An automotive motion state evaluation method and system based on automotive CAN signals

Abstract

The present invention relates to a method and system for evaluating the motion state of an automobile based on automotive CAN signals, belonging to the technical field of automotive safety and fault diagnosis. The method includes constructing a vehicle model feature database, obtaining the vehicle identification code when the vehicle starts and matching the corresponding DBC file, dynamically adapting the bus communication parameters, parsing the CAN message to obtain the vehicle motion parameters; setting the communication cycle according to the vehicle motion parameters and communication requirements, including a static segment and a dynamic segment, triggering data transmission by monitoring the change amount of the vehicle state parameters; adopting a multi-sensor data fusion algorithm to perform real-time estimation and correction of the vehicle motion state; and finally triggering stability control according to the vehicle motion state through the lateral acceleration threshold and the yaw rate deviation. The accurate evaluation of the vehicle motion state and fault warning are realized, and the driving safety and fault diagnosis efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automotive safety and fault diagnosis, and particularly relates to a method and system for evaluating the motion state of an automobile based on automotive CAN signals. Background Art

[0002] The CAN bus uses differential signal transmission (CAN_H and CAN_L), and its logical state is represented by a dominant level (logical 0) and a recessive level (logical 1). However, the encoding methods for the same motion parameters (such as vehicle speed and steering angle) may vary among different vehicle models or ECUs. For example, the data length, offset, and scale factor are different. For instance, the steering wheel angle may use 2-byte data with a resolution of 1 / 1024 rad / bit, while the resolution of the yaw angular velocity is 1 / 8192 rad / s / bit. Such differences require dynamic adaptation of the decoding rules; otherwise, parameter calculation errors will occur.

[0003] The CAN bus adopts a non-destructive arbitration mechanism, and messages with low priority may be delayed or lost due to frequent arbitration failures. When the bus load exceeds 2000 frames per second, high-priority messages (such as engine control signals) will preempt the bandwidth, resulting in transmission delays of motion state-related signals (such as acceleration and steering angle), affecting real-time performance. In addition, the 8-byte data frame limit of traditional CAN protocols (such as CAN2.0) may require splitting the transmission of long data (such as combined inertial navigation information), further increasing the delay. The judgment of the motion state requires integrating CAN signals with external sensor data (such as GPS and IMU). For example, the vehicle speed can be calculated by integrating the wheel speed sensor or the accelerometer, but both have problems of noise accumulation and drift, and need to be fused through Kalman filtering. However, the sampling frequencies of different sensors (such as CAN is usually 100Hz and GPS is 10Hz) and data transmission delays (such as CAN bus arbitration time) may cause timestamp asynchronization, affecting the fusion accuracy. In scenarios such as hard acceleration and emergency braking, vehicle mechanical vibration and electromagnetic interference may cause CAN signal noise. For example, the high-frequency noise of the motor drive system may be coupled to the CAN bus through the common ground loop, resulting in abnormal dominant level amplitude (outside the range of -12V to +12V), and then generating error frames. In addition, wire harness aging or damaged shielding layer will reduce the anti-interference ability, resulting in signal distortion. High-speed CAN requires configuring 120Ω terminal resistors at both ends of the bus to suppress signal reflection. If the ECU internal resistor is repeatedly configured with the vehicle's resistor box, it will cause bus impedance mismatch and signal distortion. For example, in a certain vehicle model, due to the ECU not disabling the internal resistor, the bus level is abnormal, and the motion state signal shows periodic fluctuations. Real-time processing of CAN signals needs to run on an embedded platform (such as AutoSAR), but complex algorithms (such as particle filtering or multi-model fusion) may exceed the computing power of the MCU.

[0004] Therefore, the judgment of the vehicle motion state based on CAN signals faces multi-dimensional challenges such as signal parsing, real-time processing, protocol differences, and algorithm verification. To solve these problems, it is necessary to further explore the potential of CAN FD / XL and address the new requirements of multi-network convergence and information security. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention provides a method and system for evaluating the vehicle motion state based on automotive CAN signals;

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A method for evaluating the vehicle motion state based on automotive CAN signals, comprising:

[0008] S1: Construct a vehicle model feature database; when the vehicle starts, obtain the vehicle identification code, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and after completing the configuration of the bus communication parameters, parse the CAN message to obtain the vehicle motion parameters;

[0009] S2: Set a communication cycle composed of a static segment and a dynamic segment according to the vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment; trigger data transmission according to the change amount of the vehicle motion parameters within the dynamic segment and transmit it through the communication network; update the reference value of the vehicle motion parameters;

[0010] S3: Obtain the original motion data through the sensor and the reference value of the vehicle motion parameters transmitted by the CAN bus, perform time synchronization on the original motion data and the reference value of the vehicle motion parameters, and then perform initial state estimation to obtain the initial motion state; predict the vehicle motion state at the current moment by combining the initial motion state with the vehicle motion model;

[0011] S4: Trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

[0012] Specifically, the dynamic adaptation method is:

[0013] Set the baud rate range list during system initialization and initialize the baud rate register; try different baud rate values one by one according to the order of the baud rate range list; for each attempted baud rate value, configure the bus communication parameters and send a test frame to the CAN bus; listen for the response on the bus and determine whether a correct response signal is received;

[0014] If a correct response signal is received, match the currently attempted baud rate with the bus baud rate and record the baud rate value;

[0015] If no matching baud rate is found after trying all baud rate values, return an error message indicating that the baud rate cannot be matched;

[0016] According to the confirmed baud rate value, configure the bus communication parameters, including the baud rate and the synchronization jump width; after completing the parameter configuration, receive and send CAN messages normally.

[0017] Specifically, the static segment synchronizes the clocks of each node through the CAN bus based on a fixed sampling period and generates a set of static time slots; distributes the longitudinal speed to the high-priority time slots and transmits the lateral offset data through the middle time slots of the static segment; the longitudinal speed and the lateral offset data are transmitted adjacent to each other in the time slot sequence, and the data is transmitted periodically through a zero-order hold.

[0018] Specifically, the trigger conditions for the transmission of activation data in the dynamic segment include state error trigger and emergency event trigger; for the state error trigger, a state error needs to be defined:

[0019] ,

[0020] where ζ(t) represents the state error at time t, which is used to measure the difference between the current state x(t) and the state x(t k at the previous sampling time t k );

[0021] Determine through the state error and the trigger threshold, and trigger data transmission when satisfied:

[0022] ,

[0023] where ζ T (t) represents the transposed vector with the state error as the vector, Φ is a positive definite matrix, σ is the trigger threshold, ε is a normal constant to prevent the denominator from being zero, is the vector length of the state x(t k );

[0024] The emergency event trigger is to detect a collision risk or an actuator failure and force the dynamic segment communication to be triggered.

[0025] Specifically, the triggering threshold is dynamically adjusted according to vehicle state parameters, calculated based on state measurement values, without introducing additional dynamic terms; and is dynamically adjusted using a barrier function, , where ||x|| is the vehicle state norm, and a and b are the sensitivity and adjustment coefficients of the control barrier function, to improve the communication priority in emergency situations. The dynamic update rule of the triggering threshold is:

[0026] ,

[0027] where σ0 is the reference threshold.

[0028] Specifically, the time synchronization method is as follows: capture timestamps during frame transceiver and parse the CAN log file, determine the absolute time origin through the file header information of the log file, map the hardware counter to the absolute time axis of the log file through synchronization events; use GPS as the main reference source and the CAN global clock as the backup; dynamically switch the reference source and record the switching event by real-time monitoring of GPS signal loss and CAN clock drift.

[0029] Specifically, the initial state estimation method is as follows:

[0030] Form an observation vector from the time-aligned data; the observation vector includes the raw data acceleration and angular velocity obtained by sensors, as well as the steering angle and vehicle speed of CAN bus parameters; construct a state vector, and the state vector includes position parameters, vehicle speed, heading angle, and steering angle; the input variable is the steering angle of CAN bus parameters.

[0031] Set the initial state vector, set the initial covariance matrix based on the sensor error characteristics, and set the process noise and observation noise based on the noise covariance.

[0032] Specifically, the vehicle motion model ignores the external force influence on the vehicle and only focuses on the geometric relationship and motion trajectory of the vehicle; performs continuous-time state modeling through the vehicle kinematic equation, converts the kinematic model into state space form and discretizes it to obtain the state transition matrix, and the state transition matrix is set as:

[0033] ,

[0034] where Ф is the state transition matrix, v is the vehicle speed, is the sampling time period, L is the vehicle's front and rear wheelbase, ψ is the heading angle, and δ is the steering angle.

[0035] Specifically, the vehicle motion state prediction method is as follows:

[0036] Construct a state prediction equation and a covariance prediction equation according to the state transition matrix derived from the vehicle motion model:

[0037] ,

[0038] ,

[0039] wherein, ê k|k-1 is the state vector predicting the state at time k based on the state at time k-1, Ф is the state transition matrix, Ф T is the transpose matrix of the state transition matrix, B is the control input matrix, u k is the input variable, ê k-1|k-1 is the state vector estimated based on all available information at time k-1, which is the optimal state estimate of the Kalman filter at time k-1, P k|k-1 is the covariance matrix predicting the covariance at time k based on the covariance at time k-1, P k-1|k-1 is the posterior estimation covariance matrix at time k-1, the uncertainty of the state estimate after combining measurement information at time k-1, Q is the process noise;

[0040] Calculate the Kalman gain according to the covariance matrix, and correct the predicted state by combining the sensor observation value through Kalman filtering:

[0041] ,

[0042] ,

[0043] wherein, K k is the Kalman gain, H is the observation matrix, H T is the transpose matrix of the observation matrix, z k is the result of sensor and CAN data fusion after time synchronization, R is the observation noise, ê k|k is the current state estimate value of the Kalman filter.

[0044] A vehicle motion state evaluation system based on vehicle CAN signals, comprising: a dynamic signal parsing module, a communication bandwidth optimization module, a multi-sensor fusion and synchronization module, and a security evaluation module;

[0045] The dynamic signal parsing module is used to construct a vehicle model feature database; when the vehicle is started, obtain the vehicle identification code, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and after completing the bus communication parameter configuration, parse the CAN message to obtain the vehicle motion parameters;

[0046] The communication bandwidth optimization module is used to set a communication cycle composed of a static segment and a dynamic segment according to vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment; trigger data transmission according to the change amount of the vehicle motion parameters in the dynamic segment, and transmit through the communication network; update the reference value of the vehicle motion parameters.

[0047] The multi-sensor fusion and synchronization module is used to obtain the original motion data through sensors and the reference value of the vehicle motion parameters transmitted by the CAN bus parameters, perform time synchronization on the original motion data and the reference value of the vehicle motion parameters, and then perform initial state estimation to obtain the initial motion state; predict the vehicle motion state at the current moment by combining the initial motion state with the vehicle motion model.

[0048] The safety evaluation module is used to trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

[0049] The beneficial effects of the present invention are as follows:

[0050] By dynamically adjusting the communication cycle and trigger threshold, the real-time and efficient monitoring of the vehicle motion state is realized, unnecessary data transmission is reduced, the bus load is reduced, and the communication efficiency and bandwidth utilization rate are improved. At the same time, the multi-sensor data fusion algorithm is adopted, comprehensively considering the data characteristics of different sensors and the time synchronization problem, and the accuracy and robustness of data fusion are improved. In addition, the present invention also solves the problem of different encoding methods for the same motion parameter by different vehicle models or ECUs by constructing a vehicle model feature database and dynamic adaptation decoding rules, improving the versatility and adaptability of the system. In terms of safety, by triggering stability control through the lateral acceleration threshold and the yaw rate deviation, the change of the vehicle motion state can be monitored and responded to in real time, ensuring the stability and safety of the vehicle in a complex and changeable driving environment. Brief Description of the Drawings

[0051] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the drawings.

[0052] Figure 1 It is a schematic flow chart of an automotive motion state evaluation method based on automotive CAN signals of the present invention;

[0053] Figure 2 It is a schematic flow chart of the motion parameter analysis in the present invention. Detailed Embodiments

[0054] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manners, structures, features and their effects of the present invention as follows.

[0055] Please refer to Figure 1 , a method for evaluating the motion state of an automobile based on automobile CAN signals, comprising:

[0056] S1: Construct a vehicle model feature database; when the vehicle starts, obtain the vehicle identification code, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and after completing the configuration of the bus communication parameters, parse the CAN message to obtain the vehicle motion parameters;

[0057] In this embodiment, the process of motion parameter parsing is as Figure 2 shown; the DBC file not only contains the scale factor and offset, but also needs to declare the start bit, length and byte order of the signal through the SG_ field; the same vehicle identification code (VIN) may correspond to multiple DBC file variants, and the vehicle identification code needs to be secondarily matched through the ECU hardware version number; a three-layer mapping is implemented using a relational database: VIN → vehicle model ID → DBC file group → signal parameter table, supporting fuzzy query and dynamic update. Among them, the baud rate adaptive algorithm adopts a dual-mode detection algorithm. In the active mode, a test frame is sent, and the baud rate matching is confirmed through the TXOK flag bit. In the passive mode, the bus traffic is monitored, and the baud rate is deduced by measuring the bit time; after successful matching, the message data is extracted according to the signal definition, and the multiplexed signal uses the switch bit to realize the signal group switching.

[0058] In this embodiment, the VIN code is obtained by connecting a diagnostic tool (such as the X431 / WDS system) through the OBD-II interface, and the VIN in the ECU is read in accordance with the SAE J2534 / J1962 standard. The field structure of the vehicle model feature database is VIN prefix code | vehicle model platform | corresponding DBC file path | recommended baud rate list.

[0059] S2: Set a communication cycle composed of a static segment and a dynamic segment according to the vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment; trigger data transmission according to the change amount of the vehicle motion parameters within the dynamic segment and transmit it through the communication network; update the reference value of the vehicle motion parameters;

[0060] S3: Obtain the original motion data through sensors and the reference values of vehicle motion parameters transmitted by the CAN bus. After synchronizing the time of the original motion data and the reference values of vehicle motion parameters, perform an initial state estimation to obtain the initial motion state; predict the vehicle motion state at the current moment by combining the initial motion state with the vehicle motion model;

[0061] In this embodiment, the sensor clock is a 16-bit unsigned integer. Determine whether a rollover occurs by the change in the clock value between two consecutive polls; generate a continuously increasing sequence by superimposing the number of cycles; check whether the packet age is within 0 - 0.033 seconds to avoid cycle slips;

[0062] For the first 7 samples in the accelerometer / gyroscope data packet, perform interpolation based on the time difference between the timestamp of the last sample and the next data packet. The formula is:

[0063] ,

[0064] where N is the number of samples in the data packet, bit sampling time interval;

[0065] Perform extrapolation on the first data packet;

[0066] The CAN control provides a free-running 32-bit counter that captures timestamps during frame transmission and reception; parse the CAN log file (ASC file format), determine the absolute time origin through the file header information, and map the hardware counter to the absolute time axis of the ASC through a synchronization event (GPS PPS signal); use GPS as the main reference source and the CAN global clock as a backup; adopt a "longitudinal confirmation + lateral comparison" mechanism to monitor the loss of GPS signals or CAN clock drift in real time, dynamically switch the reference source, and record the switching event.

[0067] S4: Trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

[0068] In this embodiment, according to the evaluation result of the vehicle motion state, combine the preset safety thresholds and the driving strategy library to evaluate the safety of the vehicle motion state and generate corresponding driving assistance instructions or warning signals;

[0069] The safety thresholds include but are not limited to vehicle speed thresholds, acceleration thresholds, yaw rate thresholds, etc., which are used to determine whether the vehicle is in a dangerous state;

[0070] The driving strategy library contains safe driving rules and emergency handling measures for different driving scenarios. According to the vehicle motion state and safety assessment results, corresponding driving strategies are matched to generate driving assistance instructions, such as automatic deceleration, emergency braking, lane keeping, etc., or trigger warning signals to alert the driver;

[0071] In this embodiment, the safety assessment module is integrated into the vehicle control unit (VCU), which receives the motion state estimation results in real time, compares them with the preset safety thresholds. When the thresholds are exceeded, warning signals are triggered and sent to the dashboard or HUD display via the vehicle bus. At the same time, according to the matched driving strategies, the vehicle is controlled by the actuator to perform corresponding safety operations.

[0072] Specifically, the dynamic adaptation method is as follows:

[0073] Set the baud rate range list during system initialization and initialize the baud rate register; try different baud rate values one by one according to the order of the baud rate range list; for each tried baud rate value, configure the bus communication parameters and send a test frame to the CAN bus; listen to the response on the bus and determine whether a correct response signal is received;

[0074] If a correct response signal is received, match the currently tried baud rate with the bus baud rate and record the baud rate value;

[0075] If no matching baud rate is found after trying all baud rate values, return an error message indicating that the baud rate cannot be matched;

[0076] According to the confirmed baud rate value, configure the bus communication parameters, including the baud rate and the synchronization jump width; after completing the parameter configuration, receive and send CAN messages normally.

[0077] Specifically, the static segment synchronizes the clocks of each node through the CAN bus based on a fixed sampling period and generates a set of static time slots; distributes the longitudinal speed to high-priority time slots and transmits the lateral offset data through the middle time slots of the static segment; the longitudinal speed and the lateral offset data are transmitted adjacent to each other in the time slot sequence, and the data is transmitted periodically through a zero-order hold.

[0078] In this embodiment, the static segment synchronizes the clocks of each node through the CAN bus based on a fixed sampling period to generate a set of static time slots; the longitudinal speed, as the core control parameter, is distributed to high-priority time slots and the lateral offset data is transmitted through the middle time slots of the static segment to balance real-time performance and bandwidth occupancy. If an emergency deviation is detected, the dynamic segment resources are preempted; the longitudinal speed and the lateral offset data are transmitted adjacent to each other in the time slot sequence for easy synchronous processing by the chassis controller.

[0079] Specifically, the triggering conditions for the activation of data transmission within the dynamic segment include state error triggering and emergency event triggering; state error triggering requires defining a state error:

[0080] ,

[0081] where ζ(t) represents the state error at time t, which is used to measure the difference between the current state x(t) and the state x(t k at the previous sampling time t k ).

[0082] Judgment is made through the state error and the triggering threshold, and data transmission is triggered when the condition is met:

[0083] ,

[0084] where ζ T (t) represents the transposed vector with the state error as the vector, Φ is a positive definite matrix, σ is the triggering threshold, ε is a normal constant to prevent the denominator from being zero, is the vector length of the state x(t k ).

[0085] The emergency event triggering is to detect a collision risk or actuator failure and force the dynamic segment communication to be triggered.

[0086] Specifically, the triggering threshold is dynamically adjusted according to vehicle state parameters, calculated based on state measurement values, without introducing additional dynamic terms; it is dynamically adjusted using a barrier function, , where ||x|| is the vehicle state norm, a and b are the sensitivity and adjustment coefficients of the control barrier function, to improve the communication priority in emergency situations. The dynamic update rule of the triggering threshold is:

[0087] ,

[0088] where σ0 is the reference threshold.

[0089] Specifically, the time synchronization method is as follows: Capture timestamps during frame transceiver and parse the CAN log file, determine the absolute time origin through the file header information of the log file, and map the hardware counter to the absolute time axis of the log file through a synchronization event; Use GPS as the main reference source and the CAN global clock as the backup; Dynamically switch the reference source and record the switching event by real-time monitoring of GPS signal loss and CAN clock drift.

[0090] Specifically, the initial state estimation method is:

[0091] The time-aligned data are composed into an observation vector; the observation vector includes the original data acceleration and angular velocity acquired by the sensor, as well as the steering angle and vehicle speed of the CAN bus parameters; a state vector is constructed, and the state vector includes position parameters, vehicle speed, heading angle, and steering angle; the input variable is the steering angle of the CAN bus parameters.

[0092] Set the initial state vector, set the initial covariance matrix based on the sensor error characteristics, and set the process noise and observation noise based on the noise covariance.

[0093] Specifically, the vehicle motion model ignores the external force influence on the vehicle and only focuses on the geometric relationship and motion trajectory of the vehicle; continuous-time state modeling is performed through the vehicle kinematic equation, and the kinematic model is converted into state space form and discretized to obtain the state transition matrix, and the state transition matrix is set as:

[0094] ,

[0095] where Ф is the state transition matrix, v is the vehicle speed, is the sampling time period, L is the wheelbase of the vehicle front and rear, ψ is the heading angle, and δ is the steering angle.

[0096] Specifically, the prediction method of the vehicle motion state is as follows:

[0097] Construct a state prediction equation and a covariance prediction equation according to the state transition matrix derived from the vehicle motion model:

[0098] ,

[0099] ,

[0100] where, ê k|k-1 is the state vector for predicting the k-th moment based on the state at the k-1 moment, Ф is the state transition matrix, Ф T is the transpose matrix of the state transition matrix, B is the control input matrix, u k is the input variable, ê k-1|k-1 is the state vector estimated based on all available information at the k-1 moment, which is the optimal state estimate of the Kalman filter at the k-1 moment, P k|k-1 is the covariance matrix for predicting the k-th moment based on the covariance at the k-1 moment, P k-1|k-1 is the posterior estimation covariance matrix at the k-1 moment, the uncertainty of the state estimation after combining the measurement information at the k-1 moment, and Q is the process noise;

[0101] Calculate the Kalman gain according to the covariance matrix, and correct the predicted state by combining the sensor observation values through Kalman filtering:

[0102] ,

[0103] ,

[0104] where K k is the Kalman gain, H is the observation matrix, and H T is the transpose matrix of the observation matrix, z k is the result of sensor and CAN data fusion after time synchronization, R is the observation noise, and ê k|k is the current state estimate of the Kalman filter.

[0105] A vehicle motion state evaluation system based on automotive CAN signals, comprising: a dynamic signal parsing module, a communication bandwidth optimization module, a multi-sensor fusion and synchronization module, and a safety evaluation module;

[0106] The dynamic signal parsing module is used to construct a vehicle model feature database; when the vehicle starts, obtain the vehicle identification code, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and after completing the bus communication parameter configuration, parse the CAN message to obtain vehicle motion parameters;

[0107] The communication bandwidth optimization module is used to set a communication cycle composed of a static segment and a dynamic segment according to vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment; trigger data transmission according to the change amount of the vehicle motion parameters in the dynamic segment and transmit it through the communication network; update the reference value of the vehicle motion parameters;

[0108] The multi-sensor fusion and synchronization module is used to obtain the original motion data through sensors and the reference value of the vehicle motion parameters transmitted by the CAN bus parameters, perform time synchronization on the original motion data and the reference value of the vehicle motion parameters, and then perform an initial state estimate to obtain an initial motion state; predict the vehicle motion state at the current moment by combining the initial motion state with the vehicle motion model;

[0109] The safety evaluation module is used to trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

[0110] When the lateral acceleration exceeds a preset threshold, it may indicate that the vehicle is performing a sharp steering operation, and at this time, the stability of the vehicle may be affected. By monitoring the lateral acceleration, once its value exceeds the safe range, the system triggers the stability control mechanism to prevent dangerous situations such as rollover or loss of control.

[0111] At the same time, the yaw rate deviation is also a key indicator for evaluating vehicle stability. The yaw rate reflects the rotation rate of the vehicle around the vertical axis and should normally match the driver's steering intention. When the system detects an abnormal deviation in the yaw rate, it indicates that the vehicle may lose stability due to factors such as uneven road surface, insufficient tire grip, or suspension system failure. At this time, the stability control system will quickly intervene and help the vehicle return to a stable state by adjusting braking force distribution, engine torque output, or suspension system settings, etc.

[0112] In summary, by triggering stability control through the lateral acceleration threshold and yaw rate deviation, the method of the present invention can monitor and respond to changes in the vehicle's motion state in real time, ensuring the stability and safety of the vehicle in complex and changing driving environments.

[0113] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0114] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0115] The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing. The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or, alternatively, can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0116] As described above, the above are only the preferred embodiments of the present invention, and there is no any formal limitation to the present invention. Although the present invention has been disclosed above in the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or refinements to the equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and refinement made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the motion state of a vehicle based on vehicle CAN signals, characterized in that Including: S1: Construct a vehicle model feature database; when the vehicle starts, obtain the vehicle identification code, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and after completing the bus communication parameter configuration, parse the CAN message to obtain the vehicle motion parameters; S2: Set a communication cycle composed of a static segment and a dynamic segment according to the vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment; trigger data transmission according to the change amount of the vehicle motion parameters within the dynamic segment, and transmit it through the communication network; update the reference value of the vehicle motion parameters; S3: Obtain the original motion data through the sensor and the reference value of the vehicle motion parameters transmitted by the CAN bus, perform time synchronization on the original motion data and the reference value of the vehicle motion parameters, and then perform an initial state estimation to obtain the initial motion state; Predict the vehicle motion state at the current moment through the vehicle motion model in combination with the initial motion state; S4: Trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

2. The method according to claim 1, characterized in that, The dynamic adaptation method for dynamically adapting the bus communication parameters according to the CAN database file in S1 is: Set a baud rate range list during system initialization and initialize the baud rate register; try different baud rate values one by one according to the order of the baud rate range list; for each attempted baud rate value, configure the bus communication parameters and send a test frame to the CAN bus; listen to the response on the bus to determine whether a correct response signal is received; If a correct response signal is received, match the currently attempted baud rate with the bus baud rate and record the baud rate value; If no matching baud rate is found after trying all baud rate values, return an error message indicating that the baud rate cannot be matched; According to the confirmed baud rate value, configure the bus communication parameters, including the baud rate and the synchronization jump width; after completing the parameter configuration, normally receive and send CAN messages.

3. The method according to claim 1, wherein In S2, the static segment is based on a fixed sampling period, synchronizes the clocks of each node through the CAN bus, and generates a set of static time slots; distributes the longitudinal speed to the high-priority time slots, and transmits the lateral offset data through the middle time slots of the static segment; the longitudinal speed and the lateral offset data are transmitted adjacent to each other in the time slot sequence, and the data is transmitted periodically through a zero-order hold.

4. The method according to claim 1, wherein The trigger conditions for activating data transmission within the dynamic segment in S2 include state error trigger and emergency event trigger; for the state error trigger, a state error needs to be defined: ， Among them, ζ(t) represents the state error at time t, which is used to measure the difference between the current state x(t) and the state x(t k ) at the previous sampling time t k ). Judge through the state error and the trigger threshold, and trigger data transmission when satisfied: ， where ζ T (t) represents the transposed vector with the state error as the vector, Φ is a positive definite matrix, σ is the triggering threshold, and ε is a positive constant to prevent the denominator from being zero. is the vector length of the state x(t k ); The emergency event trigger is to detect a collision risk or an actuator failure and forcibly trigger the dynamic segment communication.

5. The method according to claim 4, characterized in that The trigger threshold is dynamically adjusted according to vehicle state parameters, calculated based on state measurement values, without introducing additional dynamic terms; it is dynamically adjusted using a barrier function. , where ||x|| is the vehicle state norm, and a and b are the sensitivity and adjustment coefficients of the control barrier function, to improve the communication priority in emergency situations. The dynamic update rule of the trigger threshold is as follows: ， Among them, σ0 is the reference threshold.

6. The method according to claim 1, characterized in that, The time synchronization method described in S3 is as follows: capture timestamps during frame transmission and reception and parse the CAN log file, determine the absolute time origin through the file header information of the log file, and map the hardware counter to the absolute time axis of the log file through synchronization events; use GPS as the main reference source and the CAN global clock as a backup; dynamically switch the reference source and record the switching event by real-time monitoring of GPS signal loss and CAN clock drift.

7. The method according to claim 1, characterized in that, The initial state estimation method described in S3 is as follows: Form an observation vector with the time-aligned data; the observation vector includes the original data acceleration and angular velocity obtained by the sensor, as well as the steering angle and vehicle speed of the CAN bus parameters; construct a state vector, and the state vector includes position parameters, vehicle speed, heading angle, and steering angle; the input variable is the steering angle of the CAN bus parameters. Set the initial state vector, set the initial covariance matrix based on the sensor error characteristics, and set the process noise and observation noise based on the noise covariance.

8. The method according to claim 7, wherein The vehicle motion model ignores the external force influence on the vehicle and only focuses on the geometric relationship and motion trajectory of the vehicle; perform continuous-time state modeling through the vehicle kinematic equation, convert the kinematic model into state space form and discretize it to obtain the state transition matrix, and the state transition matrix is set as: ， where Ф is the state transition matrix, v is the vehicle speed, is the sampling time period, L is the wheelbase of the vehicle, ψ is the heading angle, and δ is the steering angle.

9. The method according to claim 7, wherein The prediction method for the vehicle motion state is as follows: Construct a state prediction equation and a covariance prediction equation according to the state transition matrix derived from the vehicle motion model: ， ， where, ê k|k-1 is the state vector predicted at time k based on the state at time k-1, Ф is the state transition matrix, Ф T is the transpose matrix of the state transition matrix, B is the control input matrix, u k is the input variable, ê k-1|k-1 is the state vector estimated at time k-1 based on all available information, which is the optimal state estimate of the Kalman filter at time k-1, P k|k-1 is the covariance matrix predicted at time k based on the covariance at time k-1, P k-1|k-1 is the posterior estimation covariance matrix at time k-1, the uncertainty of the state estimate after combining measurement information at time k-1, Q is the process noise; Calculate the Kalman gain according to the covariance matrix, and correct the predicted state through Kalman filtering combined with the sensor observation value: ， ， Among them, K k is the Kalman gain, H is the observation matrix, and H T is the transpose matrix of the observation matrix, z k is the fusion result of the sensor and CAN data after time synchronization, R is the observation noise, and ê k|k is the current state estimate of the Kalman filter.

10. A vehicle motion state evaluation system based on vehicle CAN signals, for implementing a vehicle motion state evaluation method based on vehicle CAN signals according to any one of claims 1-9, characterized in that, It includes: Dynamic signal analysis module, communication bandwidth optimization module, multi-sensor fusion and synchronization module, security evaluation module; The dynamic signal analysis module is used to construct a vehicle model feature database; obtain the vehicle identification code when the vehicle starts, match the associated DBC file in the vehicle model feature database based on the vehicle identification code, dynamically adapt the bus communication parameters according to the CAN database file, and parse the CAN message to obtain the vehicle motion parameters after completing the bus communication parameter configuration. The communication bandwidth optimization module is used to set a communication cycle composed of a static segment and a dynamic segment according to the vehicle motion parameters and communication requirements, and set the communication cycle duration, static segment duration, dynamic segment time threshold, and trigger threshold; use the static segment duration as the start stage of the communication cycle, initialize the monitoring of the vehicle motion parameters at the end of the static segment duration, and enter the dynamic segment based on the vehicle motion parameters at the current moment. Trigger data transmission according to the change amount of the vehicle motion parameters in the dynamic segment and transmit it through the communication network; update the reference value of the vehicle motion parameters. The multi-sensor fusion and synchronization module is used to obtain the original motion data through the sensor and the reference value of the vehicle motion parameters transmitted by the CAN bus parameters, perform time synchronization on the original motion data and the reference value of the vehicle motion parameters, and perform initial state estimation to obtain the initial motion state. Predict the vehicle motion state at the current moment through the vehicle motion model combined with the initial motion state. The safety evaluation module is used to trigger stability control according to the vehicle motion state at the current moment through the lateral acceleration threshold and the yaw rate deviation.

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