Detection method and detection device for dynamic performance attenuation state of vehicle

Abstract

The invention discloses a method and a device for detecting a vehicle dynamics performance attenuation state, and belongs to the technical field of vehicle detection.The method for detecting the vehicle dynamics performance attenuation state includes the steps: firstly, determining to-be-detected performance of a vehicle and index parameters corresponding to the to-be-detected performance, and determining a data set unit period; then, according to a data set unit period, determining a correlation signal related to the to-be-tested performance and the index parameters; screening effective sample data according to the associated signal, performing data grouping, and performing data analysis on the grouped data to obtain a data analysis result; finally, the target performance state of the vehicle is judged according to the data analysis result, whether performance degradation of the vehicle occurs or not can be judged, the dynamic performance of the vehicle is monitored and evaluated, the problem that state detection of the dynamic performance of an existing vehicle is difficult is solved, and risks or experience decline caused by performance degradation or faults of the vehicle are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle testing technology, and in particular to a method and apparatus for detecting the degradation state of vehicle dynamic performance. Background Technology

[0002] With the rapid development of the automotive industry, vehicle performance testing technology has received increasing attention. Currently, the application of big data in vehicle performance testing has matured, providing strong technical support for automakers and repair shops. Automakers can use big data technology to monitor various vehicle performance indicators in real time, while repair shops can use big data analysis to predict and diagnose faults. However, existing technologies mainly rely on system component error messages for fault analysis, which has significant limitations for performance testing of systems without messages. Especially in detecting the degradation of vehicle dynamic performance, existing technologies struggle to comprehensively and accurately assess the changing trends of key indicators such as vehicle handling performance. Traditional methods often only analyze single signals, lacking the ability to comprehensively process and correlate multi-source data, and thus failing to effectively identify early signs of performance degradation. Furthermore, existing technologies lack scientific standards for data acquisition cycles and sample selection, leading to insufficient reliability of test results. Summary of the Invention

[0003] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a method for detecting the degradation state of vehicle dynamic performance, which enables the monitoring and evaluation of vehicle dynamic performance, effectively reducing the risks or decreased experience caused by vehicle performance degradation or malfunctions.

[0004] The present invention also provides a detection device and a control device for the degradation state of vehicle dynamic performance.

[0005] A method for detecting the degradation state of vehicle dynamic performance according to a first aspect of an embodiment of the present invention includes: Determine the vehicle's performance to be tested and its corresponding index parameters, and determine the data set unit period; Based on the data set unit period, determine the associated signals related to the performance under test and the indicator parameters; Valid sample data are filtered and grouped according to the correlation signal, and the grouped data are analyzed to obtain the data analysis results. The target performance status of the vehicle is determined based on the data analysis results.

[0006] The method for detecting the degradation state of vehicle dynamic performance according to embodiments of the present invention has at least the following beneficial effects: The detection method of this invention first determines the vehicle's performance to be tested and its corresponding index parameters, and determines the data set unit period. Then, based on the data set unit period, it determines the correlation signals related to the performance to be tested and the index parameters to facilitate the screening of valid sample data. Next, it filters the valid sample data based on the correlation signals and groups the data, then performs data analysis on the grouped data to obtain the data analysis results. Finally, it determines the target performance state of the vehicle based on the data analysis results. This invention automatically detects the state of vehicle dynamics and handling performance, enabling it to determine whether the vehicle has experienced performance degradation, achieving vehicle dynamics performance monitoring and evaluation, solving the problem of existing methods for difficult state detection of vehicle dynamics performance, and effectively reducing the risks or experience degradation caused by vehicle performance degradation or malfunctions.

[0007] According to some embodiments of the present invention, determining the test performance of the vehicle and its corresponding index parameters, and determining the data set unit period, includes: The performance to be tested is determined to be the vehicle handling performance observed by detecting steering sensitivity, and the data set unit period is set. The step of determining the correlation signal related to the performance under test and the indicator parameter based on the data set unit period includes: The status information obtained based on the vehicle handling performance includes steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration. Within one data set unit cycle, data samples are taken of the steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration to obtain the associated signal containing the sampled data.

[0008] According to some embodiments of the present invention, determining the vehicle's test performance and its corresponding index parameters, and determining the data set unit period, further includes: If any item in the status information within the data set unit period is within the normal range, while any item in the past period was not within the normal range, then that item is marked as a candidate area. During data analysis, data with and without a candidate region are analyzed separately to compare and contrast the differences.

[0009] According to some embodiments of the present invention, the step of filtering valid sample data and grouping the data according to the correlation signal, and performing data analysis on the grouped data to obtain data analysis results includes: Grouped according to the vehicle speed and grouped according to the tire pressure; Analyze the effective group datasets of each group, and calculate the steering sensitivity by the input relationship between the vehicle lateral acceleration response and the steering wheel angle for each time, so as to obtain the set dataset of steering sensitivity for each group; The outlier detection method is used to determine the data characteristics of the steering sensitivity set dataset of each group within the data set unit period; By performing big data analysis on the data features, the common range of outlier percentages in the driver steering sensitivity dataset within the current period is determined, and the target range of outlier percentages for the current period is derived.

[0010] According to some embodiments of the present invention, determining the target performance state of the vehicle based on the data analysis results includes: The target performance status of the vehicle is determined by verifying the outlier percentage target range for this period and the percentage of data exceeding the basic range deviation in each segment within the data set unit period.

[0011] According to some embodiments of the present invention, determining the target performance state of the vehicle based on the data analysis results further includes: Analyze the trends and values ​​of drivers’ past outlier percentages, and predict the threshold of the current period’s percentage based on the drivers’ past percentages. By analyzing the relationship between all driver model verification and actual test data using big data, a theoretical verification baseline deviation range is set. If the percentage of outliers in any segment during the current period is greater than the theoretically verified baseline deviation range, and the percentage of outliers in the segment shows an increasing trend or has exceeded the threshold of the current period in the previous three periods, then the port is marked as pending; otherwise, all ports are marked as normal.

[0012] According to some embodiments of the present invention, the detection method further includes: The data characteristic analysis steps include: estimating the threshold for periodic outlier characteristics based on the user's past periodic data; Based on the periodic outlier characteristic threshold, the characteristics of each group of data within the current period are analyzed through the outlier relationship of data characteristics. The theoretical and experimental deviation characteristic analysis steps include: calculating the deviation threshold through big data analysis of all users using a theoretical model; Based on the aforementioned deviation threshold, analyze the relationship between the actual and theoretical deviation thresholds for each group of the current user in this period; Vehicle data characteristics are detected through the data characteristic analysis step and the theoretical-measured deviation characteristic analysis step. When the system is determined to be in an abnormal state, the detection will continue. When an abnormal state is detected, signals and data are sent, and continuous monitoring is performed.

[0013] A device for detecting the degradation state of vehicle dynamic performance according to a second aspect of the present invention includes: The data acquisition module is used to determine the vehicle's performance to be tested and its corresponding index parameters, and to determine the data set unit period; and to determine the associated signals related to the performance to be tested and the index parameters based on the data set unit period. The data analysis module is used to filter valid sample data and group the data according to the correlation signal, and to perform data analysis on the grouped data to obtain data analysis results. The evaluation module is used to determine the target performance status of the vehicle based on the data analysis results.

[0014] According to some embodiments of the present invention, the data acquisition module is further configured to: determine that the performance to be tested is the vehicle handling performance observed by detecting steering sensitivity, and set the data set unit period; and based on the state information acquired according to the vehicle handling performance, including steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration; and within one data set unit period, sample the steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration to obtain the associated signal containing the sampled data.

[0015] The vehicle dynamic performance degradation detection device according to an embodiment of the present invention has at least the following beneficial effects: The device determines the vehicle's test performance and corresponding index parameters through a data acquisition module, and defines the data set unit period. Then, based on the data set unit period, it identifies correlation signals related to the test performance and index parameters to facilitate the screening of valid sample data. Next, the data analysis module filters the valid sample data based on the correlation signals, groups the data, and analyzes the grouped data to obtain analysis results. Finally, the evaluation module determines the vehicle's target performance state based on the data analysis results. This invention's detection device automatically performs state detection on vehicle dynamics and handling performance, determining whether the vehicle has experienced performance degradation. It achieves vehicle dynamics performance monitoring and evaluation, solving the problem of difficulty in state detection of existing vehicle dynamics performance, and effectively reducing the risks or experience degradation caused by vehicle performance degradation or malfunctions.

[0016] According to a third aspect of the present invention, a control device includes at least one processor; and a memory storing instructions that, when executed by the at least one processor, perform the method for detecting the degradation state of vehicle dynamic performance described in the first aspect of the present invention.

[0017] Since the control device adopts all the technical solutions of the vehicle dynamic performance degradation detection method of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for detecting the degradation state of vehicle dynamic performance according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps for obtaining the associated signal in one embodiment of the present invention; Figure 3 This is a flowchart of the steps for obtaining data analysis results in one embodiment of the present invention; Figure 4 This is a flowchart of determining the target performance status based on data analysis results in one embodiment of the present invention; Figure 5 This is a flowchart of a method for detecting the degradation state of vehicle dynamic performance according to another embodiment of the present invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0021] In the description of this invention, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0022] In the description of this invention, "multiple" means two or more; "greater than," "less than," and "exceeding" are understood to exclude the stated number; "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0023] In the description of this invention, it should be noted that terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0024] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are some embodiments of the present invention, not all embodiments.

[0025] In existing technologies, vehicle performance testing mainly relies on fault analysis based on error messages from system components. This method has significant limitations in detecting performance degradation in systems without error messages. For example, mechanical performance degradation such as tire wear and suspension aging often lacks direct electronic signal feedback, making it difficult for traditional detection methods to capture dynamic changes in real time, thus hindering the timely detection of potential vehicle performance problems.

[0026] Reference Figure 1 As shown, this application proposes a method for detecting the degradation state of vehicle dynamic performance, including but not limited to the following steps: Step S100: Determine the vehicle's performance to be tested and its corresponding index parameters, and determine the data set unit period; Step S200: Determine the associated signals related to the performance and index parameters under test based on the data set unit period; Step S300: Filter valid sample data according to the correlation signal and group the data, and perform data analysis on the grouped data to obtain data analysis results; Step S400: Determine the target performance status of the vehicle based on the data analysis results.

[0027] The performance to be tested refers to the vehicle dynamics characteristics that need to be detected, such as steering sensitivity, which can be achieved by calculating the ratio of lateral acceleration to steering wheel angle. The index parameters are key physical quantities reflecting the performance to be tested, such as a combination of vehicle speed and tire pressure. The data set unit period refers to the time window for continuous data acquisition, which can be a single driving cycle or a fixed mileage. Associated signals include multi-source data related to performance evaluation, such as steering system sensor signals and vehicle dynamic parameters.

[0028] Specifically, steering sensitivity was first selected as the core indicator for evaluating vehicle handling performance, with a data collection cycle of 100 kilometers. During vehicle operation, parameters such as steering wheel angle, vehicle speed, and tire pressure were collected simultaneously, and invalid data from abnormal operating conditions were discarded. The valid data were grouped according to vehicle speed range and tire pressure range, and steering sensitivity values ​​for each group were calculated to form a dataset. Statistical analysis methods were used to identify data distribution characteristics, and historical data were compared to determine whether the current performance deviated from the baseline range.

[0029] Traditional methods can only handle system faults with explicit error signals, while this solution establishes a correlation model between performance parameters and mechanical state by dynamically acquiring multi-source operational data. Particularly for the attenuation problem of mechanical components without electronic messages, it significantly expands the detection coverage by using multi-parameter comprehensive analysis instead of single-signal detection.

[0030] Through the above technical solution, this application can achieve continuous monitoring of vehicle dynamics performance without relying on system messages, effectively identifying mechanical performance degradation problems such as tire wear and increased steering system clearance. Periodic data analysis can detect potential performance degradation trends in advance, providing data support for preventative maintenance, while avoiding detection errors caused by misinterpretation of a single signal.

[0031] Reference Figure 2 As shown, in some embodiments, step S100 specifically includes the following steps: Step S110: Determine the performance to be tested as the vehicle handling performance observed by detecting steering sensitivity, and set the data set unit period; Step S120: The status information obtained based on the vehicle handling performance includes steering wheel angle, tire pressure, system message signals, vehicle speed and acceleration; Step S130: Within one data set unit cycle, data samples are taken of steering wheel angle, tire pressure, system message signals, vehicle speed and acceleration to obtain a correlation signal containing the sampled data.

[0032] Steering sensitivity refers to the proportional relationship between the vehicle's lateral acceleration response and the steering wheel angle input. This can be achieved by acquiring real-time steering wheel angle signals and vehicle lateral acceleration data, establishing a mathematical model between the two, and using this model to quantify the degree of degradation in vehicle handling performance. The data set unit period refers to a preset continuous data acquisition time window, which can be implemented using a fixed duration or a dynamically adjusted period setting, such as setting it to 30 minutes of continuous driving or 50 kilometers of cumulative driving as the period boundary, to ensure the integrity of data acquisition and meet the needs of periodic analysis. Correlated signals refer to the set of sensor data that are physically related to vehicle handling performance. Specifically, this can be achieved by acquiring steering wheel angle, tire pressure, vehicle speed, and acceleration signals through hardware devices such as the vehicle's CAN bus, tire pressure monitoring module, and inertial measurement unit, and combining this with electronic stability control system messages to achieve multi-source data synchronization, used to construct a multi-dimensional performance evaluation dataset.

[0033] Specifically, after setting the data set unit cycle, the vehicle-mounted sensors continuously collect steering wheel angle signals, while simultaneously acquiring real-time data from the tire pressure monitoring system. Vehicle speed signals are provided by wheel speed sensors or a GPS module, acceleration data is collected by an inertial measurement unit, and message signals from each system are obtained by parsing the CAN bus communication protocol. Within a single data set unit cycle, for example, with a 30-minute continuous driving period, the above signals are synchronously sampled and stored as a time-series dataset. By integrating the steering wheel angle and lateral acceleration data, steering sensitivity indicators can be calculated; combined with parameters such as tire pressure and vehicle speed, performance benchmark curves under different operating conditions can be established, providing a foundation for subsequent data analysis.

[0034] Traditional methods rely solely on error messages from system components for fault analysis, failing to cover performance degradation detection in systems without such messages. This solution integrates real-time signals from multiple dimensions, including steering wheel angle, tire pressure, and vehicle speed, constructing a detection system independent of a single message, thus solving the problem of monitoring systems without messages. Furthermore, the setting of the data set unit period enables long-term performance trend analysis, overcoming the limitation of traditional methods that only address instantaneous faults.

[0035] This application further points out that if any item in the status information within the data set unit period is within the normal range, while any item in the past period is not within the normal range, then any item will be marked as a candidate area. During data analysis, data with and without a candidate region are analyzed separately to compare and contrast the differences.

[0036] The "waitlist zone" refers to a mechanism that marks data items that are within the normal range in the current cycle but have exhibited abnormal states in historical cycles. This can be achieved by setting threshold ranges or logical judgment conditions, such as triggering a mark when tire pressure returns to the normal range for the first time within three consecutive cycles. The data set unit cycle refers to a preset time period used for collecting and analyzing vehicle status information. This can be a fixed duration or a dynamically adjusted cycle division method, such as a seven-day unit cycle. Comparative analysis of differences involves establishing analytical models for the data group with the waitlist zone and the normal data group separately. For example, by calculating the difference in steering sensitivity distribution between the two sets of data, potential performance degradation correlations can be identified.

[0037] Specifically, during vehicle handling performance testing, when the steering wheel angle is detected to be within the normal threshold range for the current cycle, but a query of the historical database reveals instances where it exceeded the threshold in the past three cycles, this parameter is automatically marked as a candidate. The data grouping phase generates two independent datasets: a complete dataset containing all candidate-marked parameters, and a baseline dataset excluding candidate-marked parameters. By analyzing the two datasets in parallel, such as calculating the standard deviation of steering sensitivity, the impact of historically abnormal parameters on the current data analysis can be identified. If the difference between the two datasets exceeds a preset threshold, it indicates that the parameter may be associated with a hidden performance degradation issue.

[0038] Existing technologies typically rely on single-dimensional analysis based solely on current period data, failing to effectively identify the impact of intermittent abnormal parameters on long-term performance evaluation. This solution, however, establishes a historical anomaly tracing mechanism to capture periodically recurring hidden fault characteristics. For instance, traditional methods might attribute occasional tire pressure anomalies to steering sensitivity fluctuations as random errors, while this solution accurately identifies their correlation with suspension system aging through candidate area comparison.

[0039] This application can effectively distinguish the correlation between occasional anomalies and persistent performance degradation, avoiding misjudgments caused by short-term parameter recovery. For example, for intermittent failures of electronic power steering systems, traditional testing may miss the problem because the current data is normal, while this solution can detect trends in changes in system response characteristics in advance by comparing the dispersion of steering sensitivity in data sets with a candidate area.

[0040] Reference Figure 3 As shown, in some embodiments, step S300 specifically includes the following steps: Step S310: Group the vehicles according to their speed and according to their tire pressure. Step S320: Analyze the effective group datasets of each group, calculate the steering sensitivity by the input relationship between the vehicle lateral acceleration response and the steering wheel angle each time, and obtain the steering sensitivity set dataset of each group; Step S330: Use the outlier detection method to determine the data characteristics of each group's steering sensitivity set dataset within the data set unit period; Step S340: By performing big data analysis on the data features, determine the common range of outlier percentages in the driver steering sensitivity dataset within the current period, and derive the target range of outlier percentages for the driver within the current period.

[0041] Grouping by vehicle speed refers to dividing the collected vehicle operation data into multiple subsets according to different speed ranges. This can be achieved by setting speed threshold ranges, such as dividing vehicle speed into ranges like 0-30km / h, 30-60km / h, and 60-90km / h. Grouping can eliminate the interference of vehicle speed differences on steering sensitivity analysis.

[0042] Grouping by tire pressure refers to dividing the sample into datasets with different tire pressure ranges based on tire pressure sensor data. Specifically, ±10% of the standard tire pressure value can be used as the grouping boundary. This grouping helps identify the impact of abnormal tire pressure on steering performance. Outlier detection methods involve using statistical methods to identify steering sensitivity data that deviates from the normal distribution range. This can be achieved using box plots or standard deviation methods. For example, data exceeding ±3 times the standard deviation of the mean can be marked as outliers to filter sensitivity deviations caused by abnormal driving behavior or mechanical failure.

[0043] The target range for outlier percentage refers to the proportion of outliers allowed within the current period, determined by statistical analysis of historical data. Specifically, a big data platform can be used to perform cluster analysis on the historical outlier percentages of multiple vehicles, and the 95% confidence interval of the percentage distribution can be extracted as the target range to determine whether the current data is within the normal fluctuation range.

[0044] Specifically, within a data set unit period, the raw data is divided into multiple homogeneous subsets based on vehicle speed and tire pressure. For example, samples with vehicle speeds between 60-90 km / h and tire pressures at standard values ​​are grouped into the same group. For each data group, steering sensitivity is calculated based on the linear relationship between lateral acceleration and steering wheel angle, for example, by fitting the slope value using the least squares method as a sensitivity index. Subsequently, box plots are used to detect outliers in each sensitivity data group, and the ratio of outliers to the total number of samples in each group is calculated. By analyzing the same group data of all vehicles in historical periods, a baseline distribution range for the proportion of outliers is established. For example, the outlier proportion under a certain vehicle speed-tire pressure combination is usually between 2% and 5%. Finally, the actual proportion in the current period is compared with the baseline range to determine whether any anomalies exist.

[0045] Existing technologies rely on system error messages for fault diagnosis, but cannot detect the mechanical performance degradation of systems without error messages. This solution, by real-time acquisition of sensor data such as steering wheel angle and vehicle speed, combined with dynamic grouping and outlier analysis, can identify abnormal steering sensitivity even without fault codes. For example, traditional methods can only detect direct faults in tire pressure sensors, while this solution, through tire pressure grouping and sensitivity correlation analysis, can discover steering performance degradation caused by tire wear even when tire pressure is normal.

[0046] This application can eliminate environmental interference based on multi-dimensional data grouping, accurately identifying steering sensitivity deviations caused by mechanical component wear or latent faults. By using a dynamic threshold determination based on the proportion of outliers, it avoids the misjudgment problem of a single fixed threshold under complex operating conditions, improving the reliability of vehicle handling performance degradation detection. Furthermore, this method does not rely on system fault codes and can be extended to mechanical performance monitoring scenarios without message messages.

[0047] Reference Figure 4 As shown, in some embodiments, step S400 specifically includes the following steps: Step S410: Based on the target range of outlier proportion in this period and the proportion of data exceeding the basic range deviation in each segment within the data set unit period, the target performance status of the vehicle is determined. Step S420: Analyze the trend and value of the proportion of drivers who have previously been outliers, and predict the threshold of the proportion in the current period based on the proportion of drivers in the past. Step S430: Analyze the relationship between all driver model verification and actual test data using big data analysis to set the theoretical verification baseline deviation range; Step S440: If the proportion of outliers in any segment during the current period is greater than the theoretical basic deviation range, and the proportion of outliers in the segment shows an increasing trend or the proportion of outliers in a segment exceeds the threshold of the current period in the previous three periods, then the port is marked as pending; otherwise, all ports are marked as normal.

[0048] The target range for the outlier percentage in this period refers to a reasonable range of the proportion of normal outliers in the steering sensitivity dataset within the current period, obtained through statistical analysis of historical data. This can be achieved by using a sliding window algorithm to calculate a weighted average of data from several past periods, thus establishing a dynamically changing verification benchmark. The percentage of data exceeding the baseline range within each data set unit period refers to the proportion of samples in each sub-interval that deviate from the benchmark value in steering sensitivity, after dividing the vehicle operating data into multiple sub-intervals based on vehicle speed or tire pressure, relative to the total sample size. This can be achieved using binning statistics combined with standard deviation threshold judgment, used to identify performance anomalies under specific operating conditions.

[0049] Specifically, after completing data grouping and steering sensitivity calculation, the system first establishes an outlier distribution model for different groups based on historical big data, generating a dynamically adjusted target range for the outlier percentage in the current period. Simultaneously, for each vehicle speed or tire pressure segment within each data set unit period, the system continuously calculates the percentage of steering sensitivity deviations from the baseline range. During verification, the real-time calculated deviation percentages for each segment are cross-referenced with the target range. When more than a set number of segments simultaneously show deviation percentages exceeding the limit, it can be determined that the vehicle's dynamic performance has degraded. For example, in tire pressure grouping, if the deviation percentage of the low-pressure group (e.g., below 220 kPa) exceeds the upper limit of the target range for three consecutive periods, and the deviation percentage of the medium-pressure group (e.g., 220-250 kPa) increases simultaneously, a performance degradation warning is triggered.

[0050] Existing solutions typically use fixed thresholds for anomaly detection, which cannot adapt to the natural decay of parameters and changes in operating conditions during vehicle use. This solution, however, establishes a dynamic target range and combines it with a multi-dimensional, segmented cross-validation mechanism to effectively distinguish between normal operating condition fluctuations and true performance degradation. Particularly when dealing with performance degradation in messageless systems, it overcomes the limitations of traditional methods that rely on system messages through correlation analysis of indirect indicators such as steering sensitivity.

[0051] This application enables more accurate vehicle performance status assessment and effectively reduces the false alarm rate. Through dynamically adjusted verification ranges and interval correlation analysis mechanisms, it ensures detection sensitivity while avoiding false alarms caused by fluctuations in a single indicator. Especially in scenarios of gradual degradation such as tire wear and steering system aging, this solution can achieve early warning and accurate location through multi-period data trend analysis.

[0052] This application further proposes to analyze the trend and value of the outlier ratio in the past, and predict the threshold of the ratio in the current period based on the driver's past ratio; to analyze the relationship between the model verification and actual data of all drivers through big data analysis, and to set the theoretical verification basic deviation range; if the outlier ratio of any segment in the current period is greater than the theoretical verification basic deviation range, and the outlier ratio of the segment in the previous three periods shows an increasing trend or there has been a segment outlier ratio that exceeds the threshold of the current period, then the port is marked as pending, otherwise all ports are marked as normal.

[0053] The "trend and numerical value of past outlier proportions" refers to the statistical results of driver steering sensitivity data deviating from the normal range within historical periods. Specifically, time series analysis methods can be used to fit trends in historical data to establish a baseline for driver behavior patterns. The "theoretical verification baseline deviation range" refers to the statistical confidence interval established by aggregating multi-dimensional driving behavior data. Specifically, machine learning algorithms can be used to perform cluster analysis on massive driver samples to generate baseline parameter distributions for different driving scenarios. The "segmented outlier proportion threshold determination mechanism" refers to anomaly detection rules based on dynamic time windows. Specifically, a sliding window algorithm can be used to track data fluctuations within continuous periods and combine trend changes to achieve state classification.

[0054] Specifically, during vehicle operation, steering sensitivity-related parameters are continuously collected, and the outlier ratio of each data group is calculated. By extracting the outlier ratio variation patterns from historical driver driving data, a prediction model based on exponential smoothing is established, outputting the expected threshold for the current period's outlier ratio. Simultaneously, a driver group behavior model is constructed by integrating multi-source heterogeneous data, and a kernel density estimation method is used to calculate the theoretical verification benchmark range. When the outlier ratio of a data group exceeds the theoretical benchmark range in real time, the outlier ratio variation trajectory of that group over the previous three periods is further traced: if there is a continuous upward trend or the historical peak exceeds the current threshold, a pending label is triggered; otherwise, the normal label is maintained.

[0055] This application addresses the problems of poor adaptability and high false alarm rate of traditional detection methods for messageless systems. By integrating historical trend prediction and group benchmark verification, the accuracy of state determination is significantly improved, particularly in distinguishing between occasional anomalies and continuous degradation. The dynamic threshold mechanism can adapt to different driving habits and vehicle operating conditions, avoiding misjudgments caused by individual differences, and providing a reliable solution for performance monitoring of messageless systems.

[0056] This application further proposes that when it is determined that the target performance of the whole vehicle has deteriorated, the data analysis results should be sent to the OEM to identify and pinpoint the problematic sample.

[0057] The data analysis results refer to the dataset of steering sensitivity calculated using vehicle handling performance parameters, along with the analysis of outlier percentages. Specifically, steering sensitivity can be calculated using signals such as steering wheel angle, vehicle speed, and acceleration, and is determined by the percentage of data deviating from the baseline range within a statistical period. The OEM (Original Equipment Manufacturer) refers to the vehicle manufacturer's central data processing platform, which receives and stores vehicle-uploaded performance data via a cloud server, and then compares and analyzes this data with vehicle design parameters. Problematic components refer to specific parts or systems that cause performance degradation. These can be identified by comparing historical component replacement records with performance change trends, combined with design parameter tolerance ranges.

[0058] Specifically, when the proportion of outliers in steering sensitivity exceeds the theoretically verified baseline deviation range across multiple consecutive data set periods, the evaluation module determines that the target performance has degraded. At this point, the data analysis module uploads a data packet containing the steering sensitivity dataset, outlier distribution characteristics, and historical trends to the OEM's data center via the vehicle communication unit. After receiving the data, the OEM calls upon the vehicle's original design parameter database to cross-validate steering system component tolerances, sensor calibration records, and historical tire wear data to pinpoint the specific component causing the abnormal steering sensitivity.

[0059] Through the above technical solution, this application can proactively identify potential problems through performance degradation trend analysis even when the vehicle has not triggered a fault alarm, and upload key data to the OEM for in-depth analysis, effectively solving the problem of difficulty in locating faults in systems without messages. This solution enables OEMs to quickly pinpoint abnormal components based on actual vehicle operating data, such as abnormal steering gear clearance or tire pressure sensor drift, thereby improving the accuracy and timeliness of after-sales maintenance.

[0060] Reference Figure 5 As shown, it can be understood that in this embodiment of the invention, the performance indicators to be detected are first identified, and the data set unit period is confirmed; then, valid sample data are screened through environmental signals and the data are grouped, and the differences with past data environmental signals are analyzed. Specifically, this includes two parts: data characteristic analysis steps and theoretical-measured deviation characteristic analysis steps.

[0061] The data characteristic analysis steps include: estimating the periodic outlier characteristic threshold through users' past periodic data; analyzing the data characteristics of each group in the current period based on the periodic outlier characteristic threshold and the outlier relationship of data characteristics; the theoretical and measured deviation characteristic analysis steps include: calculating the deviation threshold through the theoretical model of big data analysis of all users; and analyzing the relationship between the measured and theoretical deviation thresholds of each group of the current user in the current period based on the deviation threshold.

[0062] Then, the vehicle data characteristics are detected through data characteristic analysis steps and theoretical-measured deviation characteristic analysis steps; when no abnormal state is determined, the detection continues; when an abnormal state is determined, signals and data are sent, and the detection continues.

[0063] The following example illustrates the detection method for the degradation state of vehicle dynamic performance.

[0064] Taking steering sensitivity as an example, the detection method of this invention includes the following steps: Step 1: Clearly define the vehicle handling performance by testing steering sensitivity, select a 2-month period, and summarize all relevant data within the 2 months into the same data set.

[0065] Step 2: Identify the required signals: steering wheel angle, tire pressure, steering angle, system message signals, vehicle speed, acceleration, and other information.

[0066] For example, tire pressure signals are detected within a cycle, a scatter plot dataset of relevant signals is created, and the frequency of common tire pressures for users is statistically analyzed. The common tire pressure range is identified, and all signal data outside this range are excluded. Similarly, other environmental signal data such as steering wheel angle are analyzed, and data where all reference signals are not outliers are selected as valid samples to determine the valid sample points within the current cycle. Any method or self-developed algorithm can be used for outlier detection, depending on the specific needs. The specific detection method must be validated by data to ensure its correctness and reasonableness.

[0067] Then, the criteria for valid samples are optimized. Taking tire pressure signals as an example, if a tire pressure within a cycle is within the normal range, but this position has not been within the common range in previous cycles, then this position is marked as a candidate area. Data analysis is performed separately with and without the candidate area, and the differences are compared. If it is found that the value is still within the normal range in the following two cycles, the difference in range with and without the candidate area is added to the data of previous cycles. If there is a large deviation in tire pressure between two adjacent cycles, the data of the cycle after the time sequence is used.

[0068] Step 3: Group the data and calculate the user's steering sensitivity at common vehicle speeds and steering wheel angle ranges by analyzing the sample data. Divide the data into common and outlier categories.

[0069] It is understandable that data can be grouped according to different signals, such as by vehicle speed, starting from 20... Starting from this point, the system is divided into groups of 20 bar each, and also grouped according to tire pressure, with each group divided into 0.5 bar intervals. The effective grouped datasets are analyzed, and steering sensitivity is calculated by the relationship between each vehicle lateral acceleration response and steering wheel angle input, resulting in a set of steering sensitivity datasets for each group. Simultaneously, each segment can be validated using a two-degree-of-freedom or three-degree-of-freedom vehicle dynamics model to determine the basic range of steering sensitivity for each segment and set the deviation range.

[0070] Outlier detection methods are used to identify data characteristics within the current period. These methods can be the same as or different from those used in step two, as long as the judgment criteria remain consistent across steps. However, the specific detection method must be validated for accuracy and reasonableness through data verification.

[0071] Step 4: Through big data analysis, determine the common range of outlier percentages in the driver's steering sensitivity dataset within the current period, and derive the target range for the outlier percentage for that driver within the current period. Simultaneously, the vehicle's performance status can be assessed based on the percentage of data exceeding the baseline deviation in each segment within a period.

[0072] First, we analyze the user's past outlier percentage, examining its trends and values. Based on this historical outlier percentage, we predict the current period's outlier threshold. Then, through big data analysis, we examine the relationship between the model validation and actual data for all users, establishing a theoretical validation baseline deviation range.

[0073] If the proportion of outliers in a certain segment exceeds the prediction threshold within the current period, and the proportion of outliers in that segment has shown an increasing trend in the previous three periods, or if the proportion of outliers in that segment has exceeded the threshold of the current period at some point, then the port is marked as pending. Otherwise, the port is marked as normal. Simultaneously, a similar method is used to determine the proportion of measured deviations exceeding the basic range for that segment. If both of the above conditions are met, port two is marked as pending. If the same segment appears pending twice, ports one and two are marked as abnormal.

[0074] Once it is determined that the vehicle's handling and stability performance has deteriorated, the relevant data can be sent to the OEM and 4S dealerships for professional engineers to assess and identify the problematic component. Simultaneously, the 4S dealership can decide whether to remind the user to bring the vehicle in for maintenance to ensure a smooth driving experience.

[0075] This invention also proposes a detection device for the degradation state of vehicle dynamic performance, including a data acquisition module, a data analysis module, and an evaluation module. The data acquisition module determines the vehicle's test performance and its corresponding index parameters, and determines the data set unit period; based on the data set unit period, it determines the correlation signals related to the test performance and index parameters. The data analysis module filters valid sample data based on the correlation signals and groups the data, then analyzes the grouped data to obtain data analysis results. The evaluation module judges the target performance state of the vehicle based on the data analysis results.

[0076] The data acquisition module is a device used to collect raw signals related to vehicle performance during operation. Specifically, it can be implemented using onboard sensors, a controller area network (CLAN) bus, or an onboard diagnostic system interface. It collects data such as steering wheel angle, tire pressure, and vehicle speed in real time to form a dataset. The data analysis module is the unit that filters, groups, and processes the raw data. Specifically, it can be implemented using an embedded processor or a cloud computing platform. Algorithms are used to perform outlier analysis and sensitivity calculations on the grouped datasets of vehicle speed and tire pressure. The evaluation module is the logical unit that judges the performance degradation state based on the analysis results. Specifically, it can be implemented using preset threshold comparisons or machine learning models. By comparing current period data with historical trends, it determines whether the performance deviates from the normal range.

[0077] Specifically, the data acquisition module first sets the performance to be tested as vehicle handling performance, selecting steering sensitivity as the indicator parameter, and determines the data set period, for example, a continuous 30-minute driving period. Within this period, the module collects related signals such as steering wheel angle, tire pressure, and vehicle speed through sensors and stores the data in a buffer. The data analysis module then filters the raw data for validity, such as excluding abnormal data from sudden braking or extreme steering conditions. It then categorizes the data into three groups based on vehicle speed (low, medium, and high speed) and tire pressure (standard and low tire pressure). For each group, the module calculates the ratio of lateral acceleration to steering wheel angle to obtain steering sensitivity and uses the standard deviation method to identify outliers. The evaluation module, based on the proportion of outliers in each group and the baseline range of driver operating habits in historical data, determines whether there is performance degradation within the current period. For example, if the proportion of outliers in the low-speed standard tire pressure group exceeds a threshold for three consecutive periods, the steering system is deemed abnormal.

[0078] Through the above technical solution, this application can accurately identify the degradation state of vehicle dynamic performance through modular data acquisition and group analysis without relying on system messages. For example, when the tire pressure monitoring system fails, it is still possible to determine whether the tire performance is abnormal by analyzing the changes in steering sensitivity under different tire pressure groups. At the same time, by dynamically adjusting the data set period and grouping rules, the device adapts to the detection needs of different driving environments, thereby improving the reliability and applicability of the detection results.

[0079] In some embodiments, the data acquisition module is used to determine the vehicle's performance to be tested as the vehicle's handling performance observed by detecting steering sensitivity, and to set a data set unit period; the state information acquired based on the vehicle's handling performance includes steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration; within one data set unit period, the steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration are sampled to obtain a correlated signal containing the sampled data.

[0080] The data set unit period refers to a preset time period used to divide the time window for data collection and analysis. This can be implemented using a fixed duration or a dynamically adjusted period, such as hours, days, or weeks, to ensure the continuity and temporal consistency of data collection. Steering sensitivity is the ratio of the vehicle's lateral acceleration response to the steering wheel angle input. This is calculated by collecting steering wheel angle signals and vehicle lateral acceleration signals from sensors, used to quantify the changing trends of vehicle handling performance. Correlated signals refer to a multi-dimensional data set related to the performance being measured, specifically including mechanical component state parameters, electronic system signals, and vehicle motion parameters. For example, system message signals are collected via the CAN bus, combined with vehicle speed information obtained from wheel speed sensors, forming the basis for multi-dimensional data correlation analysis.

[0081] Specifically, the data acquisition module first sets the target for detecting vehicle handling performance as steering sensitivity and determines the data set unit period. Within the period, the module synchronously collects steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration signals. The steering wheel angle is acquired through a steering column sensor, tire pressure is read from a tire pressure monitoring system, system message signals are extracted through the vehicle communication network, and vehicle speed and acceleration are provided by wheel speed sensors and an inertial measurement unit, respectively. After all signals are timestamped and aligned, a correlated signal dataset containing multi-dimensional parameters is formed, providing the raw data foundation for subsequent analysis.

[0082] In some specific implementations, the data collection unit period can be set to a 24-hour period of continuous vehicle operation. During this period, the module continuously collects steering wheel angle signals, for example, recording the angle value every 0.1 seconds, and simultaneously collecting tire pressure data every 5 seconds. System message signals may include status codes for the electronic stability control system and the electric power steering system. Vehicle speed and acceleration data are synchronously acquired via the vehicle bus at a frequency of 10Hz. When some parameters are missing within a period, the module automatically extends the collection time until a complete dataset is obtained.

[0083] Through the above technical solution, this application can establish a complete data model of vehicle handling performance by collecting multi-dimensional parameters without relying on system messages, thus solving the problem that existing technologies are not applicable to messageless systems. By synchronously collecting the status of mechanical components, electronic system signals, and vehicle motion parameters, it can achieve accurate detection of steering sensitivity decay, providing a reliable data foundation for vehicle dynamic performance evaluation.

[0084] Specifically, the data acquisition module can execute steps S100, S200, S110 to S130 in the detection method of the above embodiment; the data analysis module can execute steps S300, S310 to S340 in the detection method of the above embodiment; and the evaluation module can execute steps S400, S410 to S440 in the detection method of the above embodiment.

[0085] Furthermore, embodiments of the present invention also provide a control device, including: at least one processor; and a memory storing instructions, which, when executed by the at least one processor, perform the vehicle dynamic performance degradation detection method of the above embodiments.

[0086] Taking the example of a processor and memory in a control device being connected via a bus, memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the controller via a network.

[0087] The non-transient software program and instructions required to implement the detection method of the above embodiments are stored in memory. When executed by a processor, the detection method in the above embodiments is executed, for example, the method described above is executed. Figure 1 Method steps S100 to S400 Figure 2 The method steps S110 to S130, etc.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Since the control device adopts all the technical solutions of the detection method of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0090] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for detecting the degradation state of vehicle dynamic performance, characterized in that, include: Determine the vehicle's performance to be tested and its corresponding index parameters, and determine the data set unit period; Based on the data set unit period, determine the associated signals related to the performance under test and the indicator parameters; Valid sample data are filtered and grouped according to the correlation signal, and the grouped data are analyzed to obtain the data analysis results. The target performance status of the vehicle is determined based on the data analysis results.

2. The method for detecting the degradation state of vehicle dynamic performance according to claim 1, characterized in that, The process of determining the vehicle's performance to be tested and its corresponding index parameters, and determining the data set unit period, includes: The performance to be tested is determined to be the vehicle handling performance observed by detecting steering sensitivity, and the data set unit period is set. The step of determining the correlation signal related to the performance under test and the indicator parameter based on the data set unit period includes: The status information obtained based on the vehicle handling performance includes steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration. Within one data set unit cycle, data samples are taken of the steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration to obtain the associated signal containing the sampled data.

3. The method for detecting the degradation state of vehicle dynamic performance according to claim 2, characterized in that, The process of determining the vehicle's performance to be tested and its corresponding index parameters, and determining the data set unit period, further includes: If any item in the status information within the data set unit period is within the normal range, while any item in the past period was not within the normal range, then that item is marked as a candidate area. During data analysis, data with and without a candidate region are analyzed separately to compare and contrast the differences.

4. The method for detecting the degradation state of vehicle dynamic performance according to claim 2, characterized in that, The step of filtering valid sample data based on the correlation signal and grouping the data, and then performing data analysis on the grouped data to obtain data analysis results, includes: Grouped according to the vehicle speed and grouped according to the tire pressure; Analyze the effective group datasets of each group, and calculate the steering sensitivity by the input relationship between the vehicle lateral acceleration response and the steering wheel angle for each time, so as to obtain the set dataset of steering sensitivity for each group; The outlier detection method is used to determine the data characteristics of the steering sensitivity set dataset of each group within the data set unit period; By performing big data analysis on the data features, the common range of outlier percentages in the driver steering sensitivity dataset within the current period is determined, and the target range of outlier percentages for the current period is derived.

5. The method for detecting the degradation state of vehicle dynamic performance according to claim 4, characterized in that, The step of determining the target performance status of the vehicle based on the data analysis results includes: The target performance status of the vehicle is determined by verifying the outlier percentage target range for this period and the percentage of data exceeding the basic range deviation in each segment within the data set unit period.

6. The method for detecting the degradation state of vehicle dynamic performance according to claim 5, characterized in that, The step of determining the target performance status of the vehicle based on the data analysis results also includes: Analyze the trends and values ​​of drivers’ past outlier percentages, and predict the threshold of the current period’s percentage based on the drivers’ past percentages. By analyzing the relationship between all driver model verification and actual test data using big data, a theoretical verification baseline deviation range is set. If the percentage of outliers in any segment during the current period is greater than the theoretically verified baseline deviation range, and the percentage of outliers in the segment shows an increasing trend or has exceeded the threshold of the current period in the previous three periods, then the port is marked as pending; otherwise, all ports are marked as normal.

7. The method for detecting the degradation state of vehicle dynamic performance according to claim 2, characterized in that, The detection method further includes: The data characteristic analysis steps include: estimating the threshold for periodic outlier characteristics based on the user's past periodic data; Based on the periodic outlier characteristic threshold, the characteristics of each group of data within the current period are analyzed through the outlier relationship of data characteristics. The theoretical and experimental deviation characteristic analysis steps include: calculating the deviation threshold through big data analysis of all users using a theoretical model; Based on the aforementioned deviation threshold, analyze the relationship between the actual and theoretical deviation thresholds for each group of the current user in this period; Vehicle data characteristics are detected through the data characteristic analysis step and the theoretical-measured deviation characteristic analysis step. When the system is determined to be in an abnormal state, the detection will continue. When an abnormal state is detected, signals and data are sent, and continuous monitoring is performed.

8. A device for detecting the degradation state of vehicle dynamic performance, characterized in that, The data acquisition module is used to determine the vehicle's performance to be tested and its corresponding index parameters, and to determine the data set unit period; and to determine the associated signals related to the performance to be tested and the index parameters based on the data set unit period. The data analysis module is used to filter valid sample data and group the data according to the correlation signal, and to perform data analysis on the grouped data to obtain data analysis results. The evaluation module is used to determine the target performance status of the vehicle based on the data analysis results.

9. The device for detecting the degradation state of vehicle dynamic performance according to claim 8, characterized in that, The data acquisition module is further configured to: determine that the performance to be tested is the vehicle handling performance observed by detecting steering sensitivity, and set the data set unit period; and based on the state information acquired from the vehicle handling performance, including steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration; and within one data set unit period, sample the steering wheel angle, tire pressure, system message signals, vehicle speed, and acceleration to obtain the associated signal containing the sampled data.

10. A control device, characterized in that, include: At least one processor; And a memory storing instructions that, when executed by at least one processor, perform the method for detecting the degradation state of vehicle dynamics as described in any one of claims 1 to 7.

Images