Automobile brake detection system
By acquiring brake pedal pressure and wheel speed in real time, alarm signals with theoretical deceleration and deviation coefficient are generated, solving the problem of untimely detection of the braking system in the existing technology and improving the safety of the vehicle during driving.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI SANLIAN UNIV
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-12
Smart Images
Figure CN122016335A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of vehicle inspection technology, specifically vehicle inspection technology, and more specifically, automotive brake inspection systems. Background Technology
[0002] As cars have become increasingly common and are now found in almost every household, domestic demand for automobiles has surged, making the automotive industry a pillar industry. Given the high speeds cars travel on the road, their safety is not only a matter of life and property for drivers and passengers but also affects the safety of other pedestrians. Therefore, testing the performance of vehicle braking systems is particularly important; it is an indispensable and crucial part of the numerous testing procedures in automobile production.
[0003] Current automotive braking system testing often relies on periodic maintenance and manual inspections. While manual inspections offer high safety, they are inefficient and cannot meet the real-time monitoring needs of modern vehicles. During operation, vehicles are susceptible to various random events, such as severe weather and sudden changes in road conditions, all of which can potentially impact the braking system. Furthermore, unsafe driving practices can lead to braking system malfunctions, which are often only addressed after an accident, preventing timely detection and increasing the probability of brake system failure, thus compromising driver safety. Therefore, a new automotive braking system testing system is needed. Summary of the Invention
[0004] This application aims to at least solve one of the technical problems existing in the prior art; to this end, this application proposes an automotive brake detection system to solve the technical problem that existing vehicles have difficulty in timely detecting the brake system, resulting in reduced vehicle safety.
[0005] To achieve the above objectives, the first aspect of this application provides an automotive braking detection system, comprising: a data acquisition module, a braking detection module, a warning module, and a database; The data acquisition module acquires the brake pedal pressure and wheel speed in real time through the data acquisition device connected to it. The braking detection module: acquires brake pedal pressure data; the pressure data consists of pedal pressures collected from the start of the current braking action to the current acquisition time; groups the pressure data into several pressure data groups; generates theoretical deceleration based on the pressure data of each pressure data group; acquires the wheel speed corresponding to each pressure data group; generates a deviation coefficient based on the wheel speed and theoretical deceleration; and generates an alarm signal based on the deviation coefficient.
[0006] This application acquires the brake pedal pressure and wheel speed in real time during the braking process, groups the pressure data into several pressure data groups, generates a theoretical deceleration based on the pressure data of each pressure data group, acquires the wheel speed corresponding to each pressure data group, generates a deviation coefficient based on the wheel speed and theoretical deceleration, generates an alarm signal based on the deviation coefficient, and analyzes the pedal pressure and wheel speed during the braking process in real time to determine whether there are any abnormalities in the braking system, ensuring that abnormalities in the vehicle braking system can be detected in a timely manner and improving the safety of the vehicle during driving.
[0007] Preferably, the pressure data is grouped to obtain several pressure data groups, including: Extract the pedal pressure corresponding to each acquisition time in the pressure data; fit the pedal pressure into a pedal pressure change curve according to its corresponding acquisition time; divide the pedal pressure change curve into several pedal pressure change curve segments according to the set pedal pressure range, and divide the pedal pressure data corresponding to the pedal pressure curve segments into a pressure data group, thereby obtaining several pedal pressure data groups in sequence. Preferably, the step of generating theoretical deceleration based on pressure data from each pressure data set includes: The average value of each pedal pressure in each pressure data group is obtained and marked as the characteristic pressure value of the pressure data group; the characteristic pressure value is input into the deceleration prediction model to obtain the predicted deceleration corresponding to the pressure data group; the deceleration prediction model is obtained by training an artificial intelligence model. The system retrieves several historical braking data points from a database; generates a transmission influence coefficient based on the historical braking data; generates a theoretical deceleration based on the transmission influence coefficient and the estimated deceleration; and sequentially retrieves the theoretical deceleration corresponding to each pressure data group.
[0008] Preferably, the deceleration prediction model is obtained by training an artificial intelligence model, including: Several pedal pressures and their corresponding decelerations are obtained from a database; the deceleration is the deceleration of the wheel rotation speed; the correspondence between pedal pressure and deceleration recorded in the database is a normal correspondence, that is, a theoretical correspondence, which is similar to the pedal pressure and its corresponding deceleration when the car is new; several pedal pressures and decelerations are integrated into several sets of training data and test data. The AI model is trained using training data; the trained AI model is tested using testing data; an AI model is obtained with pedal pressure as input and its corresponding deceleration as output; the deceleration is used as the predicted deceleration as output, and finally a deceleration prediction model is obtained with pedal pressure as input and predicted deceleration as output; the AI model includes a BP neural network model and an RBF neural network model.
[0009] Preferably, the step of generating the transmission influence coefficient based on historical braking data includes: Extract several pressure data sets from historical braking data, along with their corresponding historical deceleration and recording time. The recording time of each pressure data set corresponds to the earliest acquisition time of the pedal pressure within that set. Obtain the historical deceleration corresponding to several pressure data sets within the same defined interval and mark them as follows: ; i is the number of the pressure data group within the same defined interval; extract the pressure characteristic values from each pressure data group and label them as follows. ; Through formula The transmission influence coefficient YX is calculated; where I is the total number of pressure data sets within the same set interval. The weighting coefficients are for the pressure data group numbered i. Obtain the transmission influence coefficients corresponding to each set interval in sequence.
[0010] Preferably, the step of generating theoretical deceleration based on the transmission influence coefficient and the estimated deceleration includes: Obtain the transmission influence coefficient of the pressure data set corresponding to the estimated deceleration; Through formula The theoretical deceleration LV corresponding to the estimated deceleration is calculated; where YV is the estimated deceleration. The theoretical deceleration corresponding to each estimated deceleration is obtained sequentially.
[0011] Preferably, the step of generating the deviation coefficient based on the wheel speed and theoretical deceleration includes: The wheel speed corresponding to each pedal pressure in each pressure data group is obtained. The average actual deceleration corresponding to each pressure data group is calculated based on the wheel speed. The theoretical deceleration corresponding to the pressure data group is obtained. The difference between the average actual deceleration and the corresponding theoretical deceleration is obtained to obtain the deceleration difference value. The deceleration difference values corresponding to each pressure data group are weighted and summed to obtain the comprehensive deviation value, and the comprehensive deviation value is used as the deviation coefficient.
[0012] Preferably, generating the alarm signal based on the deviation coefficient includes: If the deviation coefficient is greater than the set deceleration deviation threshold, a braking system abnormality alarm signal is generated; otherwise, a braking system abnormality warning signal is generated when the deviation coefficient is not zero. The alarm signals include a braking system abnormality alarm signal and a braking system abnormality warning signal.
[0013] Preferably, the early warning module is used to issue an alarm based on the alarm signal.
[0014] Preferably, the database is used to store pressure data and its corresponding wheel speed, including: When the pedal pressure returns to zero, acquire several pressure data sets and the corresponding average actual deceleration, and store them sequentially according to the order of the first acquisition time corresponding to the pedal pressure in the pressure data set.
[0015] Compared with the prior art, the beneficial effects of this application are: 1. This application acquires the brake pedal pressure and wheel speed during the current braking process in real time, groups the pressure data into several pressure data groups; generates theoretical deceleration based on the pressure data of each pressure data group; acquires the wheel speed corresponding to each pressure data group; generates a deviation coefficient based on the wheel speed and theoretical deceleration; generates an alarm signal based on the deviation coefficient; and analyzes the pedal pressure and wheel speed during the braking process in real time to determine whether there are any abnormalities in the braking system, ensuring that abnormalities in the vehicle's braking system can be detected in a timely manner and improving the safety of the vehicle during driving.
[0016] 2. This application uses wheel rotation speed analysis instead of wheel travel speed analysis to avoid incorrect analysis of the braking system due to slippery road surface, thus ensuring the accuracy of braking system detection. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of the module connections of the automotive brake detection system in this application; Figure 2 This is a flowchart illustrating the steps of the automotive brake testing system in this application. Detailed Implementation
[0019] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0020] Please see Figures 1-2 The first aspect of this application provides an automotive braking detection system, including: a data acquisition module, a braking detection module, a warning module, and a database; Data acquisition module: It acquires the brake pedal pressure and wheel speed in real time through the data acquisition device connected to it; the pedal pressure is the pressure applied when the vehicle brake pedal is pressed, which is obtained through a pressure sensor installed on the brake pedal; the vehicle speed is the average speed of several wheels controlled by the braking system, which is acquired through a speed sensor; when the pedal pressure is not zero, it starts recording the pedal pressure and wheel speed at each acquisition and recording time; when the pedal pressure returns to zero, it stops recording the pedal pressure and wheel speed; the acquisition time is a pre-set acquisition signal, such as sequential acquisition at 2-second intervals, then every 2 seconds is a acquisition time. Braking detection module: Acquires brake pedal pressure data; the pressure data consists of the pedal pressure collected from the start of the current braking operation to the current acquisition time; the pressure data is grouped into several pressure data groups, which are obtained by dividing the collected pedal pressures according to their magnitude and sequence; theoretical deceleration is generated based on the pressure data of each pressure data group; the theoretical deceleration is determined by the material structure of the corresponding vehicle's braking system and is affected by factors such as aging and wear of the braking mechanism, representing the wheel deceleration under the corresponding pedal pressure; acquires several wheel speeds corresponding to each pressure data group; generates a deviation coefficient based on the wheel speed and theoretical deceleration; the deviation coefficient is the deviation between the theoretical deceleration and the actual deceleration from the start of the current braking operation to the current time, and an alarm signal is generated based on the deviation coefficient.
[0021] The early warning module is used to generate an audible alarm based on the alarm signal.
[0022] The database stores pressure data and its corresponding wheel speeds. This includes acquiring several pressure data sets and their corresponding average actual decelerations when the pedal pressure returns to zero, and storing them sequentially according to the order of the first acquisition time corresponding to the pedal pressure in each pressure data set. It can be understood that the pressure data sets and their corresponding average actual decelerations stored in the database are the same as the pressure data sets and historical decelerations from the historical braking data.
[0023] This embodiment acquires the brake pedal pressure and wheel speed in real time during the braking process, groups the pressure data into several pressure data groups, generates a theoretical deceleration based on the pressure data of each pressure data group, acquires the wheel speed corresponding to each pressure data group, generates a deviation coefficient based on the wheel speed and theoretical deceleration, and generates an alarm signal based on the deviation coefficient. It analyzes the pedal pressure and wheel speed during the braking process in real time to determine if there are any abnormalities in the braking system, ensuring timely detection of abnormalities in the vehicle's braking system and improving vehicle safety during driving.
[0024] The pressure data is grouped to obtain several pressure data groups, including: Extract the pedal pressure corresponding to each acquisition time from the pressure data; fit the pedal pressure to its corresponding acquisition time to form a pedal pressure variation curve; the pedal pressure variation curve is the change curve of pedal pressure over time from the start of braking to the current time. The fitting method can be interpolation or other methods with better results; divide the pedal pressure variation curve into several pedal pressure variation curve segments according to the set pedal pressure range. The pedal pressure curve segments are several relatively flat curve segments divided according to the degree of pedal pressure change, and the pedal pressure within each curve segment is not significantly different; group the pedal pressure data corresponding to the pedal pressure curve segments into a pressure data group, and obtain several... Pedal pressure data set; specifically, normal pedal pressure ranges from 0-300N. In this embodiment, the pressure step size of the device is 20 Newtons, so 0-20N, 20-40N; and so on, the pedal pressure variation curve is divided into several segments. The corresponding pedal pressure values in each segment should belong to the same set range. It is understood that the set range can also be divided unequally. In another embodiment, the greater the pedal pressure, the more detailed the corresponding range division, such as 0-60N as one range, 60-100N as one range, and 100-125N as one range. Since the braking capacity difference is not large when the pedal pressure is small, and the braking capacity difference is too large when the pedal pressure is large, unequal division is more reasonable.
[0025] The theoretical deceleration is generated based on the pressure data of each pressure data group, including: obtaining the average value of each pedal pressure in each pressure data group and marking it as the characteristic pressure value of the pressure data group; the pressure characteristic value is the average pedal pressure within the time period corresponding to the pressure data group, and inputting the characteristic pressure value into the deceleration prediction model to obtain the predicted deceleration corresponding to the pressure data group; the predicted deceleration is the deceleration of the wheel under the corresponding pedal pressure, determined by the material structure of the braking system of the corresponding vehicle; the deceleration prediction model is obtained through training an artificial intelligence model; The system obtains several historical braking data points from a database; it generates a transmission influence coefficient based on the historical braking data; the transmission influence coefficient represents the difference between the ability of pedal pressure to be converted into braking force due to wear and tear on the mechanical components of the braking system caused by various reasons during vehicle use and the ability of pedal pressure to be converted into braking force when the vehicle leaves the factory. The larger the transmission influence coefficient, the greater the difference between them; it generates a theoretical deceleration based on the transmission influence coefficient and the estimated deceleration; and it sequentially obtains the theoretical deceleration corresponding to each pressure data group.
[0026] The deceleration prediction model is trained using an artificial intelligence model, including: obtaining several pedal pressures and their corresponding decelerations from a database; using the deceleration as the deceleration of the wheel rotation speed; the correspondence between pedal pressure and deceleration recorded in the database is a normal correspondence, that is, a theoretical correspondence, which is similar to the pedal pressure and its corresponding deceleration when the car is new; and integrating several pedal pressures and decelerations into several sets of training data and test data. The AI model is trained using training data and then tested using test data. Specifically, the pedal pressure from the test data is input into the trained AI model to obtain the corresponding deceleration output. If the difference between the output deceleration and the deceleration recorded in the test data is within an acceptable range, the test data passes the test, and the next set of test data is tested. Otherwise, the relevant parameters of the AI are adjusted until the output meets the requirements, and the next set of test data is tested. This process continues until a set proportion of test data passes the test, resulting in an AI model with pedal pressure as input and corresponding deceleration as output. The deceleration is then used as the predicted deceleration output, ultimately resulting in a deceleration prediction model with pedal pressure as input and predicted deceleration as output. The AI model includes a BP neural network model and an RBF neural network model.
[0027] The process of generating a transmission influence coefficient based on historical braking data includes: extracting several pressure data sets from the historical braking data, along with their corresponding historical decelerations and recording times, wherein the recording time of each pressure data set is the acquisition time corresponding to the earliest pedal pressure within that set; obtaining the historical decelerations corresponding to several pressure data sets within the same defined interval, and marking them as follows: ; i represents the number of the pressure data group within the same set interval. This can be understood as i being the number of each pressure data group after it has been sequentially sorted according to the recording time within the same set interval. When i=1, it indicates that the pressure data group consists of the first pedal pressure data collected within the set interval. The pressure feature values in each pressure data group are extracted and marked as... ; Through formula The transmission influence coefficient YX is calculated; where I is the total number of pressure data sets within the same set interval. This refers to the weighting coefficient corresponding to the pressure data group numbered i; the specific value is set based on experience, and in this embodiment... The specific value is set according to the size of i. The larger i is, the closer the corresponding pressure data group is to the current time, and the closer the state of the corresponding braking system is to the current state of the braking system; therefore, its corresponding weight coefficient should be set to a larger value. Obtain the transmission influence coefficients corresponding to each set interval in sequence.
[0028] This embodiment calculates the current transmission influence coefficient using the above formula. When the value of unit pedal pressure converted into deceleration changes more significantly in historical braking data, it indicates that the braking system's ability to convert pedal pressure into braking force has decreased, meaning that the wear of various components in the braking system is more severe, and the corresponding transmission influence coefficient is larger. The transmission influence coefficient needs to be updated every time braking occurs to ensure its accuracy.
[0029] The theoretical deceleration is generated based on the transmission influence coefficient and the estimated deceleration, including: obtaining the transmission influence coefficient of the pressure data set corresponding to the estimated deceleration; Through formula The theoretical deceleration LV corresponding to the estimated deceleration is calculated; where YV is the estimated deceleration; the theoretical deceleration corresponding to each estimated deceleration is obtained in sequence.
[0030] This embodiment corrects the predicted deceleration by passing an influence coefficient to obtain a theoretical deceleration that is closest to the current state of the vehicle's braking system, making subsequent analysis more accurate.
[0031] The deviation coefficient is generated based on wheel speed and theoretical deceleration, including: obtaining the wheel speed corresponding to each pedal pressure within each pressure data group; calculating the average actual deceleration corresponding to each pressure data group based on the wheel speed; specifically, obtaining the wheel speed corresponding to two adjacent acquisition times; calculating the existing deceleration based on the wheel speed corresponding to two adjacent acquisition times and the time difference between the acquisition times; and taking the average of the decelerations corresponding to each pressure data group as the average actual deceleration; obtaining the theoretical deceleration corresponding to the pressure data group; subtracting the average actual deceleration from its corresponding theoretical deceleration to obtain the deceleration difference; and weighted summing the deceleration differences corresponding to each pressure data group to obtain the deceleration difference. The comprehensive deviation value is used as the deviation coefficient. The weight value here is set based on experience; it can be set equally or according to different pedal pressure values. The more accurate the theoretical deceleration of the pedal, the larger the corresponding weight value should be. Specifically, the actual deceleration and theoretical deceleration corresponding to several pressure data sets for each set interval are obtained. The difference between the actual deceleration and the theoretical deceleration is calculated. The average value of the difference between the pressure data sets corresponding to different times in the same set interval is calculated. The smaller the average value, the more accurate the theoretical deceleration of the corresponding set area is. That is, the more accurate the theoretical deceleration of the pressure data set corresponding to the set interval is, the larger its corresponding weight coefficient should be.
[0032] Since different pedal pressures have different effects on wheel deceleration, it can be understood that the wear or aging of various components in the braking system has different effects on pedal pressure, resulting in different proportional relationships between the theoretical decelerations corresponding to different pedal pressures. This embodiment ensures the accuracy of the deviation coefficient by considering the difference between the theoretical deceleration and the actual deceleration corresponding to different pressure data sets, thereby ensuring the accuracy of subsequent warning signals.
[0033] The alarm signal is generated based on the deviation coefficient, including: determining whether the deviation coefficient is greater than a set deceleration deviation threshold, the specific value of which is set based on experience; if yes, a braking system abnormality alarm signal is generated; if no, a braking system abnormality warning signal is generated when the deviation coefficient is not zero; the alarm signal includes a braking system abnormality alarm signal and a braking system abnormality warning signal; generating a braking system abnormality alarm signal indicates that the current braking system has malfunctioned and may cause an accident at any time, and a safe area should be found immediately for parking and repair; generating a braking system abnormality warning signal indicates that the current braking system has a probability of malfunctioning in the future and should be repaired in a timely manner.
[0034] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.
[0035] How this application works: This application acquires the brake pedal pressure and wheel speed in real time during the braking process, groups the pressure data into several pressure data groups, generates a theoretical deceleration based on the pressure data of each pressure data group, acquires the wheel speed corresponding to each pressure data group, generates a deviation coefficient based on the wheel speed and theoretical deceleration, generates an alarm signal based on the deviation coefficient, and analyzes the pedal pressure and wheel speed during the braking process in real time to determine whether there are any abnormalities in the braking system, ensuring that abnormalities in the vehicle braking system can be detected in a timely manner and improving the safety of the vehicle during driving.
[0036] The above embodiments are only used to illustrate the technical methods of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of this application without departing from the spirit and scope of the technical methods of this application.
Claims
1. An automotive brake detection system, including: The system comprises a data acquisition module, a braking detection module, and a database; its features are as follows: The data acquisition module acquires the brake pedal pressure and wheel speed in real time through the data acquisition device connected to it. The brake detection module: acquires brake pedal pressure data; groups the pressure data to obtain several pressure data groups; generates theoretical deceleration based on the pressure data of each pressure data group; acquires the wheel speed corresponding to each pressure data group; generates a deviation coefficient based on the wheel speed and theoretical deceleration; and generates an alarm signal based on the deviation coefficient.
2. The automotive brake detection system according to claim 1, characterized in that, The pressure data is grouped to obtain several pressure data groups, including: Extract the pedal pressure corresponding to each acquisition time in the pressure data; fit the pedal pressure into a pedal pressure change curve according to its corresponding acquisition time; divide the pedal pressure change curve into several pedal pressure change curve segments according to the set pedal pressure range, and divide the pedal pressure data corresponding to the pedal pressure curve segments into a pressure data group, thereby obtaining several pedal pressure data groups in sequence.
3. The automotive brake detection system according to claim 1, characterized in that, The process of generating theoretical deceleration based on pressure data from each pressure data set includes: The average value of each pedal pressure in each pressure data group is obtained and marked as the characteristic pressure value of the pressure data group; the characteristic pressure value is input into the deceleration prediction model to obtain the predicted deceleration corresponding to the pressure data group; the deceleration prediction model is obtained by training an artificial intelligence model. The system retrieves several historical braking data points from a database; generates a transmission influence coefficient based on the historical braking data; generates a theoretical deceleration based on the transmission influence coefficient and the estimated deceleration; and sequentially retrieves the theoretical deceleration corresponding to each pressure data group.
4. The automotive brake detection system according to claim 3, characterized in that, The deceleration prediction model is obtained through training an artificial intelligence model and includes: Several pedal pressures and their corresponding decelerations are obtained from the database; these pedal pressures and decelerations are then integrated into several sets of training and testing data. The AI model is trained using training data; the trained AI model is tested using validation data; an AI model is obtained with pedal pressure as input and its corresponding deceleration as output; the deceleration is used as the predicted deceleration as output, and finally a deceleration prediction model is obtained with pedal pressure as input and predicted deceleration as output.
5. The automotive brake detection system according to claim 3, characterized in that, The generation of the transmission influence coefficient based on historical braking data includes: Extract several pressure data sets from historical braking data, along with their corresponding historical deceleration and recording time; obtain the historical deceleration corresponding to several pressure data sets within the same set interval, and mark them as follows: ; i is the number of the pressure data group within the same defined interval; extract the pressure characteristic values from each pressure data group and label them as follows. ; Through formula The transmission influence coefficient YX is calculated; where I is the total number of pressure data sets within the same set interval. The weighting coefficients are for the pressure data group numbered i. Obtain the transmission influence coefficients corresponding to each set interval in sequence.
6. The automotive brake detection system according to claim 3, characterized in that, The process of generating theoretical deceleration based on the transmission influence coefficient and the predicted deceleration includes: Obtain the transmission influence coefficient of the pressure data set corresponding to the estimated deceleration; Through formula The theoretical deceleration LV corresponding to the estimated deceleration is calculated; where YV is the estimated deceleration. The theoretical deceleration corresponding to each estimated deceleration is obtained sequentially.
7. The automotive brake detection system according to claim 6, characterized in that, The process of generating the deviation coefficient based on wheel speed and theoretical deceleration includes: The wheel speed corresponding to each pedal pressure in each pressure data group is obtained. The average actual deceleration corresponding to each pressure data group is calculated based on the wheel speed. The theoretical deceleration corresponding to the pressure data group is obtained. The difference between the average actual deceleration and the corresponding theoretical deceleration is obtained to obtain the deceleration difference value. The deceleration difference values corresponding to each pressure data group are weighted and summed to obtain the comprehensive deviation value, and the comprehensive deviation value is used as the deviation coefficient.
8. The automotive brake detection system according to claim 1, characterized in that, The step of generating an alarm signal based on the deviation coefficient includes: If the deviation coefficient is greater than the set deceleration deviation threshold, a braking system abnormality alarm signal is generated; otherwise, a braking system abnormality warning signal is generated when the deviation coefficient is not zero. The alarm signals include a braking system abnormality alarm signal and a braking system abnormality warning signal.
9. The automotive brake detection system according to claim 1, characterized in that, It also includes an early warning module, which is used to issue an alarm based on an alarm signal.
10. The automotive brake detection system according to claim 1, characterized in that, The database is used to store pressure data and its corresponding wheel speed, including: When the pedal pressure returns to zero, acquire several pressure data sets and the corresponding average actual deceleration, and store them sequentially according to the order of the first acquisition time corresponding to the pedal pressure in the pressure data set.