Vehicle lateral control method, device, electronic device, and storage medium

Abstract

This application discloses a vehicle lateral control method, device, electronic device, and storage medium, which are applied to commercial vehicles. The method includes determining the desired yaw rate of the vehicle; calculating the vehicle's lateral position change rate based on the vehicle's current lateral position deviation; determining the reference-line-based yaw rate based on a reference line and vehicle speed; and calculating the vehicle's front wheel steering angle parameters required to eliminate the vehicle's expected lateral position deviation within a preset adjustment time based on the lateral distance between the vehicle and the reference line, the vehicle's desired yaw rate, the vehicle's lateral position change rate, and the reference-line-based yaw rate, so as to perform lateral control of the vehicle. Through this application, it is possible to better achieve online vehicle yaw rate identification and lateral control of vehicles whose load is prone to change.

Description

Technical Field

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

[0002] The lateral control of autonomous vehicles mainly includes feedforward and PID (Proportional Integral Derivative) lateral control algorithms based on preview, MPC (Model Predictive Control) lateral control algorithms based on kinematics or dynamics, and LQR (linear quadratic regulator) lateral control algorithms based on kinematics or dynamics.

[0003] Related technologies, such as kinematics-based lateral control algorithms, are not suitable for high-speed lateral control. Dynamics-based lateral control algorithms have too many model parameters, requiring only pre-measurement in the laboratory. They are also unsuitable for scenarios where the dynamics model changes due to differences in vehicle weight, such as when a truck or heavy truck switches to a trailer. Summary of the Invention

[0004] Embodiments of the present application provide a vehicle lateral control method, device, electronic device, and storage medium to provide stable vehicle lateral control in scenarios where the vehicle load is prone to change.

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

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

[0007] determining a desired yaw rate of the vehicle;

[0008] Calculating a lateral position change rate of the vehicle based on the current lateral position deviation of the vehicle;

[0009] determining a yaw rate based on the reference line according to the reference line and the vehicle speed;

[0010] Based on the lateral distance between the vehicle and the reference line within a preset adjustment time, and in accordance with the expected yaw rate of the vehicle, the rate of change of the lateral position of the vehicle, and the yaw rate based on the reference line, a vehicle front wheel steering angle parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time is calculated to perform lateral control of the vehicle, wherein the expected lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on the current vehicle driving state.

[0011] In some embodiments, the method further comprises:

[0012] pre-calibrating an expected duration of the lateral position change rate;

[0013] and / or,

[0014] A preset adjustment time is pre-calibrated, where the preset adjustment time includes the time from time t0 to time t_adjust of the vehicle, where t0 represents the initial time of the vehicle and t_adjust represents the adjustment time of the vehicle.

[0015] In some embodiments, calculating the vehicle's lateral position change rate based on the vehicle's current lateral position deviation includes:

[0016] filtering the current lateral position deviation of the vehicle through a low-pass filter, and taking the derivative of the filtering result;

[0017] The derivative result is filtered through a low-pass filter to obtain the lateral position change rate of the vehicle.

[0018] In some embodiments, the lateral distance between the vehicle and the reference line within the preset adjustment time includes:

[0019] Obtain the vehicle's current lateral position deviation, current lateral position change rate, reference line curvature, current vehicle speed, and current cross slope angle;

[0020] The lateral distance between the vehicle and the reference line within the preset adjustment time is estimated based on the current lateral position deviation of the vehicle, the lateral position change rate of the vehicle within the preset adjustment time, the yaw rate based on the reference line within the preset adjustment time, and the expected yaw rate of the vehicle within the preset adjustment time.

[0021] In some embodiments, determining the desired yaw rate of the vehicle includes:

[0022] When the load of the commercial vehicle changes, the expected yaw rate of the vehicle is adaptively adjusted.

[0023] In some embodiments, the method further comprises:

[0024] When the load of the commercial vehicle changes, the yaw rate model is corrected in real time based on the collected vehicle parameter data and any one or more vehicle dynamics model parameters including the equivalent wheelbase, the dynamic equivalent coefficient, and the slope compensation coefficient to adaptively adjust the desired yaw rate of the vehicle.

[0025] In some embodiments, the method further comprises:

[0026] When the commercial vehicle travels on a road with a transverse slope, a front wheel steering angle control command of the corresponding vehicle is calculated based on a modified yaw rate model with slope compensation.

[0027] In a second aspect, an embodiment of the present application further provides a vehicle lateral control device, which is applied to a commercial vehicle, wherein the device includes:

[0028] A first determination module is configured to determine a desired yaw rate of the vehicle;

[0029] a first calculation module, configured to calculate a lateral position change rate of the vehicle according to a current lateral position deviation of the vehicle;

[0030] a second determining module, configured to determine a yaw rate based on the reference line according to the reference line and the vehicle speed;

[0031] A second calculation module is configured to calculate, based on a lateral distance between the vehicle and the reference line within a preset adjustment time, a desired yaw rate of the vehicle, a lateral position change rate of the vehicle, and the yaw rate based on the reference line, a vehicle front wheel steering angle parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time, so as to perform lateral control of the vehicle, wherein the expected lateral position deviation includes a lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on the current driving state of the vehicle.

[0032] 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.

[0033] 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.

[0034] At least one of the above-mentioned technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: In a scenario where the load of a commercial vehicle changes, the desired yaw rate of the vehicle is determined, and based on the vehicle's current lateral position deviation, the vehicle's lateral position change rate is calculated. A yaw rate based on a reference line and the vehicle speed is also determined. Based on the lateral distance between the vehicle and the reference line within a preset adjustment time, the desired yaw rate, the vehicle's lateral position change rate, and the yaw rate based on the reference line are used to calculate the vehicle's front wheel steering angle parameter required to eliminate the vehicle's predicted lateral position deviation within the preset adjustment time, thereby performing lateral control of the vehicle.

[0035] Furthermore, the estimated lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes the expected time to eliminate the lateral position deviation based on the current vehicle driving state. Based on the estimated lateral position deviation and the preset adjustment time, the vehicle's front wheel steering angle parameters required to eliminate the estimated lateral position deviation within the preset adjustment time are determined. In other words, utilizing a relatively accurate yaw rate calculation model that considers cross slope reduces the need for control calibration parameter adjustments, provides clear parameter meaning, and provides a stable vehicle control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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:

[0037] Figure 1 Schematic diagram of the principle of the vehicle lateral control method in an embodiment of the present application;

[0038] Figure 2 This is a flow chart of a vehicle lateral control method according to an embodiment of the present application;

[0039] Figure 3 This is a schematic structural diagram of a vehicle lateral control device in an embodiment of the present application;

[0040] Figure 4 This is a structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0041] 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.

[0042] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, it is a schematic diagram of the principle of the vehicle lateral control method, in which:

[0044] d_0 is the current lateral position deviation of the vehicle.

[0045] d_preview is the time between the vehicle and the reference line after t_adjust adjustment ( Figure 1 In order to eliminate the expected lateral position deviation between the two images (the dotted line in the figure), it is expected that d_preview = 0 during calculation.

[0046] c_desired, is the actual trajectory line ( Figure 1 The curvature of the solid line in .

[0047] c_ref is the curvature of the theoretical reference line.

[0048] Additionally, two calibration values ​​are included:

[0049] t_preview is the expected duration of the lateral position change rate, which is a calibrated value.

[0050] t_adjust is the time it takes to adjust the lateral position deviation to zero based on the current state. This is also a calibration value. Generally, a larger value results in poorer error control but more stable control. A smaller value results in better error control but more frequent control.

[0051] Please continue to refer to Figure 1 Based on the current lateral position deviation d_0, lateral position change rate d_rate, reference line curvature c_ref, current vehicle speed v, and slope angle θ, the estimated lateral distance between the vehicle and the reference line at time t_adjust can be obtained.

[0052]

[0053] The embodiment of the present application provides a vehicle lateral control method, such as Figure 2As shown, a flow chart of a vehicle lateral control method according to an embodiment of the present application is provided, wherein the method comprises at least the following steps S210 to S240:

[0054] Step S210: determining the desired yaw rate of the vehicle.

[0055] Based on the calculation formula in related technology 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. The corresponding relationship between them is established, and the vehicle's yaw angular velocity calculation model is established through this step.

[0056] The slope compensation coefficient calibrated by this method can obtain a more accurate model of the vehicle dynamics yaw rate on the slope, and the yaw rate can be obtained. The relationship between the front wheel turning angle δ, longitudinal vehicle speed v, and slope angle θ k_roll represents the slope compensation coefficient, θ represents the slope angle, and [θ, k_roll] has a preset lookup table relationship. This step establishes a yaw rate calculation model for a vehicle with different slope compensations added.

[0057] For example, the desired yaw rate The equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic of the above vehicle dynamics yaw rate model parameters can be adaptively identified, and the slope compensation coefficient k_roll is preset.

[0058] Step S220 , calculating the lateral position change rate of the vehicle according to the current lateral position deviation of the vehicle.

[0059] The derivative of the current lateral position deviation d_0 with respect to time represents the rate of change of the lateral position, that is,

[0060] Step S230 : determining a yaw rate based on the reference line according to the reference line and the vehicle speed.

[0061] Calculate the yaw rate of the reference line: Wherein the longitudinal vehicle speed v, c_ref, is the theoretical reference line curvature.

[0062] Step S240, based on the lateral distance between the vehicle and the reference line within the preset adjustment time, according to the expected yaw angular velocity of the vehicle, the lateral position change rate of the vehicle and the yaw angular velocity based on the reference line, calculate the vehicle front wheel angle parameters required to eliminate the expected lateral position deviation of the vehicle within the preset adjustment time, so as to control the vehicle laterally, wherein the expected lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes the expected time to eliminate the lateral position deviation based on the current driving state of the vehicle.

[0063] Based on the lateral distance between the vehicle and the reference line within the preset adjustment time:

[0064]

[0065] According to the desired yaw rate of the vehicle:

[0066] According to the rate of change of the lateral position of the vehicle: d_rate;

[0067] According to the yaw rate based on the reference line:

[0068] The vehicle front wheel steering angle parameters required to eliminate the vehicle's expected lateral position deviation within a preset adjustment time can be calculated:

[0069]

[0070] in,

[0071]

[0072] In summary, the vehicle is laterally controlled based on the front wheel steering parameters, and the yaw rate model is modified in real time based on the collected data. This can maintain consistency and adaptability of lateral control for commercial vehicles with variable loads, such as freight trucks.

[0073] The above method comprehensively considers scenarios where the vehicle load is prone to change and the situation where the cross slope of the road on which the vehicle is traveling affects the vehicle's yaw rate. By adopting a high-precision vehicle yaw rate model with an added slope compensation system, the required front wheel steering angle of the vehicle can be accurately determined when lateral errors occur, thereby achieving lateral control of the vehicle.

[0074] The above method is also applicable to the lateral control of the vehicle at high speed. Since the vehicle yaw rate model is optimized for the equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic corresponding to different speeds, it can be applied to the scenario of the vehicle traveling at high speed.

[0075] The above method is also applicable to the lateral control of a vehicle when it is driving on a slope. Since the vehicle yaw rate model adds the optimization of the slope compensation coefficient k_roll, it can be applied to scenarios where the vehicle has a large slope change.

[0076] Unlike the PID lateral control algorithm used in related technologies, this method requires fewer calibration parameters, primarily t_preview and t_adjust. It also allows for real-time correction of the yaw rate model based on collected data, maintaining consistent and adaptable lateral control for freight trucks with variable loads.

[0077] Different from the MPC lateral control algorithm or the LQR lateral control algorithm in related technologies, the above method can adapt to freight trucks with variable loads, automatically identify the vehicle yaw rate model, and maintain the consistency and adaptability of lateral control.

[0078] Different from the related art which lacks control instructions for vehicles with different loads, the vehicle lateral control method of the present application increases slope compensation and can obtain a high-precision yaw angular velocity model to calculate the appropriate turning angle control instructions.

[0079] In one embodiment of the present application, the method further includes: pre-calibrating the expected duration of the lateral position change rate; and / or pre-calibrating a preset adjustment time, the preset adjustment time including the time from the vehicle at time t0 to time t_adjust, t0 representing the initial time of the vehicle, and t_adjust representing the adjustment time of the vehicle.

[0080] The predicted duration t_preview of the lateral position change rate is pre-calibrated to obtain the predicted duration of the lateral position change rate as a calibration value.

[0081] Pre-calibrate the preset adjustment time t_adjust. This is the time it takes to adjust the lateral position deviation to zero based on the current state. A larger value results in poorer error control but more stable control. A smaller value results in better error control but more frequent control.

[0082] In one embodiment of the present application, the calculation of the vehicle's lateral position change rate based on the vehicle's current lateral position deviation includes: filtering the vehicle's current lateral position deviation through a low-pass filter and deriving the filtering result; and filtering the derivative result through a low-pass filter to obtain the vehicle's lateral position change rate.

[0083] In a specific implementation, when calculating the vehicle's lateral position change rate d_rate, the current lateral position deviation d_0 may be filtered using a Butterworth low-pass filter to obtain d_0_filter=butterworthFilter(d_0);

[0084] Furthermore, d_0_filter is derived After filtering d_rate_temp through a Butterworth low-pass filter, the vehicle's lateral position change rate is finally obtained.

[0085] d_rate=butterwosthFilter(d_rate_temp).

[0086] In one embodiment of the present application, the lateral distance between the vehicle and the reference line within the preset adjustment time includes: obtaining the vehicle's current lateral position deviation, the current lateral position change rate, the reference line curvature, the current vehicle speed, and the current cross slope angle; and estimating the lateral distance between the vehicle and the reference line within the preset adjustment time based on the vehicle's current lateral position deviation, the vehicle's lateral position change rate within the preset adjustment time, the yaw angular velocity based on the reference line within the preset adjustment time, and the expected yaw angular velocity of the vehicle within the preset adjustment time.

[0087] In specific implementation, based on the current lateral position deviation d_0, lateral position change rate d_rate, reference line curvature c_ref, current vehicle speed v, and slope angle θ, the estimated lateral distance between the vehicle and the reference line at time t_adjust can be obtained.

[0088]

[0089] Then, the vehicle front wheel steering angle required to eliminate the lateral error is further calculated based on the lateral distance between the vehicle and the reference line.

[0090] In one embodiment of the present application, determining the expected yaw rate of the vehicle includes adaptively adjusting the expected yaw rate of the vehicle when the load of the commercial vehicle changes.

[0091] because 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. After this step, a yaw rate calculation model for the vehicle is established, and the desired yaw rate of the vehicle can be adaptively adjusted.

[0092] In one embodiment of the present application, the method further includes: when the load of the commercial vehicle changes, based on the collected vehicle parameter data and any one or more vehicle dynamics model parameters including equivalent wheelbase, dynamic equivalent coefficient, and slope compensation coefficient, correcting the yaw rate model in real time to adaptively adjust the desired yaw rate of the vehicle.

[0093] The collected vehicle parameter data includes but is not limited to the front wheel angle, longitudinal vehicle speed, and the yaw rate of the vehicle obtained from the IMU inertial measurement unit.

[0094] The equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic can be calculated based on the accurate mapping relationship between the yaw rate and the front wheel angle when the corresponding parameters are known. Based on the calculation formula in the relevant technology 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.

[0095] Slope compensation coefficient, collects the front wheel steering angle δ, longitudinal vehicle speed v, and yaw rate measured by IMU when the vehicle is driving on different slopes The cross slope angle θ data is used to calibrate the lookup table relationship [θ, k_roll] so that and Relatively close, is the actual yaw rate in the actual scene collected by the IMU, It is the yaw angular velocity calculated by the relevant calculation formula, and k_roll represents the slope compensation coefficient.

[0096] By accurately mapping the yaw rate and front wheel angle obtained on the target road that meets the preset conditions, corresponding calculations can be performed when the corresponding parameters are known. Based on the calculation formula in the relevant technology 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.

[0097] Preferably, the above optimal parameters L_optimal equivalent wheelbase and k_optimal dynamic equivalent coefficient can be obtained in advance through online identification to obtain initial values.

[0098] First, the wheelbase L is sampled evenly with a sampling interval of gap_L.

[0099] For example, the sampling interval is [4.0, 5.0, 6.0, 7.0, 8.0, 9.0] m.

[0100] The specific uniform sampling range and sampling interval are determined based on the characteristics of the specific vehicle through multiple tests. Substituting the sampled wheelbase L, the initial dynamic equivalent coefficient k_kinetic_init, the collected front wheel angle δ, and the longitudinal vehicle speed v into the relevant calculation formula, the calculated yaw rate is compared with the yaw rate measured by the IMU to determine the sampled L_temp with the minimum cost. At low speeds, the impact on L_temp is minimal.

[0101] For example, Where n is the number of data points of the recorded front wheel steering angle δ.

[0102] Similarly, the online identification / adaptive identification of the dynamic equivalent coefficient k_kinetic is adopted, specifically:

[0103] First, uniformly sample the kinetic equivalent coefficient k_kinetic and the sampling interval gap_k.

[0104] For example, the sampling interval is [0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018].

[0105] 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.

[0106] For example, n is the number of data points of the recorded front wheel steering angle δ. In the high speed range, it has a greater impact on k_temp.

[0107] Preferably, the above-mentioned slope compensation coefficient is obtained by the following method:

[0108] The first thing to consider is that when a vehicle is traveling on a road with a horizontal slope, it will be subject to the lateral acceleration component caused by gravity and the friction force that resists lateral movement. Therefore, the lateral component of gravity caused by the horizontal slope is not fully superimposed.

[0109] The lateral acceleration caused by gravity is given by formula (1)a y gravity =k_roll·g·sin(θ), where k_roll is the gain coefficient of the gravity component to the lateral acceleration, that is, the slope compensation coefficient of [slope angle, slope compensation coefficient] to be calibrated in this application, and θ is the slope angle.

[0110] According to the formula (2) of yaw rate

[0111] The lateral acceleration caused by the front wheel angle is given by formula (3)

[0112] The total lateral acceleration is given by formula (4)a y =a δ +a y gravity .

[0113] That is, based on the above formulas (1)-(4) and the vehicle speed v, front wheel turning angle δ, slope angle θ and yaw rate The relationship between them is formula (5) Where L is the equivalent wheelbase of the vehicle, k_kinetic is the vehicle's dynamic equivalent coefficient, which can be determined based on the first yaw rate model, and [θ, k_roll] is a pre-calibrated lookup table mapping relationship.

[0114] Furthermore, the front wheel turning angle δ, longitudinal speed v, and yaw rate measured by IMU are collected when the vehicle is driving on different slopes. The cross slope angle θ data is used to calibrate the lookup table relationship [θ, k_roll] so that and Relatively close, is the actual yaw rate in the actual scene collected by the IMU, It is the yaw angular velocity calculated by the relevant calculation formula, and k_roll represents the slope compensation coefficient.

[0115] Furthermore, and The evaluation index close to The square of the difference between the two is used to calibrate the lookup table relationship of [θ, k_roll] that minimizes the cost.

[0116] In one embodiment of the present application, the method further includes: when the commercial vehicle travels on a road with a transverse slope, calculating a front wheel angle control instruction of the corresponding vehicle based on a modified yaw rate model with slope compensation.

[0117] The above method can adapt to freight trucks with variable loads, automatically identifying the vehicle's yaw rate model and maintaining consistent and adaptable lateral control. Unlike related technologies that lack control instructions tailored to the operation of vehicles with varying loads, the vehicle lateral control method of this application incorporates slope compensation and generates a high-precision yaw rate model to calculate appropriate steering angle control instructions.

[0118] The embodiment of the present application also provides a vehicle lateral control device 300, such as Figure 3 , a schematic structural diagram of a vehicle lateral control device according to an embodiment of the present application is provided. The vehicle lateral control device 300 includes at least: a first determination module 310, a first calculation module 320, a second determination module 330, and a second calculation module 340, wherein:

[0119] In one embodiment of the present application, the first determination module 310 is specifically configured to determine a desired yaw rate of the vehicle.

[0120] Based on the calculation formula in related technology 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. The corresponding relationship between them is established, and the vehicle's yaw angular velocity calculation model is established through this step.

[0121] The slope compensation coefficient calibrated by this method can obtain a more accurate model of the vehicle dynamics yaw rate on the slope, and the yaw rate can be obtained. The relationship between the front wheel turning angle δ, longitudinal vehicle speed v, and slope angle θ k_roll represents the slope compensation coefficient, θ represents the slope angle, and [θ, k_roll] has a preset lookup table relationship. This step establishes a yaw rate calculation model for a vehicle with different slope compensations added.

[0122] For example, the desired yaw rate The equivalent wheelbase L and the dynamic equivalent coefficient k_kinetic of the above vehicle dynamics yaw rate model parameters can be adaptively identified, and the slope compensation coefficient k_roll is usually preset.

[0123] In one embodiment of the present application, the first calculation module 320 is specifically configured to calculate a lateral position change rate of the vehicle according to a current lateral position deviation of the vehicle.

[0124] The derivative of the current lateral position deviation d_0 with respect to time represents the rate of change of the lateral position, that is,

[0125] In one embodiment of the present application, the second determining module 330 is specifically configured to determine a yaw rate based on the reference line according to the reference line and the vehicle speed.

[0126] Calculate the yaw rate of the reference line: Wherein the longitudinal vehicle speed v, c_ref, is the theoretical reference line curvature.

[0127] In one embodiment of the present application, the second calculation module 340 is specifically used to: calculate the vehicle front wheel steering parameters required to eliminate the expected lateral position deviation of the vehicle within the preset adjustment time based on the lateral distance between the vehicle and the reference line within the preset adjustment time, according to the expected yaw angular velocity of the vehicle, the lateral position change rate of the vehicle and the yaw angular velocity based on the reference line, so as to control the vehicle laterally, wherein the expected lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes the expected time to eliminate the lateral position deviation based on the current driving state of the vehicle.

[0128] Based on the lateral distance between the vehicle and the reference line within the preset adjustment time:

[0129]

[0130] According to the desired yaw rate of the vehicle:

[0131] According to the rate of change of the lateral position of the vehicle: d_rate;

[0132] According to the yaw rate based on the reference line:

[0133] The vehicle front wheel steering angle parameters required to eliminate the vehicle's expected lateral position deviation within a preset adjustment time can be calculated:

[0134]

[0135] in,

[0136]

[0137] Finally, the vehicle is laterally controlled based on the front wheel steering parameters, and the yaw rate model is corrected in real time based on the collected data. This can maintain consistency and adaptability of lateral control for commercial vehicles with variable loads, such as freight trucks.

[0138] It can be understood that the above-mentioned vehicle lateral control device can implement each step of the vehicle lateral control method provided in the aforementioned embodiment. The relevant explanations about the vehicle lateral control method are applicable to the vehicle lateral control device and will not be repeated here.

[0139] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 4 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.

[0140] 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 4 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.

[0141] 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.

[0142] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming the vehicle lateral control device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0143] determining a desired yaw rate of the vehicle;

[0144] Calculating a lateral position change rate of the vehicle based on the current lateral position deviation of the vehicle;

[0145] determining a yaw rate based on the reference line according to the reference line and the vehicle speed;

[0146] Based on the lateral distance between the vehicle and the reference line within a preset adjustment time, and in accordance with the expected yaw rate of the vehicle, the rate of change of the lateral position of the vehicle, and the yaw rate based on the reference line, a vehicle front wheel steering angle parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time is calculated to perform lateral control of the vehicle, wherein the expected lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on the current vehicle driving state.

[0147] The above application Figure 2 The methods performed by the vehicle lateral control device disclosed in the illustrated embodiments 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 performed by hardware integrated logic circuits within 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. The methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in 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 within 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.

[0148] The electronic device may also perform Figure 2 The method executed by the vehicle lateral control device in the vehicle lateral control device is realized Figure 2The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0149] 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 2 The method performed by the vehicle lateral control device in the illustrated embodiment is specifically used to perform:

[0150] determining a desired yaw rate of the vehicle;

[0151] Calculating a lateral position change rate of the vehicle based on the current lateral position deviation of the vehicle;

[0152] determining a yaw rate based on the reference line according to the reference line and the vehicle speed;

[0153] Based on the lateral distance between the vehicle and the reference line within a preset adjustment time, and in accordance with the expected yaw rate of the vehicle, the rate of change of the lateral position of the vehicle, and the yaw rate based on the reference line, a vehicle front wheel steering angle parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time is calculated to perform lateral control of the vehicle, wherein the expected lateral position deviation includes the lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on the current vehicle driving state.

[0154] 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.

[0155] 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 1A device that provides the functions specified in a block or multiple blocks.

[0156] 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.

[0157] 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.

[0158] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 vehicle lateral control method, applied to commercial vehicles, wherein: The method comprises: determining a desired yaw rate of the vehicle; Calculating a lateral position change rate of the vehicle based on the current lateral position deviation of the vehicle; pre-calibrating an expected duration of the lateral position change rate; and / or pre-calibrating a preset adjustment time, wherein the preset adjustment time includes the time from time t0 to time t_adjust of the vehicle, where t0 represents the initial time of the vehicle and t_adjust represents the adjustment time of the vehicle; Calculating the lateral position change rate of the vehicle according to the current lateral position deviation of the vehicle includes: filtering the current lateral position deviation of the vehicle through a low-pass filter, and taking the derivative of the filtering result; The derivative result is filtered through a low-pass filter to obtain the lateral position change rate of the vehicle; determining a yaw rate based on the reference line according to the reference line and the vehicle speed; calculating, based on a lateral distance between the vehicle and the reference line within a preset adjustment time, a vehicle front wheel steering parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time according to an expected yaw rate of the vehicle, a lateral position change rate of the vehicle, and the yaw rate based on the reference line, so as to perform lateral control of the vehicle, wherein the expected lateral position deviation includes a lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on a current vehicle driving state; The lateral distance between the vehicle and the reference line within the preset adjustment time includes: Obtain the vehicle's current lateral position deviation, current lateral position change rate, reference line curvature, current vehicle speed, and current cross slope angle; The lateral distance between the vehicle and the reference line within the preset adjustment time is estimated based on the current lateral position deviation of the vehicle, the lateral position change rate of the vehicle within the preset adjustment time, the yaw rate based on the reference line within the preset adjustment time, and the expected yaw rate of the vehicle within the preset adjustment time.

2. The method according to claim 1, wherein: Determining the desired yaw rate of the vehicle includes: When the load of the commercial vehicle changes, the expected yaw rate of the vehicle is adaptively adjusted.

3. The method according to claim 1, wherein: The method further comprises: When the load of the commercial vehicle changes, the yaw rate model is corrected in real time based on the collected vehicle parameter data and any one or more vehicle dynamics model parameters including the equivalent wheelbase, the dynamic equivalent coefficient, and the slope compensation coefficient to adaptively adjust the desired yaw rate of the vehicle.

4. The method according to claim 1, wherein: The method further comprises: When the commercial vehicle travels on a road with a transverse slope, a front wheel steering angle control command of the corresponding vehicle is calculated based on a modified yaw rate model with slope compensation.

5. A vehicle lateral control device, applied to a commercial vehicle, wherein: The device comprises: A first determination module is configured to determine a desired yaw rate of the vehicle; a first calculation module, configured to calculate a lateral position change rate of the vehicle according to a current lateral position deviation of the vehicle; pre-calibrating an expected duration of the lateral position change rate; and / or pre-calibrating a preset adjustment time, wherein the preset adjustment time includes the time from time t0 to time t_adjust of the vehicle, where t0 represents the initial time of the vehicle and t_adjust represents the adjustment time of the vehicle; Calculating the lateral position change rate of the vehicle according to the current lateral position deviation of the vehicle includes: filtering the current lateral position deviation of the vehicle through a low-pass filter, and taking the derivative of the filtering result; The derivative result is filtered through a low-pass filter to obtain the lateral position change rate of the vehicle; a second determining module, configured to determine a yaw rate based on the reference line according to the reference line and the vehicle speed; a second calculation module, configured to calculate, based on a lateral distance between the vehicle and the reference line within a preset adjustment time, a desired yaw rate of the vehicle, a rate of change of the lateral position of the vehicle, and the yaw rate based on the reference line, a vehicle front wheel steering angle parameter required to eliminate an expected lateral position deviation of the vehicle within the preset adjustment time, so as to perform lateral control of the vehicle, wherein the expected lateral position deviation includes a lateral position deviation between the vehicle and the reference line after the preset adjustment time, and the preset adjustment time includes an expected time to eliminate the lateral position deviation based on a current vehicle driving state; The lateral distance between the vehicle and the reference line within the preset adjustment time includes: Obtain the vehicle's current lateral position deviation, current lateral position change rate, reference line curvature, current vehicle speed, and current cross slope angle; The lateral distance between the vehicle and the reference line within the preset adjustment time is estimated based on the current lateral position deviation of the vehicle, the lateral position change rate of the vehicle within the preset adjustment time, the yaw rate based on the reference line within the preset adjustment time, and the expected yaw rate of the vehicle within the preset adjustment time.

6. 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 according to any one of claims 1 to 4.

7. 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 4.

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