimization, including adjusting the risk level classification threshold. For example, based on the verification experiment data, it was found that the risk is higher on medium-speed wet and slippery roads, so the upper limit of medium speed can be reduced from 80km / h to 70km / h.

[0106] Optimize compensation strategy parameters: Refit the weighting coefficients α, β, and γ using real vehicle data (e.g., through regression analysis), or adjust the compensation strategy parameters (e.g., dynamic damping coefficient).

[0107] The optimization methods described above can use machine learning algorithms (such as gradient descent) or design of experiments (DOE) to find the optimal parameters.

[0108] Step 403: Update the HARA analysis based on the optimization results.

[0109] It is understandable that the optimized risk matrix, model parameters, and compensation strategies are uploaded to the EPS controller and software model to replace the old version.

[0110] After the update, step S30 can be performed again to form a continuous improvement loop. For example, the updated system can be tested in a simulator, and then a decision can be made as to whether further real-vehicle testing is needed.

[0111] It is understandable that step 403 is both the end of the iteration and the beginning of a new one. The updated system is fed back to step S10 to ensure that the method is adaptive. The entire process continuously refines the HARA analysis through real vehicle data to improve the reliability of the EPS system.

[0112] It is understandable that the above method is not linear, but cyclical. Through multiple iterations, the HARA analysis module and experimental system are gradually improved. By using the above method, the controllability and safety of the EPS system under large turning angle conditions can be effectively improved, the risk of steering loss of control can be reduced, and the coverage and efficiency of extreme scenario testing can be increased.

[0113] The real-vehicle experimental verification method provided by this invention identifies risks through HARA analysis, builds an experimental platform based on the identified risks, and determines whether to conduct a verification experiment based on the results of the HARA analysis and the state of the experimental platform. If so, the HARA analysis and the experimental platform are optimized based on the verification experiment data. This invention, through HARA analysis and the construction of an experimental platform, can combine dynamic risk assessment data under real-vehicle conditions, effectively solving the problem that existing HARA analyses are mostly based on theoretical models and lack real-vehicle condition support. By proposing a closed-loop verification system of "analysis-testing-optimization", it achieves the organic integration of HARA analysis, experimental testing, and control strategy optimization, improving the controllability and safety of EPS systems under large turning angle conditions. This method effectively solves the problems of traditional EPS, provides dynamic risk assessment and quantitative mapping relationships, and is suitable for the development and verification of intelligent driving systems.

[0114] Please see Figure 4 The present invention also provides a steering system experimental verification system, which is applied to the experimental verification method provided in the above embodiments, and specifically includes: a risk module, an experimental platform module, a judgment module, and an optimization module.

[0115] The risk module is used to identify risks through HARA analysis.

[0116] It should be noted that this invention provides a theoretical basis for subsequent experiments by systematically assessing the risk level under large turning angle conditions and formulating corresponding steering compensation strategies.

[0117] The risk level is determined based on key risk parameters.

[0118] Specifically, vehicle speed and road surface adhesion coefficient are selected as key risk parameters because these parameters directly affect vehicle stability and steering controllability.

[0119] Vehicle speed is divided into three levels: low speed (less than 40 km / h), medium speed (40-80 km / h), and high speed (greater than 80 km / h).

[0120] Road surface adhesion coefficient is divided into three levels: dry road surface (greater than 0.8), wet and slippery road surface (0.3-0.8), and icy and snowy road surface (less than 0.3).

[0121] Risk level classification: The combination of vehicle speed and road surface adhesion coefficient is mapped to a risk level (e.g., low, medium, high). For example, high speed combined with icy or snowy road surface is high risk, while low speed combined with dry road surface is low risk. Risk level can be initially determined through experience or simulation data.

[0122] Determine corresponding reversal compensation strategies based on different risk levels.

[0123] Specifically, this embodiment designs different EPS control strategies based on the risk level to offset nonlinear fluctuations under large turning angles.

[0124] High-risk levels (such as highways and icy roads): Activate dynamic damping compensation (increase steering resistance to suppress oscillations) and enable advance control in conjunction with ESC (electronic stability control) (adjust steering in advance using forward path information).

[0125] Medium risk level (e.g., medium speed + slippery road surface): Increase the steering damping coefficient (to improve steering stability).

[0126] Low-risk levels (e.g., low speed + dry road surface): may not require special compensation or may only use basic damping.

[0127] It is understandable that each compensation strategy includes adjustable parameters (such as damping coefficient, aiming time, etc.), with initial values ​​set based on simulation or historical data.

[0128] A risk matrix for large turning angle conditions is constructed based on the steering compensation strategy.

[0129] Specifically, the risk matrix for large corner driving conditions includes: creating a two-dimensional table where rows represent vehicle speed levels and columns represent road surface adhesion coefficient levels, with each cell containing the corresponding risk level and compensation strategy. For example:

[0130] It is understandable that this matrix is ​​embedded in the software model as a query table triggered by real-time risk prediction and compensation strategies.

[0131] The experimental platform module is used to build an experimental platform based on the identified risks, and includes hardware and software components.

[0132] It is understood that this embodiment constructs a platform capable of performing real-vehicle experiments, with hardware used for data acquisition and execution control, and software used for real-time risk assessment and decision-making.

[0133] The hardware components include dual redundant EPS actuators, a six-dimensional torque sensor array, and a high-precision inertial navigation unit.

[0134] Specifically, the dual-redundant EPS actuator has two systems, a primary system and a backup system. When the primary system fails, the backup system automatically takes over, improving safety. The dual-redundant EPS actuator is mounted on the steering column to provide power steering.

[0135] A six-dimensional torque sensor array, consisting of six sensors placed at key points in the steering system, measures forces and torques in three directions (six-dimensional data) to monitor nonlinear fluctuations in steering torque.

[0136] The high-precision inertial navigation unit provides real-time vehicle attitude (such as yaw angle and roll angle), acceleration, and position data with an accuracy of centimeter level.

[0137] Furthermore, all hardware is connected via a CAN bus to ensure real-time data transmission and synchronization.

[0138] It is understandable that the hardware system provides the software model with experimental data input for verification and executes compensation strategies. Hardware redundancy design ensures experimental safety and is fundamental to the experiment.

[0139] The software component includes a real-time risk prediction model based on Bayesian networks.

[0140] Specifically, the model algorithm: the risk score formula is: Risk_Score = α*(δ / δ_max) + β*(V / V_max) + γ*(μ / μ_min), where:

[0141] δ is the current steering angle, and δ_max is the maximum steering angle;

[0142] V represents the current vehicle speed, and V_max represents the maximum design speed.

[0143] μ is the current road surface adhesion coefficient, and μ_min is the minimum adhesion coefficient;

[0144] α, β, and γ are weighting coefficients, with initial values ​​based on historical data or simulation fitting (e.g., α=0.5, β=0.3, γ=0.2).

[0145] As can be understood, this embodiment employs a Bayesian network to handle the uncertainty of sensor data and updates the risk score through probabilistic inference. The network structure includes nodes (such as vehicle speed and adhesion coefficient) and a conditional probability table to calculate the risk level in real time. The model is embedded in a real-time operating system, reads data from the hardware, calculates the Risk_Score, and queries the risk matrix to trigger compensation strategies.

[0146] It is understandable that the software model uses hardware data to assess risk in real time, serving as the execution layer for the risk matrix in step 103. The model output directly controls the EPS actuator, achieving closed-loop control.

[0147] The hardware-in-the-loop simulation data and real vehicle test data are fused using a time synchronization algorithm, and the fused data is used to verify the real-time risk prediction model.

[0148] The judgment module determines whether the experimental conditions are met. If they are met, a verification experiment is conducted on the steering system using an experimental platform. This includes a risk assessment for real-vehicle testing and a feasibility assessment for bench testing, with the risk assessment for real-vehicle testing having a higher priority than the feasibility assessment for bench testing.

[0149] An optimization module is used to optimize the HARA analysis and the experimental platform based on real-vehicle test data.

[0150] Specifically, this embodiment uses real-vehicle test data to verify and improve the HARA analysis module and EPS control strategy, forming an iterative optimization. The specific steps include the following:

[0151] Analyze real-vehicle test data to evaluate the accuracy of the risk prediction model.

[0152] Specifically, in real-vehicle tests, data such as vehicle speed, steering angle, adhesion coefficient, and vehicle response (e.g., yaw rate) are recorded.

[0153] Furthermore, the risk level predicted by the model is compared with the actual risk level (the actual risk is based on vehicle behavior indicators, such as whether there is a tendency to lose control).

[0154] The HARA analysis was optimized based on real-vehicle test data.

[0155] Specifically, this includes: risk matrix optimization, including adjusting the risk level classification thresholds. For example, based on real vehicle data, it was found that the risk is higher on medium-speed wet and slippery roads, so the upper limit of medium speed can be reduced from 80km / h to 70km / h.

[0156] Optimize compensation strategy parameters: Refit the weighting coefficients α, β, and γ using real vehicle data (e.g., through regression analysis), or adjust the compensation strategy parameters (e.g., dynamic damping coefficient).

[0157] The optimization methods described above can use machine learning algorithms (such as gradient descent) or design of experiments (DOE) to find the optimal parameters.

[0158] Based on the optimization results, update the HARA analysis module and control strategy.

[0159] The real-vehicle experimental verification system provided by this invention identifies risks through HARA analysis, builds an experimental platform based on the identified risks, and determines whether to conduct a real-vehicle experiment based on the results of the HARA analysis and the state of the experimental platform. If so, the HARA analysis and the experimental platform are optimized based on the real-vehicle experimental data; otherwise, the process returns to the first step. This invention, through HARA analysis and the construction of an experimental platform, combined with dynamic risk assessment data under real-vehicle conditions, effectively solves the problem that existing HARA analyses are mostly based on theoretical models and lack support from real-vehicle conditions. By proposing an "analysis-test-optimization" closed-loop verification system, it achieves the organic integration of HARA analysis, real-vehicle testing, and control strategy optimization, improving the controllability and safety of the EPS system under large turning angle conditions. This method effectively solves the problems of traditional EPS, provides dynamic risk assessment and quantitative mapping relationships, and is suitable for the development and verification of intelligent driving systems.

[0160] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the steering system experimental verification methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0161] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0162] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0163] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0164] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the real-vehicle experimental verification methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0165] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described steering system experimental verification method.

[0166] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0167] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0168] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0169] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0170] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0171] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0172] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0173] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0175] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. An experimental verification method for a steering system, characterized in that, It includes: Risks are identified through HARA analysis; An experimental platform was built based on the identified risks. Determine whether the experimental conditions are met, and if they are met, conduct a verification experiment on the steering system using an experimental platform; The HARA analysis and experimental platform were optimized based on the validation experimental data.

2. The experimental verification method for the steering system according to claim 1, characterized in that, The risk identification through HARA analysis includes: Determine the risk level based on key risk parameters; For different risk levels, determine the corresponding reversal compensation strategy; A risk matrix for large turning angle conditions is constructed based on the steering compensation strategy.

3. The experimental verification method for the steering system according to claim 2, characterized in that, The step of building an experimental platform based on the identified risks includes: The hardware includes an EPS actuator, a torque sensor array, and an inertial navigation unit. The EPS actuator is used to provide power steering, the torque sensor array is used to monitor steering torque fluctuations, and the inertial navigation unit is used to provide real-time vehicle attitude, acceleration, and position data. Software is developed to fuse hardware-in-the-loop simulation data with real vehicle test data. The fused data is then used to verify the real-time risk prediction model.

4. The experimental verification method for the steering system according to claim 2, characterized in that, The determination of whether the experimental conditions are met includes: Assess the risks of real-vehicle testing. If the risk level is low, conduct real-vehicle testing; if the risk level is high, conduct bench testing to determine the feasibility. Determine the feasibility of bench testing. If the feasibility is met, conduct the bench test; if the feasibility is not met, indicate that there are no test conditions in the risk matrix of the large turning angle condition.

5. The experimental verification method for the steering system according to claim 2, characterized in that, The optimization of the HARA analysis and the experimental platform based on the validation experimental data includes: Analyze and verify the experimental data to evaluate the accuracy of the experimental platform's status; Optimize the risk matrix and compensation strategy parameters; Based on the optimization results, the HARA analysis was updated.

6. The experimental verification method for the steering system according to claim 2, characterized in that, The key risk parameters include vehicle speed and road surface adhesion coefficient, and the combination of vehicle speed and road surface adhesion coefficient is mapped to risk level.

7. The experimental verification method for the steering system according to claim 2, characterized in that, The determination of whether the experimental conditions are met also includes: When experimental conditions are not met, the risk matrix indicates that there are no test conditions.

8. A steering system experimental verification system, characterized in that, include: The risk module is used to identify risks through HARA analysis; The experimental platform module is used to build an experimental platform based on the identified risks. The judgment module determines whether the experimental conditions are met, and if they are met, the experimental platform is used to conduct a verification experiment on the steering system. The optimization module optimizes the HARA analysis and the experimental platform based on the validation experimental data.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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