Vehicle working condition prediction system, method and device, vehicle and storage medium
By setting up measurement modules at the center of mass and top of the vehicle chassis and combining them with a complementary filtering algorithm, the vehicle rollover trend can be accurately predicted, solving the accuracy problem of vehicle rollover prediction and improving vehicle safety.
Patent Information
- Application Number
- CN202510892682.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-27
- Publication Date
- 2025-09-16
AI Technical Summary
How to accurately predict whether a vehicle will roll over, especially during high-speed driving, existing technologies are difficult to effectively predict the trend of vehicle rollover, resulting in prominent safety issues.
Measurement modules are set up at the center of mass and top of the vehicle's chassis to collect angular velocity and acceleration. The complementary filtering algorithm combines the measurement data at different heights to predict the vehicle's operating conditions, including rollover trends and rollover conditions, and output control signals for seat belt tightening and airbag deployment.
It achieves accurate prediction of vehicle rollover trends, reduces misjudgments and erroneous actions, and improves vehicle safety, especially by providing timely warnings and protection in the early stages of rollovers.
Smart Images
Figure CN120645969A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of power supply technology, and in particular to a vehicle operating condition prediction system, a vehicle operating condition prediction method, an operating condition prediction device, a vehicle, and a computer-readable storage medium. Background Art
[0002] With the advancement of society and science and technology, vehicles have become a common means of transportation in every household. Simultaneously, vehicle speeds are increasing, and driving stability is a growing concern. Among these, vehicle rollovers, a particularly dangerous form of traffic accident, pose a particularly significant risk. Therefore, accurately predicting whether a vehicle will rollover has become a pressing issue. Summary of the Invention
[0003] The embodiments of the present application provide a vehicle operating condition prediction system, a vehicle operating condition prediction method, an operating condition prediction device, a vehicle, and a computer-readable storage medium to solve at least one of the above-mentioned technical problems.
[0004] A vehicle operating condition prediction system according to an embodiment of the present application includes:
[0005] A first measurement module is provided at the center of mass of the chassis of the vehicle and is used to collect a first angular velocity and a first acceleration of the center of mass of the chassis of the vehicle;
[0006] a second measurement module, disposed on the top of the vehicle and coaxially arranged with the first measurement module along the height direction of the vehicle, the second measurement module being configured to collect a second angular velocity and a second acceleration of the top of the vehicle;
[0007] A control module is connected to the first measurement module and the second measurement module respectively, and is used to predict the working condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity and the second acceleration.
[0008] A vehicle operating condition prediction method according to an embodiment of the present application includes:
[0009] Obtaining a first angular velocity and a first acceleration of the center of mass of the vehicle chassis;
[0010] Acquire a second angular velocity and a second acceleration of the top of the vehicle;
[0011] The operating condition of the vehicle is predicted based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0012] In some embodiments, predicting the operating condition of the vehicle based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration includes:
[0013] Determining a first roll angle of the center of mass of the vehicle chassis using a complementary filtering algorithm based on the first angular velocity and the first acceleration;
[0014] determining a second roll angle of the top of the vehicle using a complementary filtering algorithm based on the second angular velocity and the second acceleration;
[0015] determining a relative roll angle of the vehicle according to the first roll angle and the second roll angle;
[0016] The operating condition of the vehicle is predicted according to the first roll angle, the second roll angle, and the relative roll angle.
[0017] In some embodiments, determining the first roll angle of the center of mass of the vehicle chassis using a complementary filtering algorithm based on the first angular velocity and the first acceleration includes:
[0018] determining a first tilt angle according to the first angular velocity;
[0019] determining a second tilt angle according to the first acceleration;
[0020] Complementary filtering is performed on the first tilt angle and the second tilt angle to determine a first roll angle of the center of mass of the vehicle chassis.
[0021] In some embodiments, determining the second roll angle of the top of the vehicle using a complementary filtering algorithm based on the second angular velocity and the second acceleration includes:
[0022] determining a third tilt angle according to the second angular velocity;
[0023] determining a fourth tilt angle according to the second acceleration;
[0024] Complementary filtering is performed on the third tilt angle and the fourth tilt angle to determine a second roll angle of the vehicle top.
[0025] In certain embodiments, predicting the operating condition of the vehicle based on the first roll angle, the second roll angle, and the relative roll angle includes:
[0026] When the second roll angle is less than a first angle threshold, determining that the vehicle is in a normal operating condition;
[0027] When the second roll angle is greater than the first angle threshold, determining that the vehicle has a rolling tendency;
[0028] In a case where the vehicle has a rolling tendency, a working condition of the vehicle is predicted based on the first roll angle and the relative roll angle.
[0029] In certain embodiments, predicting the operating condition of the vehicle based on the first roll angle and the relative roll angle includes:
[0030] When the relative roll angle is greater than a second angle threshold and the first roll angle is less than a third angle threshold, determining that the vehicle is in a sideslip condition;
[0031] When the relative roll angle is greater than the second angle threshold and the first roll angle is greater than the third angle threshold, determining that the vehicle is in a rolling condition;
[0032] The vehicle operating condition prediction method further includes:
[0033] When it is determined that the vehicle is in a rollover condition, an ignition signal is output to control the tightening of the seat belt and / or the deployment of the airbag of the vehicle.
[0034] In certain embodiments, before predicting the vehicle operating condition based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration, the vehicle operating condition prediction method further includes:
[0035] performing low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0036] performing data verification on the filtered first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0037] If the data verification is qualified, predicting the operating condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0038] If the data verification fails, return to the step of obtaining the first angular velocity and the first acceleration of the vehicle chassis center of mass.
[0039] The vehicle operating condition prediction device of the embodiment of the present application includes:
[0040] A first acquisition module, configured to acquire a first angular velocity and a first acceleration of the center of mass of the vehicle chassis;
[0041] A second acquisition module, configured to acquire a second angular velocity and a second acceleration of the top of the vehicle;
[0042] A prediction module is used to predict the operating condition of the vehicle based on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration.
[0043] The vehicle of the embodiment of the present application includes one or more processors and a memory, and the memory stores a computer program. When the computer program is executed by the processor, the vehicle operating condition prediction method of any of the above embodiments is implemented.
[0044] The computer-readable storage medium of the embodiment of the present application stores a computer program thereon, and when the program is executed by a processor, the vehicle operating condition prediction method of any of the above-mentioned embodiments is implemented.
[0045] In the vehicle operating condition prediction system, vehicle operating condition prediction method, operating condition prediction device, vehicle, and computer-readable storage medium of the embodiments of the present application, a first measurement module is disposed at the center of mass of the vehicle's chassis to collect a first angular velocity and a first acceleration of the center of mass of the vehicle's chassis. A second measurement module is coaxially disposed on the top of the vehicle to collect a second angular velocity and a second acceleration of the top of the vehicle. The vehicle's operating condition is predicted based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration. In this way, by using two measurement modules at different heights to respectively monitor the movement trends of the bottom and top of the vehicle, the vehicle's rollover tendency can be detected promptly, thereby achieving accurate prediction of the rollover condition.
[0046] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:
[0048] Figure 1 is a side schematic diagram of a vehicle according to certain embodiments of the present application;
[0049] Figure 2 is a schematic top view of a vehicle according to certain embodiments of the present application;
[0050] Figure 3 is a module schematic diagram of a vehicle operating condition prediction system according to certain embodiments of the present application;
[0051] Figure 4 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0052] Figure 5 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0053] Figure 6 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0054] Figure 7 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0055] Figure 8 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0056] Figure 9 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0057] Figure 10 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0058] Figure 11 is a flow chart of a vehicle operating condition prediction method according to certain embodiments of the present application;
[0059] Figure 12 is a schematic diagram of a module of a vehicle operating condition prediction device according to certain embodiments of the present application;
[0060] Figure 13 is a schematic diagram of a module of a vehicle according to certain embodiments of the present application;
[0061] Figure 14 This is a schematic diagram of the connection status between a computer-readable storage medium and a processor in certain embodiments of the present application. DETAILED DESCRIPTION
[0062] The following further describes the embodiments of the present application in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions. Furthermore, the embodiments of the present application described below in conjunction with the accompanying drawings are exemplary and are intended only to explain the embodiments of the present application and are not to be construed as limiting the present application.
[0063] See also Figures 1 to 3, an embodiment of the present application provides a vehicle operating condition prediction system 100. The vehicle operating condition prediction system 100 includes a first measurement module 10, a second measurement module 20 and a control module 30. The first measurement module 10 is arranged at the center of mass of the chassis of the vehicle 1000, and is used to collect the first angular velocity and the first acceleration of the center of mass of the chassis of the vehicle 1000. The second measurement module 20 is arranged at the top of the vehicle 1000, and is coaxial with the first measurement module 10 along the height direction of the vehicle 1000. The second measurement module 20 is used to collect the second angular velocity and the second acceleration of the top of the vehicle 1000. The control module 30 is connected to the first measurement module 10 and the second measurement module 20 respectively, and the control module 30 is used to predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration.
[0064] In the vehicle operating condition prediction system 100 of the embodiment of the present application, a first measurement module 10 is positioned at the chassis center of mass of vehicle 1000 to collect a first angular velocity and a first acceleration of the chassis center of mass of vehicle 1000. A second measurement module 20 is coaxially positioned on top of vehicle 1000 to collect a second angular velocity and a second acceleration of the top of vehicle 1000. The operating condition of vehicle 1000 is predicted based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration. In this way, by using two measurement modules at different heights to monitor the motion trends of the bottom and top of vehicle 1000, respectively, any rollover tendency of vehicle 1000 can be detected promptly, enabling accurate prediction of rollover conditions.
[0065] Specifically, the vehicle operating condition prediction system 100 is applied to a vehicle 1000 and includes a first measurement module 10, a second measurement module 20, and a control module 30. The first measurement module 10 and the second measurement module 20 can be implemented as an inertial measurement unit (IMU). In one example, the first measurement module 10 and the second measurement module 20 are implemented as a 6-axis IMU.
[0066] The first measurement module 10 is disposed at the center of mass of the chassis of the vehicle 1000 and can be integrated into the electronic control unit (ECU) of the airbag system (SRS) of the vehicle 1000. The second measurement module 20 is disposed on the top of the vehicle 1000. The vehicle 1000 may include a vehicle-mounted drone 40 disposed on the top, and the second measurement module 20 can be integrated into the vehicle-mounted drone 40. The first measurement module 10 and the second measurement module 20 are coaxially disposed along the height direction of the vehicle 1000, as shown in FIG. Figure 1 As shown, the first measuring module 10 and the second measuring module 20 are coaxially arranged along the axis O1.
[0067] The first measurement module 10 may include a first gyroscope and a first accelerometer, the first gyroscope is used to collect a first angular velocity of the center of mass of the chassis of the vehicle 1000 , and the first accelerometer is used to collect a first acceleration of the center of mass of the chassis of the vehicle 1000 .
[0068] The second measurement module 20 may include a second gyroscope and a second accelerometer, the second gyroscope is used to collect a second angular velocity of the top of the vehicle 1000 , and the second accelerometer is used to collect a second acceleration of the top of the vehicle 1000 .
[0069] It is understood that the gyroscope can use the X-axis, Y-axis, and Z-axis (such as Figure 1 The accelerometer can collect accelerations in the three directions of X, Y, and Z. In the embodiment of the present application, the first angular velocity and the second angular velocity can be the angular velocity of the X axis, and the first acceleration and the second acceleration can both include accelerations of the Y axis and the Z axis.
[0070] The control module 30 may be an airbag ECU, and the control module 30 is connected to the first measurement module 10 and the second measurement module 20 respectively. The control module 30 and the first measurement module 10 and the second measurement module 20 may communicate with each other via a serial peripheral interface (SPI) communication bus.
[0071] After the first measurement module 10 collects the first angular velocity and the first acceleration, it transmits them to the control module 30 via the SPI communication bus. After the second measurement module 20 collects the second angular velocity and the second acceleration, it also transmits them to the control module 30 via the SPI communication bus. The control module 30 can process the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration to predict the operating condition of the vehicle 1000.
[0072] The operating conditions of vehicle 1000 include normal operating conditions and rollover conditions. It is understood that during a rollover, the top and bottom of vehicle 1000 may move asynchronously. For example, in the early stages of a rollover, the top of vehicle 1000 may deviate from the vertical direction more rapidly, while the bottom, closer to the ground and affected by ground friction, may move relatively slowly.
[0073] Therefore, considering the changes in the posture of the entire vehicle body, two measurement modules are installed: one at the center of mass of the chassis of vehicle 1000, and the other coaxially located on the top of vehicle 1000. These two measurement modules, located at different heights, respectively monitor the motion trends of the bottom and top of vehicle 1000. The first angular velocity and first acceleration can characterize the motion trend of the bottom of vehicle 1000, while the second angular velocity and second acceleration can characterize the motion trend of the top of vehicle 1000. Based on the first angular velocity, first acceleration, second angular velocity, and second acceleration, the operating condition of vehicle 1000 can be comprehensively predicted. This allows for timely detection of the vehicle 1000's rollover tendency, enabling accurate prediction of rollover conditions.
[0074] In addition, the two measurement modules can back up each other and improve the fault tolerance and overall performance of the vehicle operating condition prediction system through redundant data.
[0075] See also Figures 1 to 5 The embodiment of the present application further provides a vehicle operating condition prediction method, which is applied to the above-mentioned vehicle operating condition prediction system 100. The vehicle operating condition prediction method includes:
[0076] 010: Obtain the first angular velocity and first acceleration of the center of mass of the chassis of vehicle 1000;
[0077] 020: Obtain the second angular velocity and second acceleration of the top of the vehicle 1000;
[0078] 030: Predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0079] In the vehicle operating condition prediction method of the embodiment of the present application, a first measurement module 10 is positioned at the chassis center of mass of vehicle 1000 to collect a first angular velocity and a first acceleration of the chassis center of mass of vehicle 1000. A second measurement module 20 is coaxially positioned on the top of vehicle 1000 to collect a second angular velocity and a second acceleration of the top of vehicle 1000. The operating condition of vehicle 1000 is predicted based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration. In this way, by using two measurement modules at different heights to monitor the motion trends of the bottom and top of vehicle 1000, respectively, it is possible to distinguish between true rollover trends and other non-rollover dynamic behaviors, thereby achieving accurate prediction of rollover conditions.
[0080] Specifically, the first angular velocity and first acceleration of the chassis center of mass of vehicle 1000 can be obtained by a first measurement module 10 set at the chassis center of mass of vehicle 1000, and the second angular velocity and second acceleration of the top of vehicle 1000 can be obtained by a second measurement module 20 set at the top of vehicle 1000.
[0081] After the first measurement module 10 collects the first angular velocity and the first acceleration, it transmits them to the control module 30 via the SPI communication bus. After the second measurement module 20 collects the second angular velocity and the second acceleration, it also transmits them to the control module 30 via the SPI communication bus. The control module 30 can process the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration to predict the operating condition of the vehicle 1000.
[0082] See also Figure 1 、 Figure 5 and Figure 6 In some embodiments, before predicting the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration (i.e., 030), the vehicle operating condition prediction method further includes:
[0083] 040: Perform low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0084] 050: Perform data verification on the filtered first angular velocity, first acceleration, second angular velocity, and second acceleration;
[0085] 060: If the data verification is qualified, predict the operating condition of the vehicle 1000 according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0086] 070: If the data verification fails, return to the step of obtaining the first angular velocity and first acceleration of the center of mass of the chassis of the vehicle 1000.
[0087] Specifically, after the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration are acquired, low-pass filtering may be performed on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration to filter out high-frequency noise and smooth the signal.
[0088] After filtering, the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration are verified for data validity and integrity. Any data verification method can be used for data verification, and is not limited here. In one example, a cyclic redundancy check (CRC) is used to verify the data of the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0089] If the data verification passes, the subsequent operating condition prediction step can be executed to predict the operating condition of vehicle 1000 based on the first angular velocity, first acceleration, second angular velocity, and second acceleration. If the data verification fails, the process returns to the step of obtaining the first angular velocity and first acceleration of the center of mass of the chassis of vehicle 1000 and re-obtains the first angular velocity, first acceleration, second angular velocity, and second acceleration until the data verification passes.
[0090] Performing data verification on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration can verify possible errors that may occur in the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration after they are transmitted and saved, thereby improving data reliability and ensuring the accuracy of subsequent working condition predictions.
[0091] See also Figure 1 、 Figure 5 and Figure 7 In some embodiments, predicting the operating condition of the vehicle 1000 (i.e., 030) based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration includes:
[0092] 031: Determine a first roll angle of the center of mass of the chassis of the vehicle 1000 using a complementary filtering algorithm according to the first angular velocity and the first acceleration;
[0093] 032: Determine a second roll angle of the top of the vehicle 1000 using a complementary filtering algorithm based on the second angular velocity and the second acceleration;
[0094] 033: Determine a relative roll angle of the vehicle 1000 according to the first roll angle and the second roll angle;
[0095] 034: Predict the operating condition of the vehicle 1000 based on the first roll angle, the second roll angle, and the relative roll angle.
[0096] According to the first angular velocity and the first acceleration, a complementary filtering algorithm may be used to determine a first roll angle of the center of mass of the chassis of the vehicle 1000. The specific process is as follows:
[0097] See also Figure 1 、 Figure 5 and Figure 8 In some embodiments, a complementary filtering algorithm is used to determine a first roll angle (i.e., 031) of the center of mass of the chassis of the vehicle 1000 based on the first angular velocity and the first acceleration, including:
[0098] 0311: Determine a first tilt angle according to the first angular velocity;
[0099] 0312: Determine a second tilt angle according to the first acceleration;
[0100] 0313: Complementarily filter the first tilt angle and the second tilt angle to determine a first roll angle of the center of mass of the chassis of the vehicle 1000.
[0101] Specifically, by integrating the first angular velocity, the angular change of the center of mass of the chassis of the vehicle 1000 can be obtained. The calculation formula is as follows:
[0102]
[0103] Among them, θ a0 is the initial angle of the center of mass of the chassis of vehicle 1000; θ g1 is the angle after time t, that is, the first tilt angle; ω1 is the first angular velocity.
[0104] The first accelerometer in the first measurement module 10 can measure the gravity acceleration on the X axis, Y axis, and Z axis (such as Figure 1 The components on the X axis (shown in FIG. 2 ) can be used to calculate the tilt angle of the center of mass of the chassis of the vehicle 1000 relative to the direction of gravity (i.e., the Z axis). It can be understood that the tilt angle relative to the Z axis is also the rotation angle around the X axis, and the calculation formula is as follows:
[0105]
[0106] Among them, a y1 is the acceleration of the Y axis in the first acceleration; a z1 is the acceleration of the Z axis in the first acceleration; θ g2 is the inclination angle of the center of mass of the chassis of the vehicle 1000 relative to the direction of gravity, that is, the second inclination angle.
[0107] Since the first gyroscope integral in the first measurement module 10 has a drift problem, and the first accelerometer measurement is easily affected by vibration and other interference, a complementary filtering algorithm is used to perform weighted fusion on the data of the first gyroscope and the first accelerometer, that is, complementary filtering is performed on the first tilt angle and the second tilt angle to achieve weighted fusion. The complementary filtering formula is as follows:
[0108]
[0109] Among them, α is the first weight used to weight the data of the first gyroscope; 1-α is the second weight used to weight the data of the first accelerometer; θ1 is the angle after complementary filtering, that is, the first roll angle.
[0110] It can be understood that the above is a calculation formula under a continuous time system. In a discrete time system, the first tilt angle can be expressed as:
[0111] θ g1 (k) = θ g1(k-1)+ω1(k)Δt
[0112] Among them, θ g1 (k) is the first tilt angle at the kth moment, θ g1 (k-1) is the first tilt angle at the k-1th moment, ω1(k) is the first angular velocity at the kth moment, and Δt is the sampling time.
[0113] The complementary filtering formula can be as follows:
[0114]
[0115] Wherein, θ1(k) is the first roll angle.
[0116] In this way, through complementary filtering, the noise of the second accelerometer in the first measurement module 10 can be suppressed, and the drift of the second gyroscope can be suppressed at the same time, so as to obtain an accurate first roll angle.
[0117] Similarly, based on the second angular velocity and the second acceleration, a complementary filtering algorithm can be used to determine the second roll angle of the top of the vehicle 1000. The specific process is as follows:
[0118] See also Figure 1 、 Figure 5 and Figure 9 In some embodiments, a complementary filtering algorithm is used to determine a second roll angle (i.e., 032) of the top of the vehicle 1000 based on the second angular velocity and the second acceleration, including:
[0119] 0321: Determine a third tilt angle according to the second angular velocity;
[0120] 0322: Determine a fourth tilt angle according to the second acceleration;
[0121] 0323: Complementary filtering is performed on the third tilt angle and the fourth tilt angle to determine a second roll angle of the top of the vehicle 1000.
[0122] Specifically, integrating the second angular velocity can obtain the angle change of the top of the vehicle 1000. The calculation formula is as follows:
[0123]
[0124] Among them, θ b0 is the initial angle of the center of mass of the chassis of vehicle 1000; θ g3 is the angle after time t, that is, the third tilt angle; ω2 is the second angular velocity.
[0125] The second accelerometer in the second measurement module 20 can measure the gravity acceleration on the X axis, Y axis, and Z axis (such as Figure 1The components on the X axis (shown in FIG. 1 ) can be used to calculate the tilt angle of the top of the vehicle 1000 relative to the direction of gravity (i.e., the Z axis). It can be understood that the tilt angle relative to the Z axis is also the rotation angle around the X axis, and the calculation formula is as follows:
[0126]
[0127] Among them, a y2 is the acceleration of the Y axis in the second acceleration; a z2 is the acceleration of the Z axis in the second acceleration; θ g4 is the inclination angle of the top of the vehicle 1000 relative to the direction of gravity, that is, the fourth inclination angle.
[0128] Since the second gyroscope integral in the second measurement module 20 has a drift problem, and the second accelerometer measurement is easily affected by vibration and other interference, a complementary filtering algorithm is used to perform weighted fusion on the data of the second gyroscope and the second accelerometer, that is, complementary filtering is performed on the third tilt angle and the fourth tilt angle to achieve weighted fusion. The complementary filtering formula is as follows:
[0129]
[0130] Among them, α is the first weight used to weight the data of the second gyroscope; 1-α is the second weight used to weight the data of the second accelerometer; θ2 is the angle after complementary filtering, that is, the second roll angle.
[0131] It can be understood that the above is a calculation formula under a continuous time system. In a discrete time system, the third tilt angle can be expressed as:
[0132] θ g3 (k) = θ g3 (k-1)+ω2(k)Δt
[0133] Among them, θ g3 (k) is the third tilt angle at the kth moment, θ g3 (k-1) is the third tilt angle at the k-1th moment, ω2(k) is the second angular velocity at the kth moment, and Δt is the sampling time.
[0134] The complementary filtering formula can be as follows:
[0135]
[0136] Wherein, θ2(k) is the second roll angle.
[0137] In this way, through complementary filtering, the noise of the second accelerometer in the second measurement module 20 can be suppressed, and the drift of the second gyroscope can be suppressed, so as to obtain an accurate second roll angle.
[0138] After calculating the first roll angle and the second roll angle, the difference in roll angle between the top of the vehicle 1000 and the center of mass of the chassis of the vehicle 1000, i.e., the relative roll angle, can be determined based on the first roll angle and the second roll angle. The specific process is as follows:
[0139] Assume that the height difference between the first measurement module 10 and the second measurement module 20 is Δh. In one example, the height difference Δh ranges from 0.8 to 1.5 m. The height difference Δh can be 0.8 m, 0.9 m, 1.0 m, 1.1 m, 1.2 m, 1.3 m, 1.4 m, 1.5 m, or any value between 0.8 and 1.5 m.
[0140] When vehicle 1000 rolls, the motion states at the top and chassis centers of mass will differ due to the height difference Δh, and the influence of angular acceleration needs to be considered. Angular acceleration can be calculated from the change in angular velocity measured by the gyroscope. In a discrete-time system, the angular acceleration at time k is:
[0141]
[0142] Where β(k) is the angular acceleration at the kth moment.
[0143] After considering the influence of angular acceleration, the calculation formula of relative roll angle is as follows:
[0144]
[0145] Where g is the angular velocity due to gravity.
[0146] In this way, setting up two measurement modules can effectively compensate for measurement errors and calculate the accurate relative roll angle. Through mutual verification and data redundancy, performance optimization and reliability improvement can be achieved.
[0147] Thereafter, the operating condition of the vehicle 1000 may be predicted based on the first roll angle and the relative roll angle.
[0148] See also Figure 1 、 Figure 5 and Figure 10 In some embodiments, predicting the operating condition of the vehicle 1000 (i.e., 034) based on the first roll angle, the second roll angle, and the relative roll angle includes:
[0149] 0341: When the second roll angle is less than the first angle threshold, it is determined that the vehicle 1000 is in a normal operating condition;
[0150] 0342: When the second roll angle is greater than the first angle threshold, it is determined that the vehicle 1000 has a rollover tendency;
[0151] 0343: When the vehicle 1000 has a rolling tendency, the operating condition of the vehicle 1000 is predicted based on the first roll angle and the relative roll angle.
[0152] Specifically, the second measurement module 20 on top of vehicle 1000 can directly sense the vehicle's violent yaw and roll, instantly accumulating a first roll angle that reflects the driver's operating intention and the vehicle's surface motion. Therefore, the second roll angle can be used to predict whether vehicle 1000 is prone to rollover.
[0153] A first angle threshold is set to predict whether vehicle 1000 has a rollover tendency. The first angle threshold corresponds to the first roll angle. The first angle threshold can be determined based on actual application conditions, such as the driving environment of vehicle 1000, and is not limited here. In one example, the first angle threshold is set to 20 degrees.
[0154] If the second roll angle is less than the first angle threshold, it indicates that the vehicle body has not significantly yaw or roll, and the vehicle 1000 is determined to be in a normal operating condition. If the second roll angle is greater than the first angle threshold, it indicates that the vehicle body has significantly yaw and roll, and the vehicle 1000 can be determined to have a rollover tendency.
[0155] It is understood that the fact that the vehicle 1000 has a rolling tendency does not necessarily mean that the vehicle 100 is in a rolling condition; it may also mean that the vehicle 1000 is skidding. Therefore, if the vehicle 1000 has a rolling tendency, the specific operating condition of the vehicle 1000 can be further predicted based on the first roll angle and the relative roll angle.
[0156] See also Figure 1 、 Figure 5 and Figure 11 In some embodiments, predicting the operating condition of the vehicle 1000 based on the first roll angle and the relative roll angle (i.e., 0343) includes:
[0157] 03431: When the relative roll angle is greater than the second angle threshold and the first roll angle is less than the third angle threshold, it is determined that the vehicle 1000 is in a sideslip condition;
[0158] 03432: When the relative roll angle is greater than the second angle threshold and the first roll angle is greater than the third angle threshold, it is determined that the vehicle 1000 is in a rolling condition;
[0159] The vehicle operating condition prediction method further includes:
[0160] 080: When it is determined that the vehicle 1000 is in a rollover condition, an ignition signal is output to control the tightening of the seat belt and / or the deployment of the airbag of the vehicle 1000.
[0161] Specifically, a second angle threshold and a third angle threshold are set to predict the operating condition of vehicle 1000. The second angle threshold corresponds to the relative roll angle, and the third angle threshold corresponds to the first roll angle. The second and third angle thresholds can be determined based on actual application conditions, such as the driving environment of vehicle 1000, and are not limited here. In one example, the second angle threshold is set to 20 degrees, and the third angle threshold is set to 50 degrees or 60 degrees.
[0162] When the relative roll angle is less than the second angle threshold and the first roll angle is less than the third angle threshold, it means that the roll angles of the chassis center of mass of vehicle 1000 and the top of vehicle 1000 are both small, and it can be determined that vehicle 1000 is in normal operating condition.
[0163] If the relative roll angle is greater than the second angle threshold and the first roll angle is less than the third angle threshold, it indicates that the roll angle of the top of vehicle 1000 is increasing rapidly, but the roll angle of the center of mass of the chassis of vehicle 1000 is not increasing synchronously or is increasing slowly. Therefore, it can be determined that vehicle 1000 is in a side-slip condition. Side-slip conditions include vehicle body swaying and skidding.
[0164] When the relative roll angle is greater than the second angle threshold and the first roll angle is greater than the third angle threshold, it means that the roll angles of the chassis center of mass of vehicle 1000 and the top of vehicle 1000 are increasing rapidly, and the roll angle of the top of vehicle 1000 is increasing faster, accumulating a certain angle difference, which is consistent with the movement trend of the vehicle 1000 in a rolling condition. It can be determined that vehicle 1000 is in a rolling condition.
[0165] When it is determined that the vehicle 1000 is in a rollover condition, the airbag system of the vehicle 1000 can output an ignition signal to control the tightening of the seat belts and / or the deployment of the airbags of the vehicle 1000 to protect the driver and passengers in a timely manner.
[0166] When it is determined that the vehicle 1000 is in a normal operating condition or a sideslipping operating condition, there is no need to output an ignition signal, and the first roll angle, the second roll angle and the relative roll angle can be recalculated based on the most recently acquired first angular velocity, first acceleration, second angular velocity and second angular velocity, thereby predicting the latest operating condition of the vehicle 1000.
[0167] In this way, it is first predicted whether the vehicle 100 has a rolling tendency based on the second roll angle, and then it is further judged whether the vehicle 1000 is in a real rolling condition based on the first roll angle and the relative roll angle. In combination with the first roll angle, the second roll angle and the relative roll angle, multiple thresholds are used to predict the working condition of the vehicle 1000. Compared with the solution of a single measurement module and a single threshold, signs of rolling can be detected in the early stage of the rolling of the vehicle 1000. The detection sensitivity is high, and a roll warning can be issued in time, and the driver and passengers can be protected in time.
[0168] Taking vehicle 1000 driving on an off-road sandy terrain as an example, when the driver makes a sharp turn left or right or turns the steering wheel forcefully, the rollover prediction method in the related art may easily misjudge this as a rollover condition and deploy the airbag within the validity period of the algorithm.
[0169] In this embodiment of the present application, the second measurement module 20 at the top of vehicle 1000 directly senses severe yaw and roll of the vehicle body, instantly accumulating a second roll angle that reflects the driver's operating intention and the surface motion of the vehicle body. The first measurement module 10 at the chassis center of mass is less sensitive than the second measurement module 20. The first measurement module 10 captures the true yaw angular velocity (i.e., the first angular velocity) and lateral acceleration (i.e., the first acceleration). Using a complementary filtering algorithm and after error compensation, the difference between the roll angle at the chassis center of mass of vehicle 1000 and the top of vehicle 1000, or the relative roll angle, is calculated.
[0170] If the relative roll angle is large, it means that the second roll angle at the top of the vehicle 1000 increases rapidly, but combined with the first roll angle at the center of mass of the chassis, the first roll angle does not increase synchronously or increases slowly, so it can be determined as a sideslip condition rather than a real rollover condition, thereby preventing the airbag from being accidentally deployed in a non-rollover condition.
[0171] In this way, by considering the posture changes of the entire vehicle body, it is possible to distinguish between true rollover trends and other non-rollover dynamic behaviors, effectively reducing the risk of vehicle 1000 rollover misjudgment and airbag accidental deployment, and providing more accurate safety protection for drivers and passengers.
[0172] See also Figures 1 to 3 、 Figure 12 The embodiment of the present application further provides a vehicle operating condition prediction device 200, which is applied to the above-mentioned vehicle operating condition prediction system 100. The vehicle operating condition prediction device 200 includes a first acquisition module 210, a second acquisition module 220, and a prediction module 230. The first acquisition module 210 is used to obtain a first angular velocity and a first acceleration of the center of mass of the chassis of the vehicle 1000. The second acquisition module 220 is used to obtain a second angular velocity and a second acceleration of the top of the vehicle 1000. The prediction module 230 is used to predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0173] In certain embodiments, the prediction module 230 is specifically used to determine a first roll angle of the center of mass of the chassis of the vehicle 1000 based on the first angular velocity and the first acceleration using a complementary filtering algorithm; determine a second roll angle of the top of the vehicle 1000 based on the second angular velocity and the second acceleration using a complementary filtering algorithm; determine a relative roll angle of the vehicle 1000 based on the first roll angle and the second roll angle; and predict the operating condition of the vehicle 1000 based on the first roll angle and the relative roll angle.
[0174] In some embodiments, the prediction module 230 is specifically used to determine a first tilt angle based on a first angular velocity; determine a second tilt angle based on a first acceleration; and perform complementary filtering on the first tilt angle and the second tilt angle to determine a first roll angle of the center of mass of the chassis of the vehicle 1000.
[0175] In some embodiments, the prediction module 230 is specifically used to determine a third tilt angle based on the second angular velocity; determine a fourth tilt angle based on the second acceleration; and perform complementary filtering on the third tilt angle and the fourth tilt angle to determine a second roll angle of the top of the vehicle 1000.
[0176] In certain embodiments, the prediction module 230 is specifically used to determine that the vehicle 1000 is in a normal operating condition when the second roll angle is less than a first angle threshold; to determine that the vehicle 1000 has a rolling tendency when the second roll angle is greater than the first angle threshold; and to predict the operating condition of the vehicle 1000 based on the first roll angle and the relative roll angle when the vehicle 1000 has a rolling tendency.
[0177] In certain embodiments, prediction module 230 is specifically configured to determine that vehicle 1000 is in a sideslip condition when the relative roll angle is greater than a second angle threshold and the first roll angle is less than a third angle threshold; and to determine that vehicle 1000 is in a rollover condition when the relative roll angle is greater than the second angle threshold and the first roll angle is greater than the third angle threshold. Prediction module 230 is further configured to output an ignition signal to control seatbelt tightening and / or airbag deployment in vehicle 1000 if vehicle 1000 is determined to be in a rollover condition.
[0178] In some embodiments, before predicting the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration, the prediction module 230 is also used to perform low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration; perform data verification on the filtered first angular velocity, the first acceleration, the second angular velocity and the second acceleration; if the data verification is qualified, predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration; if the data verification is unqualified, return to the step of obtaining the first angular velocity and the first acceleration of the center of mass of the chassis of the vehicle 1000.
[0179] It should be noted that the explanation of the vehicle operating condition prediction method in the aforementioned embodiment is also applicable to the vehicle operating condition prediction device 200 of the embodiment of the present application, and will not be elaborated here.
[0180] See also Figure 1 and Figure 13 The embodiment of the present application also provides a vehicle 1000, which includes one or more processors 1010 and a memory 1020. The memory 1020 stores a computer program. When the computer program is executed by the processor 1010, the vehicle operating condition prediction method of any of the above embodiments is implemented.
[0181] For example, when the computer program is executed by the processor 1010, the following vehicle operating condition prediction method is implemented:
[0182] 010: Obtain the first angular velocity and first acceleration of the center of mass of the chassis of vehicle 1000;
[0183] 020: Obtain the second angular velocity and second acceleration of the top of the vehicle 1000;
[0184] 030: Predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0185] For another example, when the computer program is executed by the processor 1010, the following vehicle operating condition prediction method is implemented:
[0186] 040: Perform low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0187] 050: Perform data verification on the filtered first angular velocity, first acceleration, second angular velocity, and second acceleration;
[0188] 060: If the data verification is qualified, predict the operating condition of the vehicle 1000 according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0189] 070: If the data verification fails, return to the step of obtaining the first angular velocity and first acceleration of the center of mass of the chassis of the vehicle 1000.
[0190] It should be noted that the explanations of the vehicle operating condition prediction system 100 and the vehicle operating condition prediction method in the aforementioned embodiments are also applicable to the vehicle 1000 of the embodiments of the present application and will not be elaborated here.
[0191] See also Figure 1 and Figure 14 The embodiment of the present application further provides a computer-readable storage medium 300 on which a computer program 310 is stored. When the program is executed by a processor 320, the vehicle operating condition prediction method of any of the above embodiments is implemented.
[0192] For example, when the computer program 310 is executed by the processor 320, the following vehicle operating condition prediction method is implemented:
[0193] 010: Obtain the first angular velocity and first acceleration of the center of mass of the chassis of vehicle 1000;
[0194] 020: Obtain the second angular velocity and second acceleration of the top of the vehicle 1000;
[0195] 030: Predict the operating condition of the vehicle 1000 based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
[0196] For another example, when the computer program 310 is executed by the processor 320, the following vehicle operating condition prediction method is implemented:
[0197] 040: Perform low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0198] 050: Perform data verification on the filtered first angular velocity, first acceleration, second angular velocity, and second acceleration;
[0199] 060: If the data verification is qualified, predict the operating condition of the vehicle 1000 according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration;
[0200] 070: If the data verification fails, return to the step of obtaining the first angular velocity and first acceleration of the center of mass of the chassis of the vehicle 1000.
[0201] It should be noted that the explanations of the vehicle operating condition prediction system 100 and the vehicle operating condition prediction method in the aforementioned embodiments are also applicable to the computer-readable storage medium 300 of the embodiments of the present application and will not be elaborated here.
[0202] In summary, in the vehicle operating condition prediction system 100, vehicle operating condition prediction method, operating condition prediction device 200, vehicle 1000, and computer-readable storage medium 300 of the embodiments of the present application, the first measurement module 10 is disposed at the center of mass of the chassis of vehicle 1000 to collect the first angular velocity and first acceleration of the center of mass of the chassis of vehicle 1000. The second measurement module 20 is coaxially disposed on the top of vehicle 1000 to collect the second angular velocity and second acceleration of the top of vehicle 1000. The operating condition of vehicle 1000 is predicted based on the first angular velocity, first acceleration, second angular velocity, and second acceleration. In this way, by using two measurement modules at different heights to respectively monitor the movement trends of the bottom and top of vehicle 1000, the rollover tendency of vehicle 1000 can be promptly detected, thereby achieving accurate prediction of the rollover condition.
[0203] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.
[0204] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.
[0205] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a computer-readable storage medium can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner as necessary, and then stored in a computer memory.
[0206] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0207] Those skilled in the art will appreciate that all or part of the steps carried out in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment. In addition, the various functional units in the various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk or an optical disk, etc.
[0208] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are illustrative and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application. The scope of the present application is defined by the claims and their equivalents.
Claims
1. A vehicle operating condition prediction system, characterized in that: include: A first measurement module is provided at the center of mass of the chassis of the vehicle and is used to collect a first angular velocity and a first acceleration of the center of mass of the chassis of the vehicle; a second measurement module, disposed on the top of the vehicle and coaxially arranged with the first measurement module along the height direction of the vehicle, the second measurement module being configured to collect a second angular velocity and a second acceleration of the top of the vehicle; A control module is connected to the first measurement module and the second measurement module respectively, and is used to predict the working condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity and the second acceleration.
2. A vehicle operating condition prediction method, characterized in that: The vehicle operating condition prediction method comprises: Obtaining a first angular velocity and a first acceleration of the center of mass of the vehicle chassis; Acquire a second angular velocity and a second acceleration of the top of the vehicle; The operating condition of the vehicle is predicted based on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration.
3. The vehicle operating condition prediction method according to claim 2, characterized in that: The predicting of the operating condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration includes: Determining a first roll angle of the center of mass of the vehicle chassis using a complementary filtering algorithm based on the first angular velocity and the first acceleration; determining a second roll angle of the top of the vehicle using a complementary filtering algorithm based on the second angular velocity and the second acceleration; determining a relative roll angle of the vehicle according to the first roll angle and the second roll angle; The operating condition of the vehicle is predicted according to the first roll angle, the second roll angle, and the relative roll angle.
4. The vehicle operating condition prediction method according to claim 3, characterized in that: Determining a first roll angle of the center of mass of the vehicle chassis using a complementary filtering algorithm according to the first angular velocity and the first acceleration includes: determining a first tilt angle according to the first angular velocity; determining a second tilt angle according to the first acceleration; Complementary filtering is performed on the first tilt angle and the second tilt angle to determine a first roll angle of the center of mass of the vehicle chassis.
5. The vehicle operating condition prediction method according to claim 3, characterized in that: Determining the second roll angle of the top of the vehicle using a complementary filtering algorithm based on the second angular velocity and the second acceleration includes: determining a third tilt angle according to the second angular velocity; determining a fourth tilt angle according to the second acceleration; Complementary filtering is performed on the third tilt angle and the fourth tilt angle to determine a second roll angle of the vehicle top.
6. The vehicle operating condition prediction method according to claim 3, characterized in that: The predicting of the operating condition of the vehicle according to the first roll angle, the second roll angle, and the relative roll angle includes: When the second roll angle is less than a first angle threshold, determining that the vehicle is in a normal operating condition; When the second roll angle is greater than the first angle threshold, determining that the vehicle has a rolling tendency; In a case where the vehicle has a rolling tendency, a working condition of the vehicle is predicted based on the first roll angle and the relative roll angle.
7. The vehicle operating condition prediction method according to claim 6, characterized in that: The predicting of the operating condition of the vehicle according to the first roll angle and the relative roll angle includes: When the relative roll angle is greater than a second angle threshold and the first roll angle is less than a third angle threshold, determining that the vehicle is in a sideslip condition; When the relative roll angle is greater than the second angle threshold and the first roll angle is greater than the third angle threshold, determining that the vehicle is in a rolling condition; The vehicle operating condition prediction method further includes: When it is determined that the vehicle is in a rollover condition, an ignition signal is output to control the tightening of the seat belt and / or the deployment of the airbag of the vehicle.
8. The vehicle operating condition prediction method according to claim 2, characterized in that: Before predicting the operating condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration, the vehicle operating condition prediction method further includes: performing low-pass filtering on the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration; performing data verification on the filtered first angular velocity, the first acceleration, the second angular velocity, and the second acceleration; If the data verification is qualified, predicting the operating condition of the vehicle according to the first angular velocity, the first acceleration, the second angular velocity, and the second acceleration; If the data verification fails, return to the step of obtaining the first angular velocity and the first acceleration of the vehicle chassis center of mass.
9. A vehicle operating condition prediction device, characterized in that: The vehicle operating condition prediction device comprises: A first acquisition module, configured to acquire a first angular velocity and a first acceleration of the center of mass of the vehicle chassis; A second acquisition module, configured to acquire a second angular velocity and a second acceleration of the top of the vehicle; A prediction module is used to predict the operating condition of the vehicle based on the first angular velocity, the first acceleration, the second angular velocity and the second acceleration.
10. A vehicle, characterized in that: The vehicle includes one or more processors and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the vehicle operating condition prediction method according to any one of claims 2 to 8 is implemented.
11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle operating condition prediction method according to any one of claims 2 to 8 is implemented.