Vehicle yaw rate determination method, device, electronic device, and storage medium

Abstract

This application discloses a method, device, electronic device, and storage medium for determining vehicle yaw rate. The method is applied to commercial vehicles and includes: obtaining a first yaw rate of the vehicle from an IMU (Inertial Measurement Unit); acquiring vehicle parameters through sampling and calculating a second yaw rate of the vehicle; determining a target sampling parameter based on vehicle dynamics based on the first and second yaw rates; establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle based on the target sampling parameter based on vehicle dynamics; and obtaining the vehicle's current yaw rate based on the mapping relationship. This application enables online identification and calculation of yaw rate, making it suitable for commercial vehicle scenarios where loads are subject to change.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device, electronic device, and storage medium for determining the yaw rate of a vehicle. Background Art

[0002] Autonomous driving control of autonomous vehicles usually requires the establishment of a vehicle dynamics model, and the results obtained after vehicle dynamics model analysis will be used for autonomous driving control.

[0003] However, existing vehicle dynamics models are not well suited for commercial vehicles with variable loads, such as trucks and heavy-duty semi-trailers. Furthermore, they are unable to identify the yaw rate of commercial vehicles based on vehicle dynamics, and there is no effective way to obtain the vehicle's yaw rate. Summary of the Invention

[0004] Embodiments of the present application provide a method, device, electronic device, and storage medium for determining the yaw rate of a vehicle, so as to adaptively obtain the yaw rate of the vehicle.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for determining a vehicle yaw rate, which is applied to a commercial vehicle, wherein the method includes:

[0007] Get the first yaw angular velocity of the vehicle in the IMU inertial measurement unit;

[0008] Obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle;

[0009] determining a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate;

[0010] Establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics;

[0011] According to the mapping relationship, the current yaw angular velocity of the vehicle is obtained.

[0012] In some embodiments, obtaining the first yaw angular velocity of the vehicle in the IMU inertial measurement unit includes:

[0013] When the vehicle is traveling on a target road, recording the front wheel turning angle and longitudinal speed of the vehicle, wherein the target road includes a road with a cross slope that meets the requirements;

[0014] Furthermore, a first yaw angular velocity of the vehicle in an IMU inertial measurement unit is correspondingly obtained in different speed intervals.

[0015] In some embodiments, the target sampling parameters based on vehicle dynamics include equivalent wheelbase,

[0016] The acquiring of vehicle parameters by sampling and calculating the second yaw rate of the vehicle includes:

[0017] Obtaining the equivalent wheelbase, the front wheel turning angle, and the longitudinal vehicle speed based on the first sampling interval, and calculating a second yaw rate of the vehicle according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial dynamic equivalent coefficient;

[0018] Determining a target sampling parameter based on vehicle dynamics according to the first yaw angular velocity and the second yaw angular velocity includes:

[0019] The first yaw angular velocity and the second yaw angular velocity are compared, and the target sampling equivalent wheelbase is obtained when the difference between the two is minimum.

[0020] In some embodiments, the target sampling parameters based on vehicle dynamics include dynamic equivalent coefficients,

[0021] The acquiring of vehicle parameters by sampling and calculating the second yaw rate of the vehicle includes:

[0022] obtaining the dynamic equivalent coefficient, the front wheel steering angle, and the longitudinal vehicle speed based on a second sampling interval, and calculating a second yaw rate of the vehicle according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial equivalent wheelbase;

[0023] Determining a target sampling parameter based on vehicle dynamics according to the first yaw angular velocity and the second yaw angular velocity includes:

[0024] The first yaw angular velocity and the second yaw angular velocity are compared, and the target sampling dynamic equivalent coefficient is obtained when the difference between the two is minimum.

[0025] In some embodiments, determining the target sampling parameter based on vehicle dynamics further includes:

[0026] An optimization solution is performed in the wheelbase interval [target sampling equivalent wheelbase - first sampling interval, target sampling equivalent wheelbase + first sampling interval], and after multiple calculations, the optimal target sampling equivalent wheelbase is obtained when the difference between the two is the smallest;

[0027] and / or,

[0028] The optimization solution is performed in the kinetic equivalent coefficient interval [target sampling kinetic equivalent coefficient-second sampling interval, target sampling kinetic equivalent coefficient+second sampling interval]. After multiple calculations, the optimal target sampling kinetic equivalent coefficient is obtained when the difference between the two is the smallest.

[0029] In some embodiments, the step of establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameter based on vehicle dynamics further includes:

[0030] determining a front wheel steering angle applied to the vehicle based on a mapping relationship between the yaw angular velocity of the vehicle and the front wheel steering angle when the current yaw angular velocity of the vehicle is known;

[0031] When the current front wheel turning angle of the vehicle is known, the corresponding yaw angular velocity of the vehicle is determined according to a mapping relationship between the yaw angular velocity of the vehicle and the front wheel turning angle.

[0032] In some embodiments, after establishing the mapping relationship between the vehicle's yaw rate and the front wheel angle, the method further includes:

[0033] When the control system of the vehicle is powered on, adaptively configuring the target sampling parameters based on vehicle dynamics;

[0034] According to the configuration results, the step size iteration of the preset interval is performed during the vehicle operation.

[0035] In a second aspect, an embodiment of the present application further provides a vehicle yaw rate determination device, which is applied to a commercial vehicle, wherein the device includes:

[0036] An acquisition module is used to obtain a first yaw angular velocity of the vehicle in an IMU inertial measurement unit;

[0037] A sampling module, used to obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle;

[0038] a determination module, configured to determine a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate;

[0039] An establishing module, configured to establish a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics;

[0040] The calculation module is used to obtain the current yaw angular velocity of the vehicle according to the mapping relationship.

[0041] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, enable the processor to perform the above method.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the above method.

[0043] At least one of the above-mentioned technical solutions employed in the embodiments of the present application can achieve the following beneficial effects: first, a first yaw rate of the vehicle is acquired from the IMU inertial measurement unit (IMU), then vehicle parameters are sampled and a second yaw rate of the vehicle is calculated. Target sampling parameters based on vehicle dynamics are then determined based on the first and second yaw rates. This allows for online parameter identification using relatively little data, enabling adaptive and rapid identification of vehicle dynamic parameters.

[0044] Furthermore, a mapping relationship between the vehicle's yaw rate and the front wheel angle is established for the target sampling parameters based on vehicle dynamics. Based on this mapping relationship, the vehicle's current yaw rate is obtained. This adapts to application scenarios where vehicle dynamics parameters are subject to change, such as in the case of a semi-trailer or truck with cargo, where the load is subject to change. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0046] Figure 1 This is a flow chart of a method for determining the yaw rate of a vehicle in an embodiment of the present application;

[0047] Figure 2 This is a schematic structural diagram of a vehicle yaw rate determination device in an embodiment of the present application;

[0048] Figure 3 FIG1 is a schematic diagram of a simulation comparing the calculated yaw rate and the actual yaw rate of a vehicle according to the method for determining the yaw rate of the vehicle in an embodiment of the present application, wherein the horizontal axis represents the time value and the vertical axis represents the speed value;

[0049] Figure 4 Schematic diagram of simulation of different speed sections of the method for determining the vehicle yaw rate in an embodiment of the present application, where the horizontal axis represents the time value and the vertical axis represents the steering angle value;

[0050] Figure 5Schematic diagram of simulation of different steering wheel angles of the method for determining the vehicle yaw rate in an embodiment of the present application, where the horizontal axis represents time value and the vertical axis represents speed value;

[0051] Figure 6 This is a schematic structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0052] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] During research, the inventors discovered that in autonomous vehicle control technology, the kinematic model based on the lateral motion of the vehicle is based on a motion equation established based on the geometric relationship of the control system, without considering the forces on the wheels that affect the motion. This model can be used at low speeds, but when the vehicle speed increases, the error is larger.

[0054] Furthermore, while the dynamics model based on vehicle lateral motion analysis takes wheel forces into account, it requires experimental analysis to determine parameters such as cornering stiffness, mass, moment of inertia, and the distance from the center of mass to the front and rear axles under wheel forces. While this model can be used for passenger cars and buses whose structure and weight do not change significantly, it is still difficult to apply to trucks and heavy-duty semi-trailers. This is because changes in the load and the trailer can significantly alter the overall vehicle parameters. These numerous parameters make online identification difficult, making it difficult to guarantee model accuracy. Therefore, a computational model is needed that can be applied to both low- and high-speed vehicles, as well as vehicles with easily fluctuating loads, such as trucks and heavy-duty semi-trailers.

[0055] To address these shortcomings, the vehicle yaw rate determination method in the embodiments of this application solves the problem of inaccurate vehicle yaw rate calculation for commercial vehicles (such as heavy-duty semi-trailers and cargo trucks) with varying loads and operating conditions (including trailers). By using online identification / adaptive recognition methods, a relatively accurate vehicle yaw rate calculation result is obtained. Furthermore, an accurate mapping relationship between yaw rate and front wheel angle is established. This mapping relationship can be used for subsequent lateral control of the autonomous vehicle.

[0056] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0057] The embodiment of the present application provides a method for determining the yaw rate of a vehicle, such as Figure 1As shown, a flow chart of a method for determining the yaw rate of a vehicle in an embodiment of the present application is provided. The method includes at least the following steps S110 to S150:

[0058] Step S110: Obtain a first yaw angular velocity of the vehicle in an IMU inertial measurement unit.

[0059] IMU inertial measurement unit refers to an inertial measurement unit that is a device for measuring the three-axis attitude angle (or angular rate) and acceleration of an object. Generally, an IMU contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometer detects the acceleration signal of the object in the independent three axes of the carrier coordinate system, and the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system, measures the angular velocity and acceleration of the object in three-dimensional space, and calculates the attitude of the object. In the perception and positioning module of the autonomous driving vehicle, the yaw angular velocity can usually be calculated by the IMU inertial measurement unit. In addition, the combination of IMU inertial measurement unit and GPS / RTK can provide high-precision positioning information. The IMU inertial measurement unit is usually installed on the autonomous driving vehicle, preferably, it is installed on the commercial vehicle in the embodiment of the present application as a sensor parameter on the vehicle.

[0060] The yaw angular velocity measured by the IMU, that is, the first yaw angular velocity, is used as the true yaw angular velocity and can be used for subsequent comparison calculations.

[0061] It should be noted that the yaw angular velocity calculated in the corresponding IMU inertial measurement unit can be obtained in different speed ranges.

[0062] Step S120 , obtaining vehicle parameters through sampling, and calculating a second yaw angular velocity of the vehicle.

[0063] The current vehicle parameters are obtained through sampling, including but not limited to the front wheel steering angle δ and the longitudinal vehicle speed v. In addition to the vehicle parameters obtained through sampling, these parameters may also include the initial sampling wheelbase L and the initial dynamic equivalent coefficient k_kinetic. These parameters can be obtained directly from the vehicle configuration file or other means.

[0064] Furthermore, a second yaw rate may be obtained by calculation, namely, as a calculated yaw rate, which may be used in subsequent comparison calculations.

[0065] It should be noted that the above process does not take into account the cross slope angle on the road or can collect the vehicle parameters of the front wheel steering angle δ, the longitudinal vehicle speed v, and the yaw rate in the IMU inertial measurement unit sensor parameters on a road with a relatively small cross slope.

[0066] It will be appreciated that the above vehicle parameters may be used in different speed ranges to account for forces affecting motion, and may be applicable at low speeds or when the vehicle speed increases.

[0067] Preferably, a uniform sampling method is adopted during sampling to obtain the equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic.

[0068] Step S130 : determining a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate.

[0069] By calculating the difference between the first yaw rate actually acquired and the second yaw rate obtained through calculation, we can determine the optimal equivalent wheelbase L and the optimal value of the dynamic equivalent coefficient k_kinetic corresponding to the minimum difference, thereby determining the target sampling parameters based on vehicle dynamics. It is important to note that the "difference" is not an absolute value but a relative value, and the difference needs to be squared if necessary.

[0070] Through the above calculation steps, the vehicle dynamics parameters of the vehicle can be adaptively processed to obtain the target sampling parameters of the current vehicle based on vehicle dynamics.

[0071] like Figure 3 The figure shows the comparison between the calculated yaw rate and the actual yaw rate. It can be seen that the calculated yaw rate has a relatively high accuracy and is more consistent with the actual situation.

[0072] like Figure 4 as well as Figure 5 As shown in the figure, the yaw rate and true value calculated under different speed segments (VehSpd_KPH) and steering wheel angles (SteerAng_deg) are respectively. It can be seen that the calculated yaw rate has a relatively high accuracy and is more consistent with the actual value.

[0073] Step S140 : establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics.

[0074] By using the target sampling parameters based on vehicle dynamics in the above steps, a mapping relationship between the vehicle's yaw rate and the front wheel angle is further established, which can be used for subsequent lateral control of the autonomous vehicle. This mapping relationship can be used as a computational model for calculating the yaw rate.

[0075] Step S150: Obtain the current yaw rate of the vehicle according to the mapping relationship.

[0076] The current yaw rate of the vehicle can be obtained based on the mapping relationship. The current yaw rate calculation model of the vehicle established through the above steps can obtain the yaw rate calculation result when the relevant parameters are known.

[0077] By adopting the above-mentioned vehicle yaw rate determination method, the vehicle dynamics parameters used to calculate the current yaw rate of the vehicle can be adaptively identified, and the vehicle dynamics parameters are used as target sampling parameters for online identification and calculation, which can ensure the accuracy of the vehicle yaw rate calculation model.

[0078] By adopting the above-mentioned vehicle yaw rate determination method, the vehicle yaw rate can be adaptively calculated, thereby being able to adapt to application scenarios where vehicle dynamic parameters are volatile, including but not limited to commercial vehicles such as cargo semi-trailers and cargo trucks.

[0079] By adopting the above-mentioned method for determining the vehicle yaw velocity, the vehicle yaw velocity in the vehicle dynamics model of commercial vehicles whose loads are easily changed can be calculated, thereby solving the problem of inaccurate calculation of the vehicle yaw velocity of heavy-duty semi-trailers with different loads and different trailer operating conditions. As an important vehicle dynamics parameter in vehicle lateral control, yaw velocity specifically refers to the angular velocity of the vehicle mass rotating around the z-axis (vehicle coordinate system). Therefore, if the accurate vehicle yaw velocity of the current commercial vehicle can be obtained by calculation, it can be beneficial to achieve accurate vehicle lateral control later. In addition, the yaw velocity is calculated based on the front wheel angle and vehicle speed; the yaw rate is measured by a high-precision IMU. After simulation tests, the two are very close within the high-precision range.

[0080] Using the above vehicle yaw rate determination method, a vehicle yaw rate calculation model can be derived. This model can be used to adaptively calculate the yaw rate of a commercial vehicle with a changing load. Consequently, using this model requires fewer parameters, enabling online parameter identification and correspondingly requiring less data collection.

[0081] Unlike related technologies, which typically use vehicle dynamics models that require numerous parameters acquired through extensive experimental analysis, these parameters are difficult to obtain through online identification methods. Furthermore, kinematic models are less accurate at high speeds. This method eliminates the need for extensive data collection and allows for online identification of vehicle dynamics parameters. It can also accurately calculate the yaw rate of commercial vehicles with variable loads, regardless of whether the vehicle is currently at high or low speeds.

[0082] Unlike related technologies, which fail to consider the varying loads and trailers towed by heavy-duty semi-trailer tractors, which can significantly vary actual vehicle dynamics parameters and lead to significant deviations in yaw rate calculation, the above method establishes a mapping relationship between the vehicle's yaw rate and the front wheel angle based on target sampling parameters based on vehicle dynamics. Based on this mapping relationship, an accurate yaw rate calculation result is obtained for the vehicle's current yaw rate, and an accurate mapping relationship is established between the yaw rate and the steering wheel angle.

[0083] In one embodiment of the present application, obtaining the first yaw angular velocity of the vehicle in the IMU inertial measurement unit includes recording the front wheel turning angle and longitudinal speed of the vehicle when the vehicle is traveling on a target road, wherein the target road includes a road with a cross slope that meets the conditions; and obtaining the first yaw angular velocity of the vehicle in the IMU inertial measurement unit in different speed intervals.

[0084] When the vehicle is driving on a road with a small cross slope, the front wheel angle δ, longitudinal speed v, and yaw rate measured by the IMU are recorded. The data covers high-speed and low-speed ranges, for example, it can at least include the vehicle speed range of (20kph-70kph).

[0085] In one embodiment of the present application, the target sampling parameter based on vehicle dynamics includes an equivalent wheelbase. The acquiring of the vehicle parameter by sampling and calculating the second yaw rate of the vehicle includes: acquiring the equivalent wheelbase based on a first sampling interval, calculating the second yaw rate of the vehicle according to the acquired front wheel turning angle and longitudinal vehicle speed, and according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial dynamic equivalent coefficient; and determining the target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate includes: comparing the first yaw rate with the second yaw rate, and obtaining the target sampling equivalent wheelbase when the difference between the first yaw rate and the second yaw rate is minimized.

[0086] The online identification / adaptive identification method of the equivalent wheelbase L is adopted, specifically:

[0087] First, the wheelbase L is sampled evenly with a sampling interval of gap_L.

[0088] For example, the sampling interval is [4.0, 5.0, 6.0, 7.0, 8.0, 9.0] m.

[0089] In addition, the specific uniform sampling range and sampling interval are determined by performing several tests based on the characteristics of the specific vehicle. The sampling wheelbase L, the initial dynamic equivalent coefficient k_kinetic_init, the collected front wheel angle δ, and the longitudinal vehicle speed v are substituted into the relevant calculation formula. The calculated yaw rate is compared with the yaw rate measured by the IMU to determine the sampling L_temp with the minimum cost.

[0090] For example, Where n is the number of data points of the recorded front wheel steering angle δ.

[0091] In one embodiment of the present application, the target sampling parameters based on vehicle dynamics include a dynamic equivalent coefficient. The acquiring of vehicle parameters by sampling and calculation of the second yaw rate of the vehicle include: acquiring the dynamic equivalent coefficient based on a second sampling interval, calculating the second yaw rate of the vehicle according to the acquired front wheel turning angle and longitudinal vehicle speed, and according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial equivalent wheelbase; and determining the target sampling parameters based on vehicle dynamics according to the first yaw rate and the second yaw rate include: comparing the first yaw rate with the second yaw rate, and obtaining the target sampling dynamic equivalent coefficient when the difference between the first yaw rate and the second yaw rate is minimized.

[0092] The online identification / adaptive identification of the dynamic equivalent coefficient k_kinetic is adopted, specifically:

[0093] First, uniformly sample the kinetic equivalent coefficient k_kinetic and the sampling interval gap_k.

[0094] For example, the sampling interval is [0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018].

[0095] In addition, the specific uniform sampling range and sampling interval are determined by performing several tests based on the characteristics of the specific vehicle. The wheelbase L_temp, sampling dynamics equivalent coefficient k_kinetic, collected front wheel angle δ, and longitudinal vehicle speed v are substituted into the relevant calculation formula. The calculated yaw rate is compared with the yaw rate measured by the IMU to find the sampling k_temp with the minimum cost.

[0096] For example, n is the number of data points of the recorded front wheel steering angle δ.

[0097] In one embodiment of the present application, the determination of the target sampling parameters based on vehicle dynamics also includes: performing an optimization solution in the wheelbase interval [target sampling equivalent wheelbase - first sampling interval, target sampling equivalent wheelbase + first sampling interval], and obtaining the optimal target sampling equivalent wheelbase when the difference between the two is minimized after multiple calculations; and / or, performing an optimization solution in the dynamic equivalent coefficient interval [target sampling dynamic equivalent coefficient - second sampling interval, target sampling dynamic equivalent coefficient + second sampling interval], and obtaining the optimal target sampling dynamic equivalent coefficient when the difference between the two is minimized after multiple calculations.

[0098] Based on the above calculations, the golden section optimization method is applied to the wheelbase range [L_temp-gap_L, L_temp+gap_L] to obtain the L_temp with the minimum cost. The golden section optimization method is applied to the dynamic equivalent coefficient range [k_temp-gap_k, k_temp+gap_k] to obtain the k_temp with the minimum cost.

[0099] To obtain more accurate results, the above process can be repeated to obtain the optimal parameters L_optimal equivalent wheelbase and k_optimal dynamic equivalent coefficient suitable for the vehicle.

[0100] In one embodiment of the present application, a mapping relationship between the vehicle's yaw rate and the front wheel angle is established based on the target sampling parameters based on vehicle dynamics, and then further includes: when the current yaw rate of the vehicle is known, determining the front wheel angle applied to the vehicle based on the mapping relationship between the yaw rate of the vehicle and the front wheel angle; when the current front wheel angle of the vehicle is known, determining the corresponding yaw rate of the vehicle based on the mapping relationship between the yaw rate of the vehicle and the front wheel angle.

[0101] According to the accurate mapping relationship between the yaw rate and the front wheel angle, the corresponding calculation can be performed when the corresponding parameters are known. If the front wheel steering angle δ, longitudinal vehicle speed v and yaw rate measured by IMU are known Only by obtaining the accurate equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic can the accurate front wheel turning angle δ and vehicle yaw rate be obtained. On the contrary, based on the optimal parameters L_optimal equivalent wheelbase and k_optimal dynamic equivalent coefficient adapted to the vehicle, according to the above calculation The accurate mapping relationship between the yaw rate and the front wheel angle is obtained.

[0102] In one embodiment of the present application, after establishing the mapping relationship between the vehicle's yaw angular velocity and the front wheel angle, it also includes: when the vehicle's control system is powered on, adaptively configuring the target sampling parameters based on vehicle dynamics; based on the configuration results, performing step iterations of a preset interval during the operation of the vehicle.

[0103] When the vehicle system is powered on, the results of the first parameter identification, L_optimal and k_optimal, are used directly. Subsequently, during the vehicle operation, small step size iterations are performed:

[0104] L_optimal=L_optimal_last+step_L*(L_optimal-L_optimal_last);

[0105] k_optimal=k_optimal_last+step_k*(k_optimal-k_optimal_last),

[0106] Among them, step_L and step_k are the iteration steps in the interval [0,1.0].

[0107] The embodiment of the present application further provides a vehicle yaw rate determination device 200, such as Figure 2 , a schematic diagram of the structure of a vehicle yaw rate determination device according to an embodiment of the present application is provided. The vehicle yaw rate determination device 200 includes at least: an acquisition module 210, a sampling module 220, a determination module 230, an establishment module 240, and a calculation module 250, wherein:

[0108] In one embodiment of the present application, the acquisition module 210 is specifically configured to acquire a first yaw angular velocity of the vehicle in an IMU inertial measurement unit.

[0109] The yaw angular velocity measured by the IMU, that is, the first yaw angular velocity, is used as the true yaw angular velocity and can be used for subsequent comparison calculations.

[0110] It should be noted that the yaw angular velocity calculated in the corresponding IMU inertial measurement unit can be obtained in different speed ranges.

[0111] In one embodiment of the present application, the sampling module 220 is specifically configured to obtain vehicle parameters through sampling and calculate a second yaw angular velocity of the vehicle.

[0112] The current vehicle parameters are obtained through sampling, including but not limited to the front wheel steering angle δ and the longitudinal vehicle speed v. In addition to the vehicle parameters obtained through sampling, these parameters may also include the initial sampling wheelbase L and the initial dynamic equivalent coefficient k_kinetic. These parameters can be obtained directly from the vehicle configuration file or other means.

[0113] Furthermore, a second yaw rate may be obtained by calculation, namely, as a calculated yaw rate, which may be used in subsequent comparison calculations.

[0114] It should be noted that the above process does not take into account the cross slope angle on the road or can collect the vehicle parameters of the front wheel steering angle δ, the longitudinal vehicle speed v, and the yaw rate in the IMU inertial measurement unit sensor parameters on a road with a relatively small cross slope.

[0115] It will be appreciated that the above vehicle parameters may be used in different speed ranges to account for forces affecting motion, and may be applicable at low speeds or when the vehicle speed increases.

[0116] Preferably, a uniform sampling method is adopted during sampling to obtain the equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic.

[0117] In one embodiment of the present application, the determination module 230 is specifically configured to determine a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate.

[0118] For the first yaw rate actually collected and the second yaw rate obtained through calculation, the difference between the two can be calculated to solve the optimal value of the equivalent wheelbase L and the optimal value of the dynamic equivalent coefficient k_kinetic corresponding to the minimum difference, thereby determining the target sampling parameters based on vehicle dynamics.

[0119] Through the above calculation, the vehicle dynamics parameters of the vehicle can be adaptively processed to obtain the target sampling parameters of the current vehicle based on vehicle dynamics.

[0120] In one embodiment of the present application, the establishing module 240 is specifically configured to establish a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics.

[0121] By using the target sampling parameters based on vehicle dynamics in the above steps, a mapping relationship between the vehicle's yaw rate and the front wheel angle is further established, which can be used for subsequent lateral control of the autonomous vehicle. This mapping relationship can be used as a computational model for calculating the yaw rate.

[0122] In one embodiment of the present application, the calculation module 250 is specifically configured to obtain the current yaw rate of the vehicle according to the mapping relationship.

[0123] The current yaw rate of the vehicle can be obtained based on the mapping relationship. The current yaw rate calculation model of the vehicle established through the above steps can obtain the yaw rate calculation result when the relevant parameters are known.

[0124] It can be understood that the above-mentioned vehicle yaw rate determination device can implement the various steps of the vehicle yaw rate determination method provided in the aforementioned embodiment. The relevant explanations about the vehicle yaw rate determination method are applicable to the vehicle yaw rate determination device and will not be repeated here.

[0125] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 6 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0126] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0127] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0128] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming a vehicle yaw rate determination device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0129] Get the first yaw angular velocity of the vehicle in the IMU inertial measurement unit;

[0130] Obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle;

[0131] determining a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate;

[0132] Establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics;

[0133] According to the mapping relationship, the current yaw angular velocity of the vehicle is obtained.

[0134] The above application Figure 1 The method performed by the vehicle yaw rate determination device disclosed in the illustrated embodiment can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0135] The electronic device may also perform Figure 1 The method is executed by the vehicle yaw angular velocity determination device in the vehicle yaw angular velocity determination device. Figure 1The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0136] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, can enable the electronic device to execute Figure 1 The method performed by the vehicle yaw rate determination device in the illustrated embodiment is specifically used to perform:

[0137] Get the first yaw angular velocity of the vehicle in the IMU inertial measurement unit;

[0138] Obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle;

[0139] determining a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate;

[0140] Establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics;

[0141] According to the mapping relationship, the current yaw angular velocity of the vehicle is obtained.

[0142] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0146] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0147] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0148] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0149] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for determining vehicle yaw rate, applied to commercial vehicles, wherein: The method comprises: Get the first yaw angular velocity of the vehicle in the IMU inertial measurement unit; Obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle; determining a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate; Determining the target sampling parameter based on vehicle dynamics based on the first yaw rate and the second yaw rate includes: calculating a difference between the first yaw rate actually collected and the second yaw rate obtained through calculation, and solving for an optimal value of an equivalent wheelbase and an optimal value of a dynamic equivalent coefficient corresponding to a minimum difference, thereby determining the target sampling parameter based on vehicle dynamics; Establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics; According to the mapping relationship, the current yaw angular velocity of the vehicle is obtained.

2. The method according to claim 1, wherein: The obtaining of the first yaw angular velocity of the vehicle in the IMU inertial measurement unit includes: When the vehicle is traveling on a target road, recording the front wheel turning angle and longitudinal speed of the vehicle, wherein the target road includes a road with a cross slope that meets the requirements; Furthermore, a first yaw angular velocity of the vehicle in an IMU inertial measurement unit is correspondingly obtained in different speed intervals.

3. The method according to claim 2, wherein: The target sampling parameters based on vehicle dynamics include equivalent wheelbase, The acquiring of vehicle parameters by sampling and calculating the second yaw rate of the vehicle includes: Obtaining the equivalent wheelbase, the front wheel turning angle, and the longitudinal vehicle speed based on the first sampling interval, and calculating a second yaw rate of the vehicle according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial dynamic equivalent coefficient; Determining a target sampling parameter based on vehicle dynamics according to the first yaw angular velocity and the second yaw angular velocity includes: The first yaw angular velocity and the second yaw angular velocity are compared, and the target sampling equivalent wheelbase is obtained when the difference between the two is minimum.

4. The method according to claim 2, wherein: The target sampling parameters based on vehicle dynamics include dynamic equivalent coefficients, The acquiring of vehicle parameters by sampling and calculating the second yaw rate of the vehicle includes: obtaining the dynamic equivalent coefficient, the front wheel steering angle, and the longitudinal vehicle speed based on a second sampling interval, and calculating a second yaw rate of the vehicle according to a preset vehicle dynamics model, wherein the preset vehicle dynamics model includes an initial equivalent wheelbase; Determining a target sampling parameter based on vehicle dynamics according to the first yaw angular velocity and the second yaw angular velocity includes: The first yaw angular velocity and the second yaw angular velocity are compared, and the target sampling dynamic equivalent coefficient is obtained when the difference between the two is minimum.

5. The method according to claim 3 or 4, wherein: The determining of the target sampling parameters based on vehicle dynamics further includes: Perform optimization in the wheelbase interval [target sampling equivalent wheelbase - first sampling interval, target sampling equivalent wheelbase + first sampling interval], and after multiple calculations, obtain the optimal target sampling equivalent wheelbase with the smallest difference between the two. and / or, An optimization solution is performed in the kinetic equivalent coefficient interval [target sampling kinetic equivalent coefficient - second sampling interval, target sampling kinetic equivalent coefficient + second sampling interval]. After multiple calculations, the optimal target sampling kinetic equivalent coefficient is obtained when the difference between the two is minimized.

6. The method of claim 1, wherein: The method further includes establishing a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameter based on vehicle dynamics, and then further including: determining a front wheel steering angle applied to the vehicle based on a mapping relationship between the yaw angular velocity of the vehicle and the front wheel steering angle when the current yaw angular velocity of the vehicle is known; When the current front wheel turning angle of the vehicle is known, the corresponding yaw angular velocity of the vehicle is determined according to a mapping relationship between the yaw angular velocity of the vehicle and the front wheel turning angle.

7. The method of claim 6, wherein: After establishing the mapping relationship between the vehicle's yaw rate and the front wheel angle, the method further includes: When the control system of the vehicle is powered on, adaptively configuring the target sampling parameters based on vehicle dynamics; According to the configuration results, the step size iteration of the preset interval is performed during the vehicle operation.

8. A vehicle yaw rate determination device, applied to commercial vehicles, wherein: The device comprises: An acquisition module is used to obtain a first yaw angular velocity of the vehicle in an IMU inertial measurement unit; A sampling module, used to obtain vehicle parameters through sampling and calculate the second yaw angular velocity of the vehicle; a determination module, configured to determine a target sampling parameter based on vehicle dynamics according to the first yaw rate and the second yaw rate; Determining the target sampling parameter based on vehicle dynamics based on the first yaw rate and the second yaw rate includes: calculating a difference between the first yaw rate actually collected and the second yaw rate obtained through calculation, and solving for an optimal value of an equivalent wheelbase and an optimal value of a dynamic equivalent coefficient corresponding to a minimum difference, thereby determining the target sampling parameter based on vehicle dynamics; An establishing module, configured to establish a mapping relationship between the vehicle's yaw rate and the front wheel angle according to the target sampling parameters based on vehicle dynamics; The calculation module is used to obtain the current yaw angular velocity of the vehicle according to the mapping relationship.

9. An electronic device comprising: processor; as well as A memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the method according to any one of claims 1 to 7.

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