A real-time analysis method and system based on brake data

Abstract

This invention discloses a method and system for real-time braking data analysis, relating to the field of automotive braking technology. The method includes: pattern determination: acquiring real-time operating data of the vehicle braking system, identifying and determining the driver's braking behavior pattern; pattern matching: setting multiple predefined braking system response models and automatically matching the driver's braking behavior pattern with the braking system response models; interactive adjustment: interacting with a simulated braking model using the matched braking system response model, simultaneously displaying the simulation results of the simulated braking model through a display interface, and adjusting the vehicle braking system based on the simulation results. This invention improves driving safety by analyzing the driver's braking behavior pattern in real time and automatically adjusting the braking system response model, making the vehicle's braking performance more consistent with the current driving conditions.

Description

Technical Field

[0001] This invention relates to the field of automotive braking technology, specifically to a method and system based on real-time analysis of braking data. Background Technology

[0002] With the rapid development of the automotive industry, the performance of vehicle braking systems directly affects driving safety. While traditional braking systems possess certain functions, they still have many shortcomings in terms of real-time data acquisition, processing speed, and accuracy. Especially in complex driving environments, ensuring efficient braking system response has become a pressing issue.

[0003] In recent years, advancements in sensor technology and improved data processing capabilities have made real-time analysis of braking data possible. However, existing solutions are often limited by factors such as the accuracy of data acquisition and low processing efficiency, making it difficult to fully meet the high-performance requirements of modern vehicles, especially intelligent connected vehicles, for their braking systems.

[0004] Chinese invention patent CN114169083A discloses a method, device, equipment, and medium for analyzing data of an automatic emergency braking system. The method includes tagging hardware-in-the-loop test data and real-vehicle test data of the same data format; simulating the triggering and braking process of the vehicle's automatic emergency braking system and obtaining the distance between the vehicle and an obstacle when it stops; determining the magnitude of this distance compared to a preset ideal distance between the vehicle and the obstacle when braking stops; and when the distance between the vehicle and the obstacle when stopping is less than the ideal distance, calculating the difference between the ideal distance and the obstacle and the distance between the vehicle and the obstacle when stopping, and determining the percentage of this difference to the ideal distance. This solves the problems of lag and low efficiency in the analysis of hardware-in-the-loop test data and real-vehicle verification test data. This application has the effect of improving lag and increasing efficiency.

[0005] In practice, different drivers have different driving habits when driving the same vehicle. Furthermore, due to differences in the braking effect, tuning, and hardware parameters of braking components between different vehicles, the same braking behavior by the same driver may produce different braking effects on different vehicles. In other words, there is a break-in period between the driver and the vehicle. During this period, the driver cannot fully utilize the vehicle's braking system's optimal performance and may even make mistakes. A longer break-in period indicates a higher risk to the driver. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for real-time analysis of braking data to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a real-time analysis method based on braking data, the analysis method comprising:

[0008] Pattern determination: Acquire real-time operating data of the vehicle braking system to identify and determine the driver's braking behavior pattern;

[0009] Pattern matching: Set multiple predefined braking system response models and automatically match the driver's braking behavior patterns with the braking system response models;

[0010] Interactive Adjustment: The matched braking system response model interacts with the simulated braking model, while the simulation results of the simulated braking model are displayed on the interface. The vehicle braking system is then adjusted based on these simulation results. Specifically:

[0011] When the simulation result is the same as the driver's preset driving result, the vehicle braking system is adjusted according to the braking parameters corresponding to the simulated braking model.

[0012] When the simulation results are different from the driver's preset driving results, the braking system response model will perform adaptive parameter adjustments and interact with the simulated braking model until the simulation results are the same as the driver's preset driving results.

[0013] Furthermore, identifying and determining the driver's braking behavior pattern includes:

[0014] Acquire real-time operating data of the vehicle braking system, and extract statistical features, time-domain features, and frequency-domain features from the real-time operating data;

[0015] The statistical features, time-domain features, and frequency-domain features are classified using a recognition algorithm to determine the driver's braking behavior patterns: light pressing, heavy pressing, and continuous pressing.

[0016] Furthermore, the setting of the braking system response model includes:

[0017] By extracting statistical features, time-domain features, and frequency-domain features from the real-time operating data of the vehicle braking system, the actual feature vector corresponding to the driver's braking behavior mode is determined.

[0018] Obtain the model feature vector in each predefined braking system response model, and determine the vector distance between the actual feature vector and each model feature vector;

[0019] All vector distances are compared to determine the vector distance with the smallest value. The predefined braking system response model corresponding to the vector distance with the smallest value is the braking system response model corresponding to the driver's braking behavior mode.

[0020] Furthermore, the vector distance between the actual feature vector and each model feature vector is specifically:

[0021]

[0022] in: For vector distance, This represents the i-th element in the actual feature vector. The i-th element in the model feature vector is the index number of the element in the vector. The number of elements in the vector. For the actual feature vector, These are the model feature vectors.

[0023] Furthermore, when the braking system response model performs adaptive parameter adjustments, it adjusts according to the driver's driving state, specifically as follows:

[0024] When the driver is in a driving state:

[0025] The braking system response model is adjusted systematically according to braking distance, braking time, and braking smoothness.

[0026] When the driver is not in a driving state:

[0027] The driver can adjust the braking distance, braking time, and braking smoothness within a preset braking parameter adjustment range through an interactive interface.

[0028] Furthermore, the braking system response model is adjusted systematically according to braking distance, braking time, and braking smoothness, specifically as follows:

[0029] If the braking distance is incorrect, the braking distance will be adjusted, but the braking time and braking smoothness will not be adjusted.

[0030] When the braking distance is correct but the braking time is incorrect, the braking time is adjusted, but the braking distance and braking smoothness are not adjusted.

[0031] When the braking distance and braking time are correct but the braking smoothness is incorrect, the braking smoothness is adjusted, but the braking distance and braking time are not adjusted.

[0032] Furthermore, adjusting the vehicle braking system based on the simulation results also includes:

[0033] Based on real-time operating data of the vehicle's braking system, determine the driver's driving preference behavior;

[0034] The simulation results of the simulated braking model are matched with driving preference behaviors to determine the driving preference behaviors corresponding to the simulation results. Based on the preference data corresponding to the driving preference behaviors, the vehicle braking system is adaptively adjusted.

[0035] Furthermore, the determination of the driver's driving preference behavior includes:

[0036] The operating data of the vehicle braking system is preprocessed, and based on the preprocessed operating data, the relationship between the data distribution and the driver's driving behavior is determined, specifically:

[0037] By analyzing the distribution of braking pressure and braking time, the central tendency and dispersion of the operational data are obtained, thereby determining the driver's braking habits.

[0038] By identifying the correlations between braking data, the factors influencing driver driving preferences can be determined.

[0039] Cluster analysis of braking data identifies different driving preference groups;

[0040] The factors influencing the driver's braking habits, driving preferences, and driving preference groups are comprehensively set to obtain comprehensive feature data. At the same time, the comprehensive feature data is used as the input of the machine learning model to output the driver's driving preference behavior.

[0041] A system for real-time analysis of braking data uses any one of the above-mentioned methods for real-time analysis of braking data.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Firstly, this invention analyzes the driver's braking behavior patterns in real time and automatically adjusts the braking system response model, making the vehicle's braking performance more suitable for the current driving conditions, thereby improving driving safety.

[0044] Secondly, this invention allows drivers to adjust braking parameters through an interactive interface according to their driving habits and preferences. At the same time, it can automatically match the corresponding braking system response model based on the adjusted braking parameters, thereby enhancing the driver's driving experience and making driving more comfortable and personalized.

[0045] Thirdly, this invention can automatically identify the driver's braking behavior pattern and dynamically adjust the braking system parameters according to the actual situation, thereby helping to improve the response speed and accuracy of the braking system and reduce the impact of human error.

[0046] Fourthly, by analyzing the driver's driving preferences and adjusting the braking system parameters in real time, this invention can ensure that the braking system provides the best braking effect under different driving conditions, thereby improving driving safety. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the real-time brake data analysis method of the present invention.

[0048] Figure 2 This is a schematic diagram illustrating the process for determining the braking system response model of the present invention.

[0049] Figure 3 This is a schematic diagram illustrating the process of adjusting the vehicle braking system based on driving preference behavior according to the present invention.

[0050] Figure 4 This is a schematic diagram illustrating the process for determining driver driving preference behavior according to the present invention.

[0051] Figure 5 This is a scatter plot showing the relationship between braking time and braking pressure in this invention.

[0052] Figure 6 This is a line graph showing the relationship between braking pressure and frequency in this invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In actual driving, due to significant differences in driving habits among different drivers, even the same driver may experience significant differences in driving experience across different vehicles. This difference is primarily reflected in the break-in period between the driver and the vehicle's braking system. During the initial use phase, the driver needs time to adapt to the braking characteristics of the new vehicle, during which over- or under-braking may occur, increasing the risk of traffic accidents. Therefore, it is necessary to shorten the break-in period to reduce the likelihood of traffic accidents. The technical solution provided in this application collects and analyzes the vehicle's braking system operating data in real time, identifies the driver's braking behavior patterns, and dynamically adjusts the braking system's response parameters based on these patterns. This not only helps drivers adapt to the new vehicle's braking system more quickly and shortens the break-in period, but also improves the overall safety performance of the vehicle to a certain extent.

[0055] Example 1

[0056] refer to Figure 1 and Figure 2 This embodiment provides a real-time braking data analysis method, which can be divided into three stages: pattern determination, pattern matching, and interactive adjustment. The specific implementation process of this real-time braking data analysis method in this embodiment is as follows:

[0057] Step S1: Determine the sensor types based on the characteristics of the vehicle's braking system and monitoring requirements. For example, a pressure sensor monitors the force applied to the brake pedal, a speed sensor monitors the vehicle's speed, and a position sensor monitors changes in the brake pedal's position. Simultaneously, install the determined sensors in their appropriate locations. For instance, a pressure sensor is installed under the brake pedal to detect the pressure applied by the driver. A speed sensor is installed on the wheel hub or drive shaft to measure changes in vehicle speed. A displacement sensor is installed along the brake pedal's travel path to measure the length of the brake pedal's travel. A time sensor records the timing of braking actions, and combined with data from other sensors, calculates the braking frequency and duration.

[0058] Simultaneously, real-time operating data of the vehicle's braking system is acquired through sensors to identify and determine the driver's braking behavior patterns. Specifically, this includes:

[0059] Step S1.1: Acquire real-time operating data of the vehicle's braking system using sensors installed on the vehicle. Digital sensors output digital signals, and their data can be directly read and transmitted. Analog sensors output analog signals, and their data can be converted into digital signals via an analog-to-digital converter before transmission. In other words, depending on the sensor type and the selected data transmission method, sensor data can be transmitted to the central processing unit via a data transmission protocol (such as CAN bus, LIN bus, and FlexRay).

[0060] Furthermore, in the central processing unit, after cleaning and standardizing the sensor data, corresponding statistical features, time-domain features, and frequency-domain features are extracted. Statistical features include extracting the mean, variance, maximum, and minimum values ​​from the raw data; time-domain features include extracting the peak value, mean, and standard deviation of the signal; and frequency-domain features are extracted by performing a Fourier transform on the data to extract frequency components as features.

[0061] Step S1.2: Classify the statistical features, time-domain features and frequency-domain features obtained in step S1.1, that is, perform data analysis through recognition algorithms (such as clustering algorithms, decision trees and support vector machines) to identify the driver's braking behavior pattern.

[0062] Specifically, the driver engages in the following braking behaviors during driving:

[0063] Brake pedal pressure: In an emergency, the driver will usually apply greater pressure (e.g., pressure range of 70%-100%).

[0064] Braking frequency: More braking is required when driving in the city, while less braking is required when driving on the highway.

[0065] Braking duration: Multiple light taps on the brakes within a short period indicate that the driver is decelerating. A prolonged, heavy tap on the brakes indicates that the driver is attempting an emergency stop.

[0066] Step S2: Set multiple predefined braking system response models and automatically match the braking behavior pattern obtained in step S1.2 with the predefined braking system response models. In this embodiment, the feature data in the predefined braking system response models include: average braking pressure, standard deviation of braking pressure, maximum braking pressure, average braking duration, braking frequency, and the main frequency components of braking force. Specifically, it includes:

[0067] Step S2.1: Determine the actual feature vector corresponding to the driver's braking behavior pattern from the statistical features, time-domain features, and frequency-domain features obtained in step S1.1. Specifically, these are:

[0068] Statistical characteristics: average braking pressure, standard deviation of braking pressure, maximum braking pressure, etc.

[0069] Time-domain characteristics: average braking duration, braking frequency, etc.

[0070] Frequency domain characteristics: spectral analysis results of braking pressure, such as the main frequency components.

[0071] In the specific implementation process, the features extracted above are as follows:

[0072] Average braking pressure: 40%.

[0073] Standard deviation of brake pressure: 10%.

[0074] Maximum braking pressure: 80%.

[0075] Average braking duration: 0.5s.

[0076] Braking frequency: 2 times / min.

[0077] The main frequency component of braking pressure is 2Hz.

[0078] In other words, the actual feature vector extracted based on this feature is: [40,10,80,0.5,2,2].

[0079] Step S2.2: Obtain the model feature vector in each predefined braking system response model, and determine the vector distance between the actual feature vector and each model feature vector.

[0080] In the specific implementation process, this embodiment sets up three predefined braking system response models, which correspond to three different braking behavior modes, namely Model A (light pressing mode), Model B (heavy pressing mode), and Model C (continuous pressing mode), as follows:

[0081] Model A (Light Stomp Mode):

[0082] Average braking pressure: 30%.

[0083] Standard deviation of brake pressure: 8%.

[0084] Maximum braking pressure: 70%.

[0085] Average braking duration: 0.6s.

[0086] Braking frequency: 1 time / min.

[0087] The main frequency component of braking pressure is 1Hz.

[0088] The extracted model feature vector A is: [30,8,70,0.6,1,1].

[0089] Model B (Re-stepping mode):

[0090] Average braking pressure: 60%.

[0091] Standard deviation of brake pressure: 15%.

[0092] Maximum braking pressure: 90%.

[0093] Average braking duration: 0.4s.

[0094] Braking frequency: 3 times / min.

[0095] The main frequency component of braking pressure is 3Hz.

[0096] The extracted model feature vector B is: [60,15,90,0.4,3,3].

[0097] Model C (Continuous Stepping Model):

[0098] Average braking pressure: 40%.

[0099] Standard deviation of brake pressure: 10%.

[0100] Maximum braking pressure: 75%.

[0101] Average braking duration: 0.5s.

[0102] Braking frequency: 2 times / min.

[0103] The main frequency component of braking pressure is 2Hz.

[0104] The extracted model feature vector C is: [40,10,75,0.5,2,2].

[0105] Furthermore, the characteristic data for the three different braking behavior modes—Model A (light braking mode), Model B (heavy braking mode), and Model C (continuous braking mode)—can be determined based on the driver's actual driving records. Specifically, this involves obtaining data from the driver's actual driving records, including brake pedal position (reflecting the degree to which the driver depresses the brake pedal), vehicle speed (current vehicle speed), braking distance (distance from the start of braking to a complete stop), braking time (time from the start of braking to a complete stop), vehicle braking system intervention time (time when the anti-lock braking system begins to intervene), brake pressure (pressure value in the braking system), braking frequency (number of braking actions per unit time), and the main frequency components of brake pressure (frequency components obtained through Fourier transform). In other words, the data obtained from the driver's actual driving records can determine the average brake pressure, standard deviation of brake pressure, maximum brake pressure, average braking duration, braking frequency, and the main frequency components of brake pressure corresponding to different braking behavior modes.

[0106] In this embodiment, the formula for calculating the vector distance between the actual feature vector and each model feature vector is as follows:

[0107]

[0108] in: For vector distance, This represents the i-th element in the actual feature vector. The i-th element in the model feature vector is the index number of the element in the vector. The number of elements in the vector. For the actual feature vector, These are the model feature vectors.

[0109] In other words, the vector distances between the actual feature vector extracted in step S2.1 and the three model feature vectors extracted above are as follows:

[0110] The vector distance between the model feature vector A and the ... model feature vector A is:

[0111]

[0112] The vector distance between the model feature vector B and the vector distance between the model feature vector B is:

[0113]

[0114] The vector distance between the model feature vector C and the vector distance between the model feature vector C is:

[0115]

[0116] That is, the vector distance between the actual feature vector and the model feature vector A is 1.438, the vector distance between the actual feature vector and the model feature vector B is 1.436, and the vector distance between the actual feature vector and the model feature vector C is 0.05.

[0117] Step S2.3: Compare the three vector distances obtained in step S2.2 and determine the vector distance with the smallest value. The predefined braking system response model corresponding to the vector distance with the smallest value is the braking system response model corresponding to the driver's braking behavior mode.

[0118] In the actual implementation process, among all the vector distances between the actual feature vector and the model feature vector, the vector distance between the actual feature vector and the model feature vector C is the smallest. That is to say, among the three predefined braking system response models, the driver is currently executing model C (continuous pedaling model).

[0119] Step S3: Interact with the braking system response model matched in step S2.3 and the simulated braking model. Simultaneously, display the simulation results of the simulated braking model through the display interface, and adjust the vehicle braking system based on the simulation results. In this embodiment, during the display of the simulation results of the simulated braking model, the results can be presented in the form of charts, animations, etc., allowing the driver to clearly see the current response of the vehicle braking system. Specific implementation can be selected based on the simulation results, including:

[0120] When the simulation results are the same as the driver's preset driving results, the vehicle braking system can be directly adjusted according to the corresponding braking parameters in the braking system response model matched in step S2.3.

[0121] Conversely, when the simulation results differ from the driver's preset driving results, the braking system response model needs to undergo adaptive parameter adjustments. It is worth noting that in this embodiment, the adaptive parameter adjustments are made based on the driver's driving state. Simultaneously, the adaptively adjusted braking system response model interacts with the simulated braking model until the simulation results match the driver's preset driving results.

[0122] Furthermore, when the driver is in motion, the braking system response model is adjusted systematically according to braking distance, braking time, and braking smoothness, specifically as follows:

[0123] If the braking distance is incorrect, only the braking distance will be adjusted, while the braking time and braking smoothness will not be adjusted.

[0124] When the braking distance is correct but the braking time is incorrect, only the braking time should be adjusted, while the braking distance and braking smoothness should not be adjusted.

[0125] When the braking distance and braking time are correct but the braking smoothness is incorrect, only the braking smoothness should be adjusted, and the braking distance and braking time should not be adjusted.

[0126] Furthermore, when the driver is not in motion, the driver can adjust the braking distance, braking time, and braking smoothness within a preset braking parameter adjustment range through the interactive interface. It is worth noting that the braking parameter adjustment range in this embodiment is set based on safety standard data, driving habit data, and expert opinion data. Specifically, the maximum intersection range determined from these three data sets constitutes the braking parameter adjustment range in this embodiment. The safety standard data is obtained from vehicle manufacturers, relevant regulations, and industry standards; the driving habit data is obtained from actual driving records, user surveys, etc.; and the expert opinion data consists of the opinions and suggestions of professionals collected through interviews, questionnaires, etc.

[0127] During the implementation process, the initial sensor data obtained are as follows:

[0128] Braking distance: 8m.

[0129] Braking time: 1.5s.

[0130] Vehicle braking system intervention time: 0.25s earlier.

[0131] The driver's preset driving result is:

[0132] Braking distance: 7.5m.

[0133] Braking time: 1.5s.

[0134] Vehicle braking system intervention time: 0.25s earlier.

[0135] Furthermore, in this embodiment, if it is detected that the driver is currently in a driving state, and the simulation result differs from the preset driving result (i.e., the braking distance is 8m > 7.5m, while the braking time and the vehicle braking system intervention time are correct), then the braking distance is automatically adjusted from 8m to 7.5m, and a new simulation is performed using the simulated braking model. The new simulation result is as follows:

[0136] Braking distance: 7.5m.

[0137] Braking time: 1.5s.

[0138] Vehicle braking system intervention time: 0.25s earlier.

[0139] This indicates that the simulation results are consistent with the preset driving results, and the adaptive adjustment has ended.

[0140] Furthermore, in this embodiment, if it is detected that the driver is not currently driving, the driver can adjust the parameters themselves through the interactive interface. That is, the driver can directly adjust the braking distance to 7.5m in the interactive interface, so that the simulation result displayed on the interactive interface is consistent with the preset driving result.

[0141] This embodiment also provides a real-time brake data analysis system, which uses the aforementioned real-time brake data analysis method. The specific implementation process of this real-time brake data analysis system is as follows:

[0142] During the implementation process, the sensor data collected in real time by the driver while driving the vehicle is as follows:

[0143] Brake pedal position: 20%-80%.

[0144] Vehicle speed: 0-100km / h.

[0145] Braking distance: 5-15m.

[0146] Braking time: 1-3 seconds.

[0147] Vehicle braking system intervention time: 0.1-0.5 seconds earlier.

[0148] The following features can be extracted from the sensor data mentioned above:

[0149] Statistical characteristics:

[0150] Average braking distance: 10m.

[0151] Average braking time: 2 seconds.

[0152] Average vehicle braking system intervention time: 0.3s earlier.

[0153] Temporal characteristics:

[0154] Brake pedal position change rate: 0.5% / s.

[0155] Vehicle speed change rate: 5 .

[0156] Frequency domain characteristics:

[0157] Main frequency component: 2Hz.

[0158] Data analysis based on the above statistical, time-domain, and frequency-domain characteristics reveals that the driver's braking behavior pattern is a continuous braking mode. The predefined braking system response model corresponding to this continuous braking mode is as follows:

[0159] Braking distance: 7-9m.

[0160] Braking time: 1.3-1.8s.

[0161] Vehicle braking system intervention time: 0.15-0.35s earlier.

[0162] Furthermore, after adjusting the vehicle braking system parameters based on the above matching results, the parameters are as follows:

[0163] Braking distance: 8m.

[0164] Braking time: 1.5s.

[0165] Vehicle braking system intervention time: 0.25s earlier.

[0166] Example 2

[0167] refer to Figure 3 and Figure 4 This embodiment provides a method for real-time analysis of braking data. The specific implementation method is the same as that in Embodiment 1. The difference is that the invention will be illustrated below with reference to the specific implementation method of this embodiment.

[0168] This embodiment uses driver driving data as an example to adjust the vehicle braking system based on the simulation results of the simulated braking model. Unlike the adjustment method in Embodiment 1, this embodiment uses adaptive parameter adjustment based on the driver's driving preferences, as detailed below:

[0169] Step SB3.1: Determine the driver's driving preference behavior based on real-time operating data of the vehicle's braking system (e.g., braking pressure, braking time, braking distance). This involves using data mining and machine learning techniques to conduct in-depth analysis of the collected braking data and extract the driver's driving preference characteristics. For example, the distribution of braking pressure and braking time can be used to determine whether the driver prefers sudden or smooth braking. Braking distance can be used to assess the driver's ability to predict and control braking distance.

[0170] It is worth noting that before extracting the driver's driving preference features, the collected braking data needs to be preprocessed, including but not limited to cleaning (such as noise reduction and filling in missing values) and normalization, in order to improve the accuracy of the braking data.

[0171] In this embodiment, extracting the driver's driving preference features includes:

[0172] Step SB3.1.1: Preprocess the operating data of the vehicle braking system, and based on the preprocessed operating data, determine the relationship between the data distribution and the driver's driving behavior, specifically as follows:

[0173] In this embodiment, the central tendency and dispersion of the operating data are obtained by analyzing the distribution of braking pressure and braking time, thereby determining the driver's braking habits. Specifically, the central tendency during braking is determined by calculating the average braking pressure and average braking time. The dispersion during braking is determined by calculating the standard deviation of braking pressure and the standard deviation of braking time.

[0174] During the implementation process, the braking data shown in the table below was collected in real time:

[0175] Time (s) Brake pressure (%) Braking time (s) 1 35 0.6 2 40 0.5 3 38 0.55 4 36 0.6 5 37 0.5 6 39 0.55 7 35 0.6 8 41 0.5 9 38 0.55 10 36 0.6

[0176] Based on the data in the table above, and referring to... Figure 5 and Figure 6 It can be seen that the average braking pressure is 37.30%, the average braking time is 0.55s, the standard deviation of braking pressure is 2.05%, and the standard deviation of braking time is 0.04s.

[0177] In other words, the driver's average braking pressure was 37.30%, and the average braking time was 0.55 seconds, indicating that the driver's braking pressure and time were relatively stable. The standard deviation of braking pressure was 2.05%, and the standard deviation of braking time was 0.04 seconds, indicating that the driver's pressure and time fluctuated little in different braking events, and that the braking habits were relatively consistent.

[0178] Furthermore, by analyzing the correlations between braking data, we can identify the factors influencing driver preferences. Specifically, we can determine the relationships between different characteristics using correlation coefficients, such as the correlation coefficient between braking pressure and braking time.

[0179]

[0180] in: The correlation coefficient between braking pressure and braking time. Let i be the braking pressure for the i-th braking event. The average braking pressure across all braking events. Let i be the braking time of the i-th braking event. The average braking time for all braking events. This represents the total number of braking events.

[0181] Furthermore, by using cluster analysis of braking data (such as K-means, DBSCAN, etc.), different driving preference groups can be identified.

[0182] Step SB3.1.2: Take the factors influencing the driver's braking habits and driving preferences and the driving preference groups determined in step SB3.1.1 above, and make comprehensive settings to obtain comprehensive feature data. At the same time, use the comprehensive feature data as input to the machine learning model (such as support vector machine, decision tree and random forest) and output to obtain the driver's driving preference behavior.

[0183] Step SB3.2: Match the simulation results of the simulated braking model with the driving preference behavior determined in step SB3.1.2 to determine the driving preference behavior corresponding to the simulation results, and adaptively adjust the vehicle braking system according to the preference data corresponding to the driving preference behavior.

[0184] The simulation results during the actual implementation are as follows:

[0185] The simulated braking distance is 8m.

[0186] The simulated braking time is 1.5 seconds.

[0187] The simulated vehicle braking system intervention time is 0.25 seconds earlier.

[0188] The driver's preferred driving behavior was determined to be "smooth braking" using a machine learning model, and the corresponding preference data is as follows:

[0189] The simulated braking distance is 7-9m.

[0190] The simulated braking time is 1.3-1.8 seconds.

[0191] The simulated vehicle braking system intervention time is 0.15-0.35 seconds earlier.

[0192] Based on the preference data corresponding to driving preference behavior, adjust the braking system parameters as follows:

[0193] The simulated braking distance is 8m, which is within the preferred data range (7-9m), so no adjustment is needed.

[0194] The simulated braking time is 1.5s, which is within the preferred data range (1.3-1.8s), so no adjustment is needed.

[0195] The simulated vehicle braking system intervention time is 0.25s earlier, which is within the preferred data range (0.15-0.35s earlier), so no adjustment is made.

[0196] In other words, the simulation results are compared with the preferred data range. If the simulation results are outside the preferred data range, the braking system parameters are adjusted to fall within the preferred data range. If all the above braking data are within the preferred data range, then the above braking data does not need to be changed.

[0197] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A method for real-time analysis of braking data, characterized in that, The analytical methods include: Pattern determination: Acquire real-time operating data of the vehicle braking system to identify and determine the driver's braking behavior pattern; Pattern matching: Set multiple predefined braking system response models and automatically match the driver's braking behavior patterns with the braking system response models; Interactive Adjustment: The matched braking system response model interacts with the simulated braking model, while the simulation results of the simulated braking model are displayed on the interface. The vehicle braking system is then adjusted based on these simulation results. Specifically: When the simulation result is the same as the driver's preset driving result, the vehicle braking system is adjusted according to the braking parameters corresponding to the simulated braking model. When the simulation results are different from the driver's preset driving results, the braking system response model will perform adaptive parameter adjustments and interact with the simulated braking model until the simulation results are the same as the driver's preset driving results.

2. The method for real-time analysis of braking data according to claim 1, characterized in that, Identifying and determining the driver's braking behavior pattern includes: Acquire real-time operating data of the vehicle braking system, and extract statistical features, time-domain features, and frequency-domain features from the real-time operating data; The statistical features, time-domain features, and frequency-domain features are classified using a recognition algorithm to determine the driver's braking behavior patterns: light pressing, heavy pressing, and continuous pressing.

3. The method for real-time analysis of braking data according to claim 1, characterized in that, The setting of the braking system response model includes: By extracting statistical features, time-domain features, and frequency-domain features from the real-time operating data of the vehicle braking system, the actual feature vector corresponding to the driver's braking behavior mode is determined. Obtain the model feature vector in each predefined braking system response model, and determine the vector distance between the actual feature vector and each model feature vector; All vector distances are compared to determine the vector distance with the smallest value. The predefined braking system response model corresponding to the vector distance with the smallest value is the braking system response model corresponding to the driver's braking behavior mode.

4. The method for real-time analysis of braking data according to claim 3, characterized in that, The vector distance between the actual feature vector and each model feature vector is specifically: ,in: For vector distance, This represents the i-th element in the actual feature vector. The i-th element in the model feature vector is the index number of the element in the vector. The number of elements in the vector. For the actual feature vector, These are the model feature vectors.

5. The method for real-time analysis of braking data according to claim 1, characterized in that, When the braking system response model performs adaptive parameter adjustment, it adjusts according to the driver's driving state, specifically as follows: When the driver is in a driving state: The braking system response model is adjusted systematically according to braking distance, braking time, and braking smoothness. When the driver is not in a driving state: The driver can adjust the braking distance, braking time, and braking smoothness within a preset braking parameter adjustment range through an interactive interface.

6. The method for real-time analysis of braking data according to claim 5, characterized in that, The braking system response model is adjusted systematically according to braking distance, braking time, and braking smoothness, specifically as follows: If the braking distance is incorrect, the braking distance will be adjusted, but the braking time and braking smoothness will not be adjusted. When the braking distance is correct but the braking time is incorrect, the braking time is adjusted, but the braking distance and braking smoothness are not adjusted. When the braking distance and braking time are correct but the braking smoothness is incorrect, the braking smoothness is adjusted, but the braking distance and braking time are not adjusted.

7. The method for real-time analysis of braking data according to claim 1, characterized in that, Adjusting the vehicle braking system based on the simulation results also includes: Based on real-time operating data of the vehicle's braking system, determine the driver's driving preference behavior; The simulation results of the simulated braking model are matched with driving preference behaviors to determine the driving preference behaviors corresponding to the simulation results. Based on the preference data corresponding to the driving preference behaviors, the vehicle braking system is adaptively adjusted.

8. The method for real-time analysis of braking data according to claim 7, characterized in that, The determination of the driver's driving preference behavior includes: The operating data of the vehicle braking system is preprocessed, and based on the preprocessed operating data, the relationship between the data distribution and the driver's driving behavior is determined, specifically: By analyzing the distribution of braking pressure and braking time, the central tendency and dispersion of the operational data are obtained, thereby determining the driver's braking habits. By identifying the correlations between braking data, the factors influencing driver driving preferences can be determined. Cluster analysis of braking data identifies different driving preference groups; The factors influencing the driver's braking habits, driving preferences, and driving preference groups are comprehensively set to obtain comprehensive feature data. At the same time, the comprehensive feature data is used as the input of the machine learning model to output the driver's driving preference behavior.

9. A real-time braking data analysis system, characterized in that, The method for real-time analysis of braking data as described in any one of claims 1-8 was used.

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