Method and device for determining target operation parameter value of target vehicle

Abstract

The invention discloses a method and a device for determining a target operation parameter value of a target vehicle. The method comprises the following steps: acquiring a real-time control mode of the target vehicle, real-time spatio-temporal information corresponding to the real-time control mode, expected spatio-temporal information and an expected operation parameter value; performing comparison processing on the real-time spatio-temporal information and the expected spatio-temporal information to obtain a spatio-temporal information comparison result; if the spatio-temporal information comparison result represents that the real-time spatio-temporal information is consistent with the expected spatio-temporal information, acquiring a real-time operation parameter value of the target vehicle; determining a real-time operation difference value according to the real-time operation parameter value and the expected operation parameter value; according to the real-time operation difference value, performing prediction processing on an operation parameter value required by the target vehicle during longitudinal driving to obtain a longitudinal operation parameter value corresponding to the target vehicle; and adjusting the longitudinal operation parameter value according to the real-time control mode to obtain a target operation parameter value corresponding to the target vehicle. According to the invention, self-adaptive adjustment of the target vehicle during longitudinal driving is realized.

Description

Technical Field

[0001] This application relates to the field of vehicle dynamics control technology, and in particular to a method and apparatus for determining the target operating parameter values ​​of a target vehicle. Background Technology

[0002] Vehicle dynamics models are a key tool in the development of intelligent chassis and the verification of intelligent driving systems. By simulating the dynamic characteristics of real vehicles, they provide an efficient and safe virtual environment for testing functions such as braking, power, and advanced driver assistance systems. In this model, longitudinal trajectory control directly determines the tracking accuracy of vehicle speed and position, and is crucial to the realism and reliability of the simulation results.

[0003] However, traditional longitudinal control methods for vehicle models have significant limitations. They typically employ open-loop or simple feedback control strategies based on a single target vehicle speed, executing the speed setting only once during simulation, thus failing to monitor and dynamically correct the vehicle's operating status in real time. Furthermore, the control module parameters are usually pre-set fixed values, unable to perform online self-tuning based on changing operating conditions. This static, non-adaptive control approach results in poor model robustness, easy accumulation of tracking errors, insufficient overall control accuracy, and low simulation efficiency, making it difficult to meet the advanced development requirements of high fidelity and real-time in-loop testing. Summary of the Invention

[0004] This application provides a method and apparatus for determining the target operating parameter values ​​of a target vehicle, which can achieve adaptive adjustment when the target vehicle is traveling longitudinally.

[0005] On the one hand, this application provides a method for determining target operating parameter values ​​for a target vehicle, the method comprising: The real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values ​​are obtained. The real-time spatiotemporal information and the desired spatiotemporal information are compared and processed to obtain a spatiotemporal information comparison result. If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle is obtained; The real-time operating parameter value and the expected operating parameter value are used to determine the real-time operating difference of the target vehicle. Based on the real-time operational difference, the required operational parameter values ​​for the longitudinal movement of the target vehicle are predicted to obtain the longitudinal operational parameter values ​​corresponding to the target vehicle. The longitudinal operating parameter values ​​are adjusted according to the real-time control mode to obtain the target operating parameter values ​​corresponding to the target vehicle.

[0006] In one exemplary embodiment, the step of predicting the required operating parameter values ​​for the longitudinal movement of the target vehicle based on the real-time operating difference to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle includes: The control parameters in the fuzzy PID controller are updated based on the real-time operating difference to obtain the updated control parameters. Based on the updated control parameters, the real-time operating difference is processed by PID calculation to obtain the longitudinal operating parameter value corresponding to the target vehicle.

[0007] In one exemplary embodiment, updating the control parameters in the fuzzy PID controller based on the real-time operating difference to obtain updated control parameters includes: Obtain the real-time error change rate of the target vehicle; The correction amount of the control parameter is determined based on the real-time operating difference and the real-time error change rate. The updated control parameters are obtained by summing the correction amount of the control parameters with the control parameters themselves.

[0008] In one exemplary embodiment, if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, obtaining the real-time operating parameter values ​​of the target vehicle includes: If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle and the real-time operating time corresponding to the real-time operating parameter value are obtained. The step of obtaining the real-time error change rate of the target vehicle includes: Obtain the historical running difference value corresponding to the target vehicle and the historical running time corresponding to the historical running difference value; the historical running time is less than the real-time running time. Calculate the difference between the real-time running time and the historical running time to obtain the running time difference; Calculate the difference between the real-time operating difference and the historical operating difference to obtain the current operating error; The ratio between the current operating error and the operating time difference is calculated to obtain the real-time error change rate of the target vehicle.

[0009] In one exemplary embodiment, determining the correction amount of the control parameter based on the real-time operating difference and the real-time error change rate includes: The real-time operating difference is fuzzified to obtain the actual membership degree of the fuzzy set corresponding to the fuzzy PID controller; The real-time error change rate is fuzzified to obtain the error membership degree of the fuzzy set corresponding to the fuzzy PID controller; The target membership degree of the fuzzy set is determined based on the actual membership degree and the error membership degree of the fuzzy set. Fuzzy inference is performed on the target membership degree according to the preset fuzzy rules corresponding to the fuzzy PID controller to obtain the fuzzy inference result; The fuzzy inference result is defuzzified to obtain the correction amount of the control parameter.

[0010] In one exemplary embodiment, adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle includes: If the real-time control mode is speed control mode, the target operating parameter value corresponding to the target vehicle is determined according to the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle; The method for constructing the longitudinal dynamic equations includes: If the target vehicle is in driving mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic driving equation corresponding to the target vehicle is constructed; the longitudinal dynamic driving equation includes the correlation between the acceleration, half-shaft torque and drag corresponding to the target vehicle; If the target vehicle is in braking mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic braking equation corresponding to the target vehicle is constructed; the longitudinal dynamic braking equation includes the correlation between the acceleration, wheel cylinder pressure and resistance of the target vehicle. Based on the longitudinal dynamic driving equation and the longitudinal dynamic braking equation, the longitudinal dynamic equation is constructed.

[0011] In one exemplary embodiment, the step of performing PID calculation on the real-time operating difference based on the updated control parameters to obtain the longitudinal operating parameter value corresponding to the target vehicle includes: If the real-time control mode is the speed control mode, the real-time running difference is processed by PID calculation according to the updated control parameters to obtain the target acceleration value corresponding to the target vehicle. If the real-time control mode is acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, perform PID calculation on the real-time operating difference according to the updated control parameters to obtain the first target half-shaft torque value corresponding to the target vehicle. If the real-time control mode is pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the real-time operating difference is processed by PID calculation based on the updated control parameters to obtain the first target wheel cylinder pressure value corresponding to the target vehicle.

[0012] In one exemplary embodiment, if the real-time control mode is a speed control mode, determining the target operating parameter value corresponding to the target vehicle based on the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle includes: If the real-time control mode is the speed control mode, obtain the real-time resistance value of the target vehicle; If the real-time operating parameter value is less than the expected operating parameter value, the acceleration in the longitudinal dynamics driving equation is adjusted to the target acceleration value, and the drag in the longitudinal dynamics driving equation is adjusted to the real-time drag value, so as to obtain the second target half-shaft torque value corresponding to the target vehicle; The second target half-shaft torque value is used as the target operating parameter value; If the real-time operating parameter value is greater than the expected operating parameter value, the acceleration in the longitudinal dynamic braking equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic braking equation is adjusted to the real-time resistance value, thereby obtaining the second target wheel cylinder pressure value corresponding to the target vehicle; The second target wheel cylinder pressure value is used as the target operating parameter value.

[0013] In one exemplary embodiment, adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle further includes: If the real-time control mode is the acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, the first target half-shaft torque value is taken as the target operating parameter value; If the real-time control mode is the pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the first target wheel cylinder pressure value is used as the target operating parameter value.

[0014] On the other hand, a device for determining the target operating parameter values ​​of a target vehicle is provided, the device comprising: The acquisition module is used to acquire the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values. The spatiotemporal information comparison result determination module is used to compare and process the real-time spatiotemporal information and the expected spatiotemporal information to obtain the spatiotemporal information comparison result. The real-time operating parameter value acquisition module is used to acquire the real-time operating parameter values ​​of the target vehicle if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information. The real-time operating difference determination module is used to determine the real-time operating difference of the target vehicle based on the real-time operating parameter values ​​and the expected operating parameter values. The longitudinal operation parameter value determination module is used to predict the operation parameter values ​​required for the longitudinal driving of the target vehicle based on the real-time operation difference, and obtain the longitudinal operation parameter values ​​corresponding to the target vehicle. The target operating parameter value determination module is used to adjust the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle.

[0015] On the other hand, this application provides a method for determining target operating parameter values ​​for a target vehicle, the method comprising: The real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values ​​are obtained. The real-time spatiotemporal information and the desired spatiotemporal information are compared and processed to obtain a spatiotemporal information comparison result. If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle and the real-time operating time corresponding to the real-time operating parameter value are obtained. The real-time operating parameter value and the expected operating parameter value are used to determine the real-time operating difference of the target vehicle. Obtain the historical running difference value corresponding to the target vehicle and the historical running time corresponding to the historical running difference value; the historical running time is less than the real-time running time. Calculate the difference between the real-time running time and the historical running time to obtain the running time difference; Calculate the difference between the real-time operating difference and the historical operating difference to obtain the current operating error; Calculate the ratio between the current operating error and the operating time difference to obtain the real-time error change rate of the target vehicle; The real-time operating difference is fuzzified to obtain the actual membership degree of the fuzzy set corresponding to the fuzzy PID controller; The real-time error change rate is fuzzified to obtain the error membership degree of the fuzzy set corresponding to the fuzzy PID controller; The target membership degree of the fuzzy set is determined based on the actual membership degree and the error membership degree of the fuzzy set. Fuzzy inference is performed on the target membership degree according to the preset fuzzy rules corresponding to the fuzzy PID controller to obtain the fuzzy inference result; The fuzzy inference result is defuzzified to obtain the correction amount of the control parameter; The updated control parameters are obtained by summing the correction amount of the control parameters with the control parameters themselves. If the real-time control mode is the speed control mode, the real-time running difference is processed by PID calculation according to the updated control parameters to obtain the target acceleration value corresponding to the target vehicle. If the real-time control mode is acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, perform PID calculation on the real-time operating difference according to the updated control parameters to obtain the first target half-shaft torque value corresponding to the target vehicle. If the real-time control mode is pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, perform PID calculation on the real-time operating difference according to the updated control parameters to obtain the first target wheel cylinder pressure value corresponding to the target vehicle. If the real-time control mode is the speed control mode, obtain the real-time resistance value of the target vehicle; If the real-time operating parameter value is less than the expected operating parameter value, the acceleration in the longitudinal dynamics driving equation is adjusted to the target acceleration value, and the drag in the longitudinal dynamics driving equation is adjusted to the real-time drag value, so as to obtain the second target half-shaft torque value corresponding to the target vehicle; The second target half-shaft torque value is used as the target operating parameter value; If the real-time operating parameter value is greater than the expected operating parameter value, the acceleration in the longitudinal dynamic braking equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic braking equation is adjusted to the real-time resistance value, thereby obtaining the second target wheel cylinder pressure value corresponding to the target vehicle; The second target wheel cylinder pressure value is used as the target operating parameter value; The method for constructing the longitudinal dynamic equations includes: If the target vehicle is in driving mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic driving equation corresponding to the target vehicle is constructed; the longitudinal dynamic driving equation includes the correlation between the acceleration, half-shaft torque and drag corresponding to the target vehicle; If the target vehicle is in braking mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic braking equation corresponding to the target vehicle is constructed; the longitudinal dynamic braking equation includes the correlation between the acceleration, wheel cylinder pressure and resistance of the target vehicle. Based on the longitudinal dynamic driving equation and the longitudinal dynamic braking equation, the longitudinal dynamic equation is constructed. If the real-time control mode is the acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, the first target half-shaft torque value is taken as the target operating parameter value; If the real-time control mode is the pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the first target wheel cylinder pressure value is used as the target operating parameter value.

[0016] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded by the processor and executed as described above in the method for determining the target operating parameter value of the target vehicle.

[0017] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or at least one program being loaded and executed by a processor to implement the method for determining the target operating parameter values ​​of the target vehicle as described above.

[0018] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, executes the computer instructions, and causes the computer device to perform a method for determining the target operating parameter values ​​of the target vehicle as described above.

[0019] This application provides a method and apparatus for determining target operating parameter values ​​for a target vehicle, which has the following technical effects: This application acquires the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values; compares the real-time spatiotemporal information and the desired spatiotemporal information to obtain a spatiotemporal information comparison result; if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the desired spatiotemporal information, the real-time operating parameter values ​​of the target vehicle are acquired; based on the real-time operating parameter values ​​and the desired operating parameter values, a real-time operating difference of the target vehicle is determined; based on the real-time operating difference, the required operating parameter values ​​for longitudinal driving of the target vehicle are predicted to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle; the longitudinal operating parameter values ​​are adjusted according to the real-time control mode to obtain the target operating parameter values ​​corresponding to the target vehicle. For any of the speed control mode, acceleration control mode, or pedal control mode of the target vehicle during longitudinal travel, the required operating parameter values ​​for longitudinal travel can be predicted based on the real-time operating parameter values ​​of the target vehicle and the real-time operating parameter values ​​between them. This yields the longitudinal operating parameter values, which are then adjusted in conjunction with the real-time control mode of the target vehicle to obtain the target operating parameter values. This achieves adaptive control of the target vehicle for different control modes, resulting in ideal simulation test results. It improves the longitudinal control accuracy and adaptability of the vehicle model to the working environment, thereby enhancing the efficiency of simulation work. Attached Figure Description

[0020] To more clearly illustrate the technical solutions and advantages in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle, as provided in an embodiment of this specification. Figure 2 This is a logic block diagram of a fuzzy adaptive PID control algorithm provided in the embodiments of this specification; Figure 3 This is a flowchart illustrating a method for determining target operating parameter values ​​under multiple control modes of a target vehicle, as provided in the embodiments of this specification. Figure 4 This is a flowchart illustrating a method for determining the target operating parameter values ​​of a target vehicle under a speed control mode, as provided in the embodiments of this specification. Figure 5 This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle under acceleration control mode, as provided in the embodiments of this specification. Figure 6 This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle in pedal control mode, as provided in the embodiments of this specification. Figure 7 This is a longitudinal force analysis diagram of a target vehicle provided in the embodiments of this specification; Figure 8 This is a schematic diagram of the structure of the device for determining the target operating parameter values ​​of the target vehicle provided in the embodiments of this specification.

[0022] Figure 9 This is a schematic diagram of the server structure for a method of determining the target operating parameter value of a target vehicle provided in an embodiment of this specification. Detailed Implementation

[0023] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0025] The following describes a method for determining the target operating parameter values ​​of a target vehicle according to this application. Figure 1This is a flowchart illustrating a method for determining target operating parameter values ​​for a target vehicle, as provided in an embodiment of this specification. This specification provides the operational steps described in the embodiments or flowchart, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown in the embodiments or drawings... Figure 1 As shown, the method can be applied to a controller corresponding to a target vehicle, and the method includes: S101: Obtain the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values.

[0026] In the embodiments of this specification, the target vehicle is a simulated vehicle under test, that is, a mathematical software model running on a server, which accurately simulates the physical characteristics of a real vehicle. When this model receives a control signal, it calculates in real time the reaction of a real vehicle under that condition according to its internal physical laws and outputs the result. That is, the embodiments of this specification are applied in a simulation test environment, which includes a real controller and a target vehicle. The real controller is connected to a hardware-in-the-loop (HIL) simulation platform to control the simulated vehicle, that is, the target vehicle. The target vehicle has three control modes: speed control mode, acceleration control mode, and pedal control mode. The system acquires the target vehicle's real-time control mode, corresponding real-time spatiotemporal information, desired spatiotemporal information, and desired operating parameter values. The real-time spatiotemporal information includes either the target vehicle's real-time position or real-time runtime, determined by the real-time control mode. The desired spatiotemporal information and desired operating parameter values ​​are set within the vehicle simulation model environment. The desired spatiotemporal information is either the target vehicle's desired position or desired runtime, determined by the real-time control mode. The desired operating parameter values ​​are any one of the target vehicle's desired speed, desired acceleration, desired deceleration, desired accelerator pedal opening, or desired brake pedal opening, determined by the real-time control mode.

[0027] S103: The real-time spatiotemporal information and the expected spatiotemporal information are compared and processed to obtain the spatiotemporal information comparison result.

[0028] In the embodiments of this specification, real-time spatiotemporal information and expected spatiotemporal information are compared and processed to obtain a spatiotemporal information comparison result, which characterizes whether the target vehicle has reached the expected position or whether the running time of the target vehicle has reached the expected running time. Specifically, if the real-time control mode is speed control mode, the desired spatiotemporal information preset in the vehicle simulation model environment is the position of the trajectory points of the target vehicle during its movement. It also sets multiple trajectory point locations; the real-time spatiotemporal information is the real-time trajectory point location of the target vehicle, and the spatiotemporal information comparison result indicates whether the real-time trajectory point location of the target vehicle is consistent with the expected trajectory point location. If the real-time control mode is acceleration control mode, the preset desired spatiotemporal information of the vehicle simulation model environment is the simulation runtime of the target vehicle. It also sets multiple simulation runtimes; the real-time spatiotemporal information is the real-time simulation runtime of the target vehicle, and the spatiotemporal information comparison result characterizes whether the real-time simulation runtime of the target vehicle is consistent with the expected simulation runtime. If the real-time control mode is pedal control mode, the desired spatiotemporal information preset in the vehicle simulation model environment is the simulation runtime of the target vehicle. It also sets multiple simulation runtimes; the real-time spatiotemporal information is the real-time simulation runtime of the target vehicle, and the spatiotemporal information comparison result characterizes whether the real-time simulation runtime of the target vehicle is consistent with the expected simulation runtime.

[0029] S105: If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, obtain the real-time operating parameter value of the target vehicle.

[0030] In the embodiments of this specification, if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, that is, the target vehicle reaches the expected trajectory position or the real-time simulation runtime of the target vehicle reaches the expected simulation runtime, then the real-time running parameter value of the target vehicle is obtained. Specifically, if the real-time control mode is speed control mode, the real-time operating parameter value is the real-time speed value of the target vehicle; If the real-time control mode is acceleration control mode, the real-time operating parameter value is the real-time acceleration / deceleration value of the target vehicle, which is calculated based on the real-time speed and historical speed of the target vehicle. If the real-time control mode is pedal control mode, the real-time operating parameter value is the real-time accelerator pedal opening value / brake pedal opening value of the target vehicle, which is calculated based on the real-time pedal opening value and historical pedal opening value of the target vehicle.

[0031] S107: Determine the real-time operating difference of the target vehicle based on the real-time operating parameter values ​​and the expected operating parameter values.

[0032] In the embodiments of this specification, the difference between the desired operating parameter value and the real-time operating parameter value is calculated to obtain the real-time operating difference of the target vehicle; Specifically, if the real-time control mode is speed control mode, the desired operating parameter value is the position of the trajectory point during the target vehicle's movement. Corresponding expected speed The real-time difference is the value when the target vehicle reaches the trajectory point position. The difference between the expected vehicle speed and the actual vehicle speed; If the real-time control mode is acceleration control mode, the expected operating parameter value is the target vehicle simulation runtime. At that time, the expected acceleration / deceleration value of the target vehicle, and the real-time running difference, are the simulation running time of the target vehicle. At that time, the difference between the expected acceleration value and the actual acceleration value / the difference between the expected deceleration value and the actual deceleration value; If the real-time control mode is pedal control mode, the expected operating parameter value is the target vehicle simulation runtime. At that time, the difference between the desired accelerator pedal opening value and the desired brake pedal opening value of the target vehicle, and the real-time running time of the target vehicle simulation running time is . At that time, the difference between the expected accelerator pedal opening value and the actual accelerator pedal opening value / the difference between the expected brake pedal opening value and the actual brake pedal opening value.

[0033] S109: Based on the real-time operating difference, predict the operating parameter values ​​required for the longitudinal movement of the target vehicle to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle.

[0034] In the embodiments of this specification, the real-time error change rate of the target vehicle is determined based on the real-time operating parameter values ​​and historical operating parameter values ​​of the target vehicle. The operating parameter values ​​required for the longitudinal movement of the target vehicle are predicted by combining the real-time operating difference and the real-time error change rate. That is, the three control parameters of the fuzzy PID controller are updated, corrected and adjusted in real time using the real-time operating difference and the real-time error change rate. The corrected control parameters are then input to the adaptive PID controller to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle.

[0035] In this embodiment of the specification, the step of predicting the required operating parameter values ​​for the longitudinal movement of the target vehicle based on the real-time operating difference to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle includes: The control parameters in the fuzzy PID controller are updated based on the real-time operating difference to obtain the updated control parameters. In the embodiments described in this specification, a fuzzy adaptive (Proportion Integral Derivative, PID) control algorithm is used as the control strategy for the longitudinal motion of the target vehicle, such as... Figure 2 As shown, Figure 2 This document provides a logic block diagram of a fuzzy adaptive PID control algorithm as an embodiment. Unlike traditional PID control logic, which requires constant parameter changes and operates only in a single control mode, the fuzzy adaptive PID control algorithm can adaptively adjust the various control parameters of the PID controller across multiple control modes. Specifically, the fuzzy PID control algorithm has three control modules: proportional (P) (parameters can be adaptively varied), integral (I) (parameters can be adaptively varied), and derivative (D) (parameters can be adaptively varied). Common PID controllers are non-interactive, with the proportional, integral, and derivative modules operating in parallel. They achieve closed-loop control by controlling the difference e between the desired value u and the actual value u(t), thus minimizing deviation. In contrast, the fuzzy adaptive PID control algorithm... Figure 2 As shown, this is a complete fuzzy control strategy module, which includes a reference input variable, a process output variable, a fuzzification submodule, a knowledge base submodule, a logical reasoning submodule, and an antifuzzification submodule. First, the reference input is reasonably fuzzified to obtain the fuzzy subset to which the reference input belongs in the fuzzy domain. Then, the knowledge base, composed of a database and a rule base, makes logical judgments and inferences to obtain the corresponding reasonable fuzzy control signal. Finally, the obtained fuzzy control signal is defuzzified and transformed into the corresponding control signal to achieve control. Therefore, by collecting the real-time error change rate of the target vehicle, the real-time operating error value and the real-time error change rate can be used as reference input variables for fuzzy control. This allows for the updating of the control parameters (proportional control parameters, integral control parameters, and derivative control parameters) in the fuzzy PID controller, resulting in updated control parameters and enabling real-time adaptive adjustment of the control parameters.

[0036] In the embodiments of this specification, the step of updating the control parameters in the fuzzy PID controller based on the real-time operating difference to obtain the updated control parameters includes: Obtain the real-time error change rate of the target vehicle; In the embodiments of this specification, the real-time error change rate is calculated based on the real-time operating difference of the target vehicle, the historical operating difference, and the corresponding operating time.

[0037] In this embodiment of the specification, if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, obtaining the real-time operating parameter value of the target vehicle includes: If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle and the real-time operating time corresponding to the real-time operating parameter value are obtained. In the embodiments of this specification, if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter values ​​of the target vehicle and the real-time operating time corresponding to the real-time operating parameter values ​​are obtained and used to calculate the real-time error change rate.

[0038] In the embodiments of this specification, obtaining the real-time error change rate of the target vehicle includes: Obtain the historical running difference value corresponding to the target vehicle and the historical running time corresponding to the historical running difference value; the historical running time is less than the real-time running time. In the embodiments of this specification, the control system stores the historical operating difference e(k-1) and the historical operating time corresponding to the historical operating difference; and the historical operating time is less than the real-time operating time.

[0039] Calculate the difference between the real-time running time and the historical running time to obtain the running time difference; In the embodiments of this specification, the difference between the real-time running time and the historical running time is calculated to obtain the running time difference. t.

[0040] Calculate the difference between the real-time operating difference and the historical operating difference to obtain the current operating error; In the embodiments of this specification, the difference between the real-time running difference e(k) and the historical running difference e(k-1) is calculated to obtain the current running error e(k)-e(k-1).

[0041] The ratio between the current operating error and the operating time difference is calculated to obtain the real-time error change rate of the target vehicle.

[0042] In the embodiments of this specification, the ratio between the current operating error and the difference in operating time is calculated to obtain the real-time error change rate of the target vehicle [e(k)-e(k-1)] / The real-time operating difference e(k) is stored as the historical operating difference e(k-1) for the next cycle. By recording and comparing the operating differences at different times, the real-time error change rate is calculated, enabling the controller to not only respond based on the real-time operating difference but also to perceive the dynamic behavior of the target vehicle. Subsequently, the PID parameters are adjusted in conjunction with the real-time error change rate, which helps to improve the system's response speed and stability, effectively suppresses oscillations, and enables the system to approach the target value more intelligently and smoothly, thereby enhancing the robustness of the control system.

[0043] The correction amount of the control parameter is determined based on the real-time operating difference and the real-time error change rate. In the embodiments of this specification, the real-time running difference and the real-time error change rate are used as input variables for fuzzy control. After a series of operations such as fuzzification, knowledge base logic reasoning and defuzzification, the correction amount of the control parameters is obtained. Specifically, such as Figure 3 As shown, Figure 3 This flowchart illustrates a method for determining target operating parameter values ​​under multiple control modes of a target vehicle, as provided in an embodiment of this specification. The method allows selection of any control mode from speed control mode, acceleration control mode, and pedal control mode. Based on the selected mode, the desired operating parameter values ​​are set and compared with the feedback real-time operating parameter values. Figure 3 The actual longitudinal coordinates, longitudinal speed, and longitudinal acceleration are compared to obtain the real-time operating difference *e* and the real-time error rate of change *ec* of the target vehicle under each control mode. These two values ​​are then fed into the FUZZY controller, whose internal preset fuzzy rules deduce the appropriate adjustment amounts for the three PID control parameters—that is, the correction amounts—based on the fuzzy membership degrees of the real-time operating difference and the real-time error rate of change. .

[0044] In the embodiments of this specification, determining the correction amount of the control parameter based on the real-time operating difference and the real-time error change rate includes: The real-time operating difference is fuzzified to obtain the actual membership degree of the fuzzy set corresponding to the fuzzy PID controller; In the embodiments of this specification, the actual physical range of the real-time operating difference and the real-time error change rate can be determined for each control mode, and a unified fuzzy universe of discourse is set. Based on the upper and lower limits of the fuzzy universe of discourse, the upper and lower limits of the actual range of the real-time operating difference, the quantization factor of the real-time operating difference is calculated, resulting in k_e = (upper limit of fuzzy universe of discourse - lower limit of fuzzy universe of discourse) / (upper limit of actual operating error range - lower limit of actual operating error range). Based on the upper and lower limits of the fuzzy universe of discourse, the upper and lower limits of the actual range of the real-time error change rate, the quantization factor of the real-time error change rate is calculated, resulting in k_e = (upper limit of fuzzy universe of discourse - lower limit of fuzzy universe of discourse) / (upper limit of actual operating error range - lower limit of actual operating error range). K_ec = (Upper bound of fuzzy universe - Lower bound of fuzzy universe) / (Upper bound of actual range of real-time error change rate - Lower bound of actual range of real-time error change rate); Map the real-time running difference to the fuzzy universe range to obtain the fuzzy universe range of the real-time running difference e_fuzzy = e(k) × K_e and the fuzzy universe range of the real-time error change rate ec_fuzzy = ec(k) × K_ec, and perform amplitude limiting processing on them to ensure that they are within the fuzzy universe range and prevent outliers from affecting the stability of fuzzy inference; Then calculate the membership degree of e_fuzzy in each fuzzy set and the membership degree of ec_fuzzy in each fuzzy set, and record the active fuzzy sets with membership degree > 0.

[0045] Therefore, the fuzzy PID controller can set 7 fuzzy sets for the real-time running difference, namely negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM and positive large PB. A membership function is defined for each fuzzy set to fuzzify the real-time running difference, map it to the fuzzy universe of discourse, and calculate the membership degree of the real-time running difference to each fuzzy set, that is, the actual membership degree. For example, if the real-time control mode is speed control mode, the actual input range of the target vehicle, i.e., the actual range of the target vehicle's speed deviation, can be obtained based on preset physical limits / historical data statistics / dynamic adaptive adjustment. For example, it can be [-20, 20] m / s, and the actual range of the speed deviation change rate can be [-5, 5]. The fuzzy domain can be [-6, 6]. In this case, NB is a trigonometric function defined in the domain [-6, -4], NM is a trigonometric function defined in the domain [-5, -2], NS is a trigonometric function defined in the domain [-3, 0], ZO is a trigonometric function defined in the domain [-1, 1], PS is a trigonometric function defined in the domain [0, 3], PM is a trigonometric function defined in the domain [2, 5], and PB is a trigonometric function defined in the domain [4, 6]. At this time, in speed control mode, the fuzzy universe span is 12, the vehicle speed span is 40, and the calculated quantization factor for the vehicle speed deviation is 0.3. The span of the vehicle speed deviation change rate is 10, and the calculated quantization factor for the vehicle speed deviation change rate is 1.2. Assuming the real-time running difference is 5 m / s, mapping it to the fuzzy universe range yields e_fuzzy = 1.5. Calculating its membership degree, since 1.5 is located in the overlapping region of ZO[-1,1] and PS[0,3], we obtain... , All other membership degrees are 0; If the real-time control mode is acceleration control mode, the actual input range of the target vehicle, i.e., the actual range of the target vehicle's acceleration deviation, can be obtained based on preset physical limits / historical data statistics / dynamic adaptive adjustment. For example, it can be [-10, 10]. The actual range of the rate of change of acceleration error can be [-20, 20]. The fuzzy universe of discourse can be [-3,3]. Here, NB is a triangular function defined in the universe of discourse [-3,-2], NM is a triangular function defined in the universe of discourse [-2.5,-0.5], NS is a triangular function defined in the universe of discourse [-1.5,0.5], ZO is a triangular function defined in the universe of discourse [-1,1], PS is a triangular function defined in the universe of discourse [-0.5,1.5], PM is a triangular function defined in the universe of discourse [0.5,2.5], and PB is a triangular function defined in the universe of discourse [2,3]. In this acceleration control mode, the fuzzy universe of discourse span is 6, the acceleration span is 20, and the quantization factor for acceleration deviation is calculated to be 0.3. The span of the acceleration deviation change rate is 40, and the quantization factor for the vehicle speed deviation change rate is calculated to be 0.15. Assume the real-time operating difference is 0.7. Mapping it to the fuzzy universe of discourse, we obtain e_fuzzy as 0.21. Calculating its membership degree, since 0.21 is close to the overlap region of ZO[-1,1] and PS[-0.5,1.5], we obtain... , All other membership degrees are 0; If the real-time control mode is pedal control mode, the actual input range of the target vehicle can be obtained based on preset physical limits / historical data statistics / dynamic adaptive adjustment, i.e., the actual range of the pedal opening difference deviation of the target vehicle. For example, it can be [-100, 100]%, and the actual range of the pedal opening value error change rate can be [-200, 200]% / s, with its fuzzy universe of discourse being [-3, 3]. At this time, the fuzzy universe of discourse span is 12 in pedal control mode, the pedal opening value span is 200, and the quantization factor for the pedal opening value is calculated to be 0.06. The span of the pedal opening error change rate is 400, and the quantization factor for the pedal opening error change rate is calculated to be 0.03. Assuming the real-time running difference is 15%, mapping it to the fuzzy universe of discourse range yields e_fuzzy of 0.9. Calculating the membership degree yields... All other membership degrees are 0.

[0046] The real-time error change rate is fuzzified to obtain the error membership degree of the fuzzy set corresponding to the fuzzy PID controller; In the embodiments of this specification, the real-time error change rate in the fuzzy PID controller can also be set with 7 fuzzy sets, namely negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM and positive large PB. A membership function is defined for each fuzzy set to fuzzify the real-time error change rate, map it to the fuzzy universe of discourse, and calculate the membership degree of the real-time error change rate to each fuzzy set, i.e., the error membership degree. For example, if the real-time control mode is speed control mode, assume the real-time error change rate is 20. Mapping it to the fuzzy universe of discourse, we get e_fuzzy = 24, which exceeds the universe of discourse. Therefore, we perform amplitude limiting, setting the amplitude to the upper limit of the universe of discourse, 6. We then calculate its membership degree. Since 6 reaches the maximum value of PB, we obtain... All other membership degrees are 0; If the real-time control mode is speed control mode, assume the real-time error change rate is 4. Mapping it to the fuzzy universe, we get e_fuzzy = 0.6. Calculating its membership degree, we obtain... All other membership degrees are 0; If the real-time control mode is pedal control mode, assuming the real-time error change rate is 250% / s, mapping it to the fuzzy universe of discourse yields e_fuzzy = 7.5. This exceeds the universe of discourse range, so it is limited to the upper limit of the universe of discourse, 6. Calculating its membership degree, since 6 reaches the maximum value of PB, we obtain... All other membership degrees are 0.

[0047] The target membership degree of the fuzzy set is determined based on the actual membership degree and the error membership degree of the fuzzy set. In the embodiments of this specification, the actual membership degree and error membership degree corresponding to each fuzzy set are used as the target membership degree corresponding to each fuzzy set.

[0048] Fuzzy inference is performed on the target membership degree according to the preset fuzzy rules corresponding to the fuzzy PID controller to obtain the fuzzy inference result; In the embodiments of this specification, a preset fuzzy rule is typically defined as follows: IF e IS A AND ec IS B THEN ΔKp IS C, ΔKi IS D, ΔKd IS E. The antecedent (IF part) involves a fuzzy subset combination of all input variables (real-time operating difference e and real-time error change rate ec). The consequent (THEN part) corresponds to the fuzzy sets of the correction amounts (ΔKp, ΔKi, ΔKd) of the three control parameters. The real-time operating difference e and the real-time error change rate ec measured at the real-time operating moment, after fuzzification, will simultaneously belong to multiple fuzzy sets (e.g., e belongs to both ZO and PS, and ec belongs to both PM and PB). Each fuzzy set of the real-time operating difference e and each fuzzy set of the real-time error change rate ec constitute a combination, which will match the corresponding rule in the preset fuzzy rule. The activation strength of each matched rule is equal to the minimum membership degree of all conditions in the antecedent of the rule (a commonly used AND operation, i.e., Mamdani inference). For example, if a rule "IF e is A AND ec is B" is matched, and the current membership degree of e to A is 0.6 and the membership degree of ec to B is 0.8, then the activation strength of this rule is min(0.6,0.8) = 0.6.

[0049] For each output variable (e.g., ΔKp), all activated rules will provide a fuzzy conclusion (e.g., ΔKp is PB, ΔKp is PM, etc.). These fuzzy conclusions are merged to form a total, comprehensive fuzzy output set. A common method is to take the union, that is, for each point on the output domain, take the maximum value of its membership degree in different rule conclusions and merge it into a total fuzzy output set to obtain the fuzzy inference result.

[0050] The fuzzy inference result is defuzzified to obtain the correction amount of the control parameter.

[0051] In the embodiments of this specification, the fuzzy inference results are defuzzified. For example, the centroid method can be used to calculate the universe of discourse value corresponding to the centroid of the entire synthetic fuzzy output set, and this value is used as the precise output, i.e., the correction amount of the control parameters. By using fuzzy logic, the precise real-time running difference and the real-time error change rate are converted into membership degrees relative to each fuzzy set. Based on preset fuzzy rules, the proportional, integral, and derivative coefficients of the PID controller are dynamically adjusted online. This enables intelligent changes in the control strategy according to the current dynamic response state of the system (such as the magnitude and trend of deviation), improving the dynamic response speed, control accuracy, and stability of the system, and enhancing the robustness of the system.

[0052] The updated control parameters are obtained by summing the correction amount of the control parameters with the control parameters themselves.

[0053] In the embodiments of this specification, the updated control parameters can be obtained by calculating the correction amount of the control parameters and the sum of the control parameters; for example... Figure 3 As shown, after obtaining the correction amount of the control parameters, it is sent to the adaptive PID mode to adjust the control parameters of the fuzzy PID controller in real time, resulting in updated control parameters that are more adaptable to the current vehicle dynamics. By introducing the real-time error change rate, the controller can capture the dynamic trend while sensing the real-time operating difference. When the real-time error change rate is large, even if the real-time operating difference is still small, the controller can predict the potential for excessive deviation or oscillation risk and adjust the parameters in advance to suppress it. Therefore, by combining the real-time operating difference and the real-time error change rate to calculate the correction amount of the control parameters, advanced correction is achieved, enabling the fuzzy PID controller to nonlinearly adjust the strength of its proportional, integral, and derivative actions according to the real-time state of the system's dynamic response, significantly improving the system's response speed, stability accuracy, and anti-interference capability.

[0054] Based on the updated control parameters, the real-time operating difference is processed by PID calculation to obtain the longitudinal operating parameter value corresponding to the target vehicle.

[0055] In the embodiments described in this specification, the updated control parameters are used to calculate the control output corresponding to the real-time operating difference, thereby obtaining the longitudinal operating parameter values ​​corresponding to the target vehicle. Based on the real-time operating difference, the proportional, integral, and derivative coefficients of the PID controller are adjusted in real time and the output is calculated, realizing full-condition adaptive optimization of the controller parameters. This enables the system to intelligently change the control strategy according to the actual dynamic response state: enhancing the proportional action to accelerate the response speed when the deviation is large; strengthening the integral action to accurately eliminate steady-state error when approaching the target; and predicting the system behavior based on the deviation change trend, adjusting the derivative action to effectively suppress overshoot and oscillation, thus improving the robustness of the control system. This allows it to overcome the nonlinearity, time-varying characteristics, and external disturbances of the vehicle system, thereby achieving faster, smoother, and more accurate pedal tracking control.

[0056] In this embodiment of the specification, the step of performing PID calculation on the real-time operating difference based on the updated control parameters to obtain the longitudinal operating parameter value corresponding to the target vehicle includes: If the real-time control mode is the speed control mode, the real-time running difference is processed by PID calculation according to the updated control parameters to obtain the target acceleration value corresponding to the target vehicle. In the embodiments of this specification, if the real-time control mode is speed control mode, such as Figure 4 As shown, Figure 4This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle under a speed control mode, as provided in an embodiment of this specification. The vehicle simulation model is configured with the target vehicle's initial speed, longitudinal coordinates of the target's final position, coordinates of trajectory points during the target vehicle's movement, and corresponding speeds. Combined with the target vehicle's real-time operating parameter values, the real-time operating difference and real-time error rate of change of the target vehicle are calculated. These two values ​​are used as inputs to the vehicle's longitudinal control strategy module, adaptively adjusting the control parameters in the fuzzy PID controller to calculate the target acceleration value corresponding to the target vehicle. This value is then used as the output of the vehicle's longitudinal control strategy module, enabling the target vehicle to quickly and smoothly track the acceleration value required to reach the target speed. Figure 4 The target value of longitudinal acceleration in the middle; Additionally, after calculating the target acceleration value, it is input into the vehicle's longitudinal simulation model. Based on the longitudinal dynamic equation, inverse calculation is performed to solve for the required front / rear axle half-shaft torque (during driving) or front / rear wheel cylinder pressure (during braking), thereby generating the actual acceleration to eliminate errors. Furthermore, the actual longitudinal coordinate position of the target vehicle at the next moment and the corresponding vehicle speed Y(t) are obtained to achieve closed-loop control.

[0057] If the real-time control mode is acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, perform PID calculation on the real-time operating difference according to the updated control parameters to obtain the first target half-shaft torque value corresponding to the target vehicle. In the embodiments of this specification, if the real-time control mode is acceleration control mode, such as Figure 5 As shown, Figure 5 This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle under acceleration control mode, as provided in an embodiment of this specification. The vehicle simulation model is set with the initial vehicle speed, the model simulation running time t, and a certain moment during the operation. Corresponding longitudinal target acceleration / deceleration By combining the real-time operating parameters of the target vehicle, the real-time operating difference and real-time error rate of change of the target vehicle are calculated. These two values ​​are used as inputs to the vehicle longitudinal control strategy module, which adaptively adjusts the control parameters in the fuzzy PID controller to calculate the target half-shaft torque / target wheel cylinder pressure value corresponding to the target vehicle. This value is then used as the output of the vehicle longitudinal control strategy module, enabling the target vehicle to achieve the target half-shaft torque / target wheel cylinder pressure. Figure 5 The value is calculated as: vehicle tire half-shaft torque / vehicle tire wheel cylinder pressure. When the real-time operating parameter value is less than the expected operating parameter value, it indicates that the driving mode is in effect. Therefore, the output should be the first target half-shaft torque value corresponding to the target vehicle. After calculating the target half-shaft torque value / target wheel cylinder pressure value, it is input into the vehicle longitudinal simulation model. Then, forward dynamics calculation is performed, that is, the calculated target half-shaft torque value / target wheel cylinder pressure value is directly substituted into the longitudinal dynamics equation to solve for the target vehicle's acceleration value Y(t) (i.e., Figure 5 The actual vehicle model's longitudinal acceleration is compared with the set target acceleration, and closed-loop feedback control is performed based on the error between the two to ensure that the actual acceleration can reach the set longitudinal acceleration.

[0058] If the real-time control mode is pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the real-time operating difference is processed by PID calculation based on the updated control parameters to obtain the first target wheel cylinder pressure value corresponding to the target vehicle.

[0059] In the embodiments of this specification, if the real-time control mode is the pedal control mode, such as Figure 6 As shown, Figure 6 This is a flowchart illustrating a method for determining target operating parameter values ​​of a target vehicle under pedal control mode, as provided in an embodiment of this specification. The vehicle simulation model is set with the initial vehicle speed, target vehicle speed, model simulation running time t, and a certain moment during operation. The corresponding accelerator pedal opening value / brake pedal opening value, combined with the real-time operating parameters of the target vehicle, are used to calculate the real-time operating difference and real-time error rate of the target vehicle. These two values ​​are then used as inputs to the vehicle longitudinal control strategy module. The control parameters in the fuzzy PID controller are adaptively adjusted to calculate the target half-shaft torque value / target wheel cylinder pressure value for the target vehicle. This value is then used as the output of the vehicle longitudinal control strategy module, enabling the target vehicle to achieve the target half-shaft torque / target wheel cylinder pressure. Figure 6 The value is calculated as: vehicle tire half-shaft torque / vehicle tire wheel cylinder pressure. When the real-time operating parameter value is greater than the expected operating parameter value, it indicates that the vehicle is in braking mode. Therefore, the output should be the first target wheel cylinder pressure value corresponding to the target vehicle. After calculating the target half-shaft torque / wheel cylinder pressure, it is input into the vehicle's longitudinal simulation model. Then, forward dynamics calculations are performed, meaning the calculated target half-shaft torque / wheel cylinder pressure is directly substituted into the longitudinal dynamics equations to solve for the target vehicle's acceleration. This acceleration is then integrated with the target vehicle's running time to obtain the actual vehicle speed. Figure 6The simulation process involves measuring the actual vehicle model's running time and speed Y(t). Additionally, it measures the actual pedal opening on the target vehicle using sensors, feeding back this running time, speed, and actual opening. This data is then compared to predefined parameters, and closed-loop feedback control is implemented based on the error. This ensures the target vehicle's actual pedal response quickly and accurately matches the preset pedal commands during simulation testing. In speed control mode, the target acceleration value is output, enabling the control system to automatically coordinate smooth switching between drive and braking based on speed deviations. However, this requires inverse dynamics calculations on the vehicle model. In acceleration and pedal control mode, the half-shaft torque or wheel cylinder pressure is directly output, extending control to the actuator level. This eliminates the inverse calculation step, improving response speed and reducing latency. This allows the control system to flexibly adapt to simulation requirements at different granularities, from high-level strategy verification to low-level actuator testing, enhancing the system's intelligence, real-time performance, and control output accuracy.

[0060] S111: Adjust the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle.

[0061] In the embodiments of this specification, since the output longitudinal operating parameter values ​​differ under different control modes, specific adjustments are required for specific control modes. Ultimately, all values ​​are output in the form of half-shaft torque / wheel cylinder pressure to obtain the target operating parameter values ​​corresponding to the target vehicle; for example... Figure 3 As shown, the adaptive PID module outputs longitudinal running parameter values, which are then input into the vehicle simulation model. Based on the longitudinal dynamic equation corresponding to the target vehicle, the target running parameter values ​​(half-shaft torque / wheel cylinder pressure) are first obtained, and then the real state Y(t) of the target vehicle at the next moment (actual longitudinal coordinate / longitudinal speed / longitudinal acceleration) is further calculated to achieve real-time closed-loop control of the longitudinal running control of the target vehicle.

[0062] In this embodiment of the specification, adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle includes: If the real-time control mode is speed control mode, the target operating parameter value corresponding to the target vehicle is determined according to the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle; In the embodiments of this specification, if the real-time control mode is the speed control mode, the longitudinal running parameter value is the acceleration / deceleration value of the target vehicle. Since the actual actuators of the target vehicle include, but are not limited to, motors, engines and brake calipers, the acceleration / deceleration cannot be directly understood. Therefore, it is necessary to perform inverse calculation on the longitudinal running parameter value in the speed control mode based on the longitudinal dynamic equation to obtain the target running parameter value corresponding to the target vehicle.

[0063] In this embodiment of the specification, if the real-time control mode is a speed control mode, determining the target operating parameter value corresponding to the target vehicle based on the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle includes: If the real-time control mode is the speed control mode, obtain the real-time resistance value of the target vehicle; In the embodiments of this specification, if the real-time control mode is the speed control mode, the real-time resistance value at the real-time running moment is obtained, including the air resistance of the target vehicle, the rolling resistance of the front axle wheels of the target vehicle, the rolling resistance of the rear axle wheels of the target vehicle, and the slope resistance of the target vehicle.

[0064] If the real-time operating parameter value is less than the expected operating parameter value, the acceleration in the longitudinal dynamics driving equation is adjusted to the target acceleration value, and the drag in the longitudinal dynamics driving equation is adjusted to the real-time drag value, so as to obtain the second target half-shaft torque value corresponding to the target vehicle; In the embodiments of this specification, the real-time operating condition of the target vehicle is judged. If the real-time operating parameter value is less than the expected operating parameter value, it indicates that the target vehicle needs to accelerate and enter the driving mode. The acceleration in the longitudinal dynamic driving equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic equation is adjusted to the real-time resistance value to obtain the second target half-shaft torque value corresponding to the target vehicle, which is the driving force required by the target vehicle at this time.

[0065] The second target half-shaft torque value is used as the target operating parameter value; In this embodiment of the specification, the second target half-shaft torque value is used as the target operating parameter value, and the total driving force is... According to the preset power distribution strategy, it is converted into the front axle half-shaft torque T1 and the rear axle half-shaft torque T2 of the target vehicle, and then sent to the power system so that the target vehicle runs according to the obtained half-shaft torque.

[0066] If the real-time operating parameter value is greater than the expected operating parameter value, the acceleration in the longitudinal dynamic braking equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic braking equation is adjusted to the real-time resistance value, thereby obtaining the second target wheel cylinder pressure value corresponding to the target vehicle; In the embodiments of this specification, if the real-time operating parameter value is greater than the expected operating parameter value, it indicates that deceleration is required and braking mode is entered. The acceleration obtained at this time is negative, which is essentially a deceleration value. The braking force required by the braking system not only needs to offset the kinetic energy of the target vehicle, but also needs to partially or completely utilize the resistance to help decelerate. Therefore, the acceleration in the longitudinal dynamic braking equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic equation is adjusted to the real-time resistance value to obtain the second target wheel cylinder pressure value corresponding to the target vehicle.

[0067] The second target wheel cylinder pressure value is used as the target operating parameter value.

[0068] In the embodiments described in this specification, the second target wheel cylinder pressure value is used as the target operating parameter value, and the total braking force is... According to the preset braking force distribution strategy, it is converted into the front axle wheel cylinder pressure of the specific target vehicle. and rear axle wheel cylinder pressure The measured values ​​are then sent to the braking system, causing the target vehicle to operate according to the obtained wheel cylinder pressure. Based on the actual and expected operating parameters of the target vehicle, the system determines whether the target vehicle is in drive mode or braking mode. Taking into account all external environmental factors (resistance, gradient), the system ultimately calculates the lowest-level physical quantity (half-shaft torque / wheel cylinder pressure) that the actuator can directly understand, thus achieving precise and efficient speed tracking control.

[0069] The method for constructing the longitudinal dynamic equations includes: If the target vehicle is in driving mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic driving equation corresponding to the target vehicle is constructed; the longitudinal dynamic driving equation includes the correlation between the acceleration, half-shaft torque and drag corresponding to the target vehicle; In the embodiments described in this specification, such as Figure 7 As shown, Figure 7 This specification provides a longitudinal force analysis diagram of a target vehicle in an embodiment. A longitudinal coordinate system of the target vehicle's center of mass is established, and the forward direction of the target vehicle is defined as the positive X-axis direction. That is, to the left in the diagram is the positive X-axis direction. Therefore, all forces and accelerations in the same direction as the positive X-axis are positive, and vice versa. According to Newton's second law F=ma, the net force F_total on the longitudinal motion of the target vehicle is equal to the mass m of the target vehicle multiplied by its longitudinal acceleration. That is, F_total=m* ; Force analysis is performed on the vehicle's center of gravity to identify forces in all X-axis directions, thus obtaining the longitudinal tire forces on the front axle wheels of the target vehicle in the positive direction. Longitudinal tire force of rear axle wheels The rolling resistance of the front axle wheels of the target vehicle in the opposite direction Rear axle wheel rolling resistance Gravity gradient component of the target vehicle and air resistance By combining all the longitudinal forces, the initial longitudinal dynamic equations of the target vehicle are obtained:

[0070] in, The mass m of the target vehicle multiplied by the longitudinal acceleration ; The longitudinal tire force of the front axle wheels of the target vehicle; The longitudinal tire force of the rear axle wheels of the target vehicle; The rolling resistance of the front axle wheels of the target vehicle; The rolling resistance of the rear axle wheels of the target vehicle; The gravity gradient component of the target vehicle; This refers to air resistance.

[0071] Without considering tire deformation in the simulation model, the tires of the target vehicle are rigid bodies rotating about a center. If the target vehicle is in drive mode, its power comes from the torque output by the powertrain. In this case, the longitudinal tire force on the front axle wheels of the target vehicle... and longitudinal tire force of the rear axle wheels The longitudinal tire force is generated by the half-shaft torque T1 of the front axle tires and the half-shaft torque T2 of the rear axle tires of the target vehicle. Therefore, under the rigid body assumption that tire deformation is not considered, the longitudinal tire force can be expressed as half-shaft torque / tire radius, thus yielding the longitudinal dynamic driving equation corresponding to the target vehicle:

[0072] in, The mass m of the target vehicle multiplied by the longitudinal acceleration ; The longitudinal tire force on the front axle wheels of the target vehicle; The tire radii of the left and right front wheels of the target vehicle; The longitudinal tire force of the rear axle wheels on the target vehicle; The tire radii of the left and right rear wheels of the target vehicle; The rolling resistance of the front axle wheels of the target vehicle; The rolling resistance of the rear axle wheels of the target vehicle; The gravity gradient component of the target vehicle; This refers to air resistance.

[0073] If the target vehicle is in braking mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic braking equation corresponding to the target vehicle is constructed; the longitudinal dynamic braking equation includes the correlation between the acceleration, wheel cylinder pressure and resistance of the target vehicle. In the embodiments described in this specification, if the target vehicle is in braking mode, the power of the target vehicle is provided by the braking system, and at this time the longitudinal tire force of the front axle wheels of the target vehicle... and longitudinal tire force of the rear axle wheels The braking force is generated by the pressure of the brake wheel cylinders of the target vehicle pushing the brake piston. Without considering heat fade, this braking force can be expressed as the pressure of the front and rear wheel cylinders of the target vehicle × the cross-sectional area of ​​the front and rear brake disc pistons of the target vehicle. Therefore, the longitudinal dynamic braking equation corresponding to the target vehicle is obtained:

[0074] in, The mass m of the target vehicle multiplied by the longitudinal acceleration ; The pressure of the front axle wheel cylinders on the target vehicle; The cross-sectional area of ​​the front axle brake disc piston of the target vehicle; The pressure of the rear axle wheel cylinders on the target vehicle; The cross-sectional area of ​​the rear axle brake disc piston of the target vehicle; The rolling resistance of the front axle wheels of the target vehicle; The rolling resistance of the rear axle wheels of the target vehicle; The gravity gradient component of the target vehicle; This refers to air resistance.

[0075] Based on the longitudinal dynamic driving equation and the longitudinal dynamic braking equation, the longitudinal dynamic equation is constructed.

[0076] In the embodiments of this specification, the longitudinal dynamics driving equation and longitudinal dynamics braking equation corresponding to the target vehicle are combined to obtain the longitudinal dynamics equation of the target vehicle that can be used for simulation; the forces acting on the longitudinal motion of the target vehicle are analyzed according to Newton's second law and synthesized: the driving force / braking force is set to positive, and the rolling resistance, air resistance and gravity slope component are set to negative; finally, the resultant force expression is substituted into Newton's second law to obtain the longitudinal dynamics equation of the target vehicle; thus, in speed control mode, the longitudinal dynamics equation is used to perform inverse calculation to accurately deduce the front and rear axle driving torque or braking pressure required to achieve the longitudinal running parameter values, realizing the direct mapping of the required vehicle speed to half-shaft torque / wheel cylinder pressure, thereby achieving fast, accurate and stable closed-loop control of vehicle speed, effectively improving the system response speed and control accuracy.

[0077] In this embodiment of the specification, adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle further includes: If the real-time control mode is the acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, the first target half-shaft torque value is taken as the target operating parameter value; In the embodiments of this specification, if the real-time control mode is the acceleration control mode and the real-time operating parameter value is less than the expected operating parameter value, the target vehicle needs to accelerate and enter the drive mode. Since the output of the fuzzy PID control algorithm is the half-shaft torque that the actuator can directly understand, the first target half-shaft torque value is used as the target operating parameter value.

[0078] If the real-time control mode is the pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the first target wheel cylinder pressure value is used as the target operating parameter value.

[0079] In the embodiments of this specification, if the real-time control mode is pedal control mode and the real-time operating parameter value is greater than the expected operating parameter value, the target vehicle needs to decelerate and enter braking mode. Since the fuzzy PID control algorithm outputs wheel cylinder pressure that the actuator can directly understand, the first target wheel cylinder pressure value is used as the target operating parameter value. When the target vehicle is in acceleration control mode / pedal control mode, the half-shaft torque / wheel cylinder pressure is directly output, eliminating the calculation steps of inverse dynamics solution, significantly reducing control delay, avoiding inverse calculation of complex and error-prone vehicle models, and the command reaches the actuator directly and without loss, improving the accuracy of the underlying control.

[0080] This manual also provides a device for determining the target operating parameter values ​​of the target vehicle, such as... Figure 8 As shown, the device includes: The acquisition module 801 is used to acquire the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values. The spatiotemporal information comparison result determination module 802 is used to compare the real-time spatiotemporal information and the expected spatiotemporal information to obtain the spatiotemporal information comparison result. The real-time operating parameter value acquisition module 803 is used to acquire the real-time operating parameter values ​​of the target vehicle if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information. The real-time operating difference determination module 804 is used to determine the real-time operating difference of the target vehicle based on the real-time operating parameter values ​​and the expected operating parameter values. The longitudinal running parameter value determination module 805 is used to predict the running parameter values ​​required for the longitudinal driving of the target vehicle based on the real-time running difference, so as to obtain the longitudinal running parameter values ​​corresponding to the target vehicle. The target operating parameter value determination module 806 is used to adjust the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle.

[0081] In some embodiments, the longitudinal running parameter value determination module further includes: The updated control parameter determination submodule is used to update the control parameters in the fuzzy PID controller based on the real-time operating difference to obtain the updated control parameters. The longitudinal running parameter value determination submodule is used to perform PID calculation processing on the real-time running difference based on the updated control parameters to obtain the longitudinal running parameter value corresponding to the target vehicle.

[0082] In some embodiments, the updated control parameter determination submodule further includes: A real-time error change rate acquisition unit is used to acquire the real-time error change rate of the target vehicle. The correction amount determination unit is used to determine the correction amount of the control parameter based on the real-time operating difference and the real-time error change rate. The updated control parameter determination unit is used to calculate the sum of the correction amount of the control parameter and the control parameter to obtain the updated control parameter.

[0083] In some embodiments, the real-time running parameter value acquisition module further includes: The real-time operating parameter value acquisition submodule is used to acquire the real-time operating parameter value of the target vehicle and the real-time operating time corresponding to the real-time operating parameter value if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information. In some embodiments, the real-time error change rate acquisition unit further includes: The historical operation data subunit is used to obtain the historical operation difference corresponding to the target vehicle and the historical operation time corresponding to the historical operation difference; the historical operation time is less than the real-time operation time; The runtime difference determination subunit is used to calculate the difference between the real-time runtime and the historical runtime to obtain the runtime difference. The current operating error determination subunit is used to calculate the difference between the real-time operating error and the historical operating error to obtain the current operating error; The real-time error change rate determination subunit is used to calculate the ratio between the current operating error and the difference in operating time to obtain the real-time error change rate of the target vehicle.

[0084] In some embodiments, the correction amount determination unit further includes: The actual membership determination subunit is used to perform fuzzification processing on the real-time running difference to obtain the actual membership of the fuzzy set corresponding to the fuzzy PID controller. The error membership determination subunit is used to perform fuzzification processing on the real-time error change rate to obtain the error membership of the fuzzy set corresponding to the fuzzy PID controller; The target membership determination subunit is used to determine the target membership degree of the fuzzy set based on the actual membership degree and the error membership degree of the fuzzy set. The fuzzy inference result determination subunit is used to perform fuzzy inference on the target membership degree according to the preset fuzzy rules corresponding to the fuzzy PID controller, and obtain the fuzzy inference result; The correction amount determination subunit is used to defuzzify the fuzzy inference result to obtain the correction amount of the control parameter.

[0085] In some embodiments, the target operating parameter value determination module further includes: The first target operating parameter value determination submodule is used to determine the target operating parameter value corresponding to the target vehicle based on the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle if the real-time control mode is speed control mode. In some embodiments, the apparatus further includes: The longitudinal dynamics driving equation construction module is used to perform force analysis on the longitudinal motion of the target vehicle if the target vehicle is in driving mode, and construct the longitudinal dynamics driving equation corresponding to the target vehicle based on the obtained force analysis results; the longitudinal dynamics driving equation includes the correlation between the acceleration, half-shaft torque and drag of the target vehicle. The longitudinal dynamic braking equation construction module is used to perform force analysis on the longitudinal motion of the target vehicle when the target vehicle is in braking mode, and construct the longitudinal dynamic braking equation corresponding to the target vehicle based on the obtained force analysis results; the longitudinal dynamic braking equation includes the correlation between the acceleration, wheel cylinder pressure and resistance of the target vehicle. The longitudinal dynamics equation construction module is used to construct the longitudinal dynamics equation based on the longitudinal dynamics driving equation and the longitudinal dynamics braking equation.

[0086] In some embodiments, the longitudinal running parameter value determination submodule further includes: The target acceleration value determination unit is used to perform PID calculation processing on the real-time running difference according to the updated control parameters if the real-time control mode is the speed control mode, so as to obtain the target acceleration value corresponding to the target vehicle. The first target half-shaft torque value determination unit is used to perform PID calculation processing on the real-time operating difference based on the updated control parameters if the real-time control mode is acceleration control mode and the real-time operating parameter value is less than the expected operating parameter value, so as to obtain the first target half-shaft torque value corresponding to the target vehicle. The first target wheel cylinder pressure value determination unit is used to perform PID calculation processing on the real-time operating difference based on the updated control parameters if the real-time control mode is pedal control mode and the real-time operating parameter value is greater than the expected operating parameter value, so as to obtain the first target wheel cylinder pressure value corresponding to the target vehicle.

[0087] In some embodiments, the first target operating parameter value determination submodule further includes: A real-time resistance value acquisition unit is used to acquire the real-time resistance value of the target vehicle if the real-time control mode is the speed control mode. The second target half-shaft torque value determination unit is used to adjust the acceleration in the longitudinal dynamics drive equation to the target acceleration value and the resistance in the longitudinal dynamics drive equation to the real-time resistance value if the real-time operating parameter value is less than the expected operating parameter value, so as to obtain the second target half-shaft torque value corresponding to the target vehicle. The first target operating parameter value determination unit is used to take the second target half-shaft torque value as the target operating parameter value; The second target wheel cylinder pressure value determination unit is used to adjust the acceleration in the longitudinal dynamic braking equation to the target acceleration value and the resistance in the longitudinal dynamic braking equation to the real-time resistance value if the real-time operating parameter value is greater than the expected operating parameter value, so as to obtain the second target wheel cylinder pressure value corresponding to the target vehicle. The second target operating parameter value determination unit is used to take the second target wheel cylinder pressure value as the target operating parameter value.

[0088] In some embodiments, the target operating parameter value determination module further includes: The second target operating parameter value determination submodule is used to take the first target half-shaft torque value as the target operating parameter value if the real-time control mode is the acceleration control mode and the real-time operating parameter value is less than the expected operating parameter value. The third target operating parameter value determination submodule is used to take the first target wheel cylinder pressure value as the target operating parameter value if the real-time control mode is the pedal control mode and the real-time operating parameter value is greater than the expected operating parameter value.

[0089] The apparatus and method embodiments described herein are based on the same inventive concept.

[0090] This specification provides an electronic device including a processor and a memory. The memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the method for determining the target operating parameter value of a target vehicle as provided in the above method embodiments.

[0091] Embodiments of this application also provide a computer storage medium, which can be disposed in a terminal to store at least one instruction or at least one program related to the method for determining the target operating parameter value of a target vehicle in the method embodiment. The at least one instruction or at least one program is loaded and executed by the processor to implement the method for determining the target operating parameter value of the target vehicle provided in the above method embodiment.

[0092] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method for determining the target operating parameter values ​​of a target vehicle as provided in the above-described method embodiments.

[0093] The memory described in the embodiments of this specification can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for the functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0094] The method for determining the target operating parameter values ​​of the target vehicle provided in the embodiments of this specification can be executed on a mobile terminal, computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 9 This is a hardware structure block diagram of a server for a method of determining target operating parameter values ​​for a target vehicle, as provided in the embodiments of this specification. Figure 9 As shown, the server 900 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 910 (CPUs 910 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing application programs 923 or data 922. The memory 930 and storage media 920 may be temporary or persistent storage. The program stored in the storage media 920 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 910 may be configured to communicate with the storage media 920 and execute the series of instruction operations stored in the storage media 920 on the server 900. Server 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input / output interfaces 940, and / or one or more operating systems 921, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0095] The input / output interface 940 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 900. In one example, the input / output interface 940 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 940 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0096] Those skilled in the art will understand that Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 900 may also include... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0097] As can be seen from the embodiments of the method and apparatus for determining the target operating parameter value of the target vehicle provided in this application, this application acquires the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter value; compares the real-time spatiotemporal information and the desired spatiotemporal information to obtain a spatiotemporal information comparison result; if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the desired spatiotemporal information, the real-time operating parameter value of the target vehicle is acquired; based on the real-time operating parameter value and the desired operating parameter value, the real-time operating difference of the target vehicle is determined; based on the real-time operating difference, the operating parameter value required for the longitudinal driving of the target vehicle is predicted to obtain the longitudinal operating parameter value corresponding to the target vehicle; the longitudinal operating parameter value is adjusted according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle. For any of the speed control mode, acceleration control mode, or pedal control mode of the target vehicle during longitudinal movement, the required longitudinal movement parameters can be predicted based on the real-time difference between the target vehicle's real-time operating parameters and the desired operating parameters, along with the real-time error rate of change. This prediction process yields the longitudinal movement parameter values, which are then adjusted in conjunction with the target vehicle's real-time control mode to obtain the target operating parameter values. In essence, the vehicle model's longitudinal coordinate position and corresponding speed, model simulation time and corresponding acceleration, and model running time and corresponding pedal opening are used as the control objects to determine the vehicle model's trajectory. The longitudinal control enables real-time control and adjustment of the vehicle model's longitudinal trajectory under joint simulation test conditions involving chassis, powertrain, and intelligent assistance modules. This achieves the goal of real-time controllability of the vehicle's longitudinal trajectory under complex simulation conditions, enabling adjustable longitudinal control of the vehicle model's trajectory based on different control methods. This improves the ease of use of the vehicle simulation model and the efficiency and effectiveness of simulation verification. It also enhances the adaptability of the vehicle model's longitudinal movement and control under different simulation scenarios and the efficiency of simulation conditions. This provides a good guarantee for the quality of work, verification results, and data validity of HIL simulation verification in the intelligent chassis domain, powertrain domain, and driving assistance domain.

[0098] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0100] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer storage medium, such as a read-only memory, a disk, or an optical disk.

[0101] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining target operating parameter values ​​for a target vehicle, characterized in that, The method includes: The real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values ​​are obtained. The real-time spatiotemporal information and the desired spatiotemporal information are compared and processed to obtain a spatiotemporal information comparison result. If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle is obtained; The real-time operating parameter value and the expected operating parameter value are used to determine the real-time operating difference of the target vehicle. Based on the real-time operational difference, the required operational parameter values ​​for the longitudinal movement of the target vehicle are predicted to obtain the longitudinal operational parameter values ​​corresponding to the target vehicle. The longitudinal operating parameter values ​​are adjusted according to the real-time control mode to obtain the target operating parameter values ​​corresponding to the target vehicle.

2. The method according to claim 1, characterized in that, The step of predicting the required operating parameter values ​​for the longitudinal movement of the target vehicle based on the real-time operating difference to obtain the longitudinal operating parameter values ​​corresponding to the target vehicle includes: The control parameters in the fuzzy PID controller are updated based on the real-time operating difference to obtain the updated control parameters. Based on the updated control parameters, the real-time operating difference is processed by PID calculation to obtain the longitudinal operating parameter value corresponding to the target vehicle.

3. The method according to claim 2, characterized in that, The step of updating the control parameters in the fuzzy PID controller based on the real-time operating difference to obtain the updated control parameters includes: Obtain the real-time error change rate of the target vehicle; The correction amount of the control parameter is determined based on the real-time operating difference and the real-time error change rate. The updated control parameters are obtained by summing the correction amount of the control parameters with the control parameters themselves.

4. The method according to claim 3, characterized in that, If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter values ​​of the target vehicle are obtained, including: If the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information, the real-time operating parameter value of the target vehicle and the real-time operating time corresponding to the real-time operating parameter value are obtained. The step of obtaining the real-time error change rate of the target vehicle includes: Obtain the historical running difference value corresponding to the target vehicle and the historical running time corresponding to the historical running difference value; the historical running time is less than the real-time running time. Calculate the difference between the real-time running time and the historical running time to obtain the running time difference; Calculate the difference between the real-time operating difference and the historical operating difference to obtain the current operating error; The ratio between the current operating error and the operating time difference is calculated to obtain the real-time error change rate of the target vehicle.

5. The method according to claim 4, characterized in that, The step of determining the correction amount of the control parameter based on the real-time operating difference and the real-time error change rate includes: The real-time operating difference is fuzzified to obtain the actual membership degree of the fuzzy set corresponding to the fuzzy PID controller; The real-time error change rate is fuzzified to obtain the error membership degree of the fuzzy set corresponding to the fuzzy PID controller; The target membership degree of the fuzzy set is determined based on the actual membership degree and the error membership degree of the fuzzy set. Fuzzy inference is performed on the target membership degree according to the preset fuzzy rules corresponding to the fuzzy PID controller to obtain the fuzzy inference result; The fuzzy inference result is defuzzified to obtain the correction amount of the control parameter.

6. The method according to claim 5, characterized in that, The step of adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle includes: If the real-time control mode is speed control mode, the target operating parameter value corresponding to the target vehicle is determined according to the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle; The method for constructing the longitudinal dynamic equations includes: If the target vehicle is in driving mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic driving equation corresponding to the target vehicle is constructed; the longitudinal dynamic driving equation includes the correlation between the acceleration, half-shaft torque and drag corresponding to the target vehicle; If the target vehicle is in braking mode, a force analysis is performed on the longitudinal motion of the target vehicle, and based on the obtained force analysis results, a longitudinal dynamic braking equation corresponding to the target vehicle is constructed; the longitudinal dynamic braking equation includes the correlation between the acceleration, wheel cylinder pressure and resistance of the target vehicle. Based on the longitudinal dynamic driving equation and the longitudinal dynamic braking equation, the longitudinal dynamic equation is constructed.

7. The method according to claim 6, characterized in that, The step of performing PID calculation on the real-time operating difference based on the updated control parameters to obtain the longitudinal operating parameter value corresponding to the target vehicle includes: If the real-time control mode is the speed control mode, the real-time running difference is processed by PID calculation according to the updated control parameters to obtain the target acceleration value corresponding to the target vehicle. If the real-time control mode is acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, perform PID calculation on the real-time operating difference according to the updated control parameters to obtain the first target half-shaft torque value corresponding to the target vehicle. If the real-time control mode is pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the real-time operating difference is processed by PID calculation based on the updated control parameters to obtain the first target wheel cylinder pressure value corresponding to the target vehicle.

8. The method according to claim 7, characterized in that, If the real-time control mode is a speed control mode, the target operating parameter value corresponding to the target vehicle is determined based on the longitudinal operating parameter value and the longitudinal dynamic equation corresponding to the target vehicle, including: If the real-time control mode is the speed control mode, obtain the real-time resistance value of the target vehicle; If the real-time operating parameter value is less than the expected operating parameter value, the acceleration in the longitudinal dynamics driving equation is adjusted to the target acceleration value, and the drag in the longitudinal dynamics driving equation is adjusted to the real-time drag value, so as to obtain the second target half-shaft torque value corresponding to the target vehicle; The second target half-shaft torque value is used as the target operating parameter value; If the real-time operating parameter value is greater than the expected operating parameter value, the acceleration in the longitudinal dynamic braking equation is adjusted to the target acceleration value, and the resistance in the longitudinal dynamic braking equation is adjusted to the real-time resistance value, thereby obtaining the second target wheel cylinder pressure value corresponding to the target vehicle; The second target wheel cylinder pressure value is used as the target operating parameter value.

9. The method according to claim 8, characterized in that, The step of adjusting the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle further includes: If the real-time control mode is the acceleration control mode, and the real-time operating parameter value is less than the expected operating parameter value, the first target half-shaft torque value is taken as the target operating parameter value; If the real-time control mode is the pedal control mode, and the real-time operating parameter value is greater than the expected operating parameter value, the first target wheel cylinder pressure value is used as the target operating parameter value.

10. A device for determining the target operating parameter values ​​of a target vehicle, characterized in that, The device includes: The acquisition module is used to acquire the real-time control mode of the target vehicle, the real-time spatiotemporal information corresponding to the real-time control mode, the desired spatiotemporal information, and the desired operating parameter values. The spatiotemporal information comparison result determination module is used to compare and process the real-time spatiotemporal information and the expected spatiotemporal information to obtain the spatiotemporal information comparison result. The real-time operating parameter value acquisition module is used to acquire the real-time operating parameter values ​​of the target vehicle if the spatiotemporal information comparison result indicates that the real-time spatiotemporal information is consistent with the expected spatiotemporal information. The real-time operating difference determination module is used to determine the real-time operating difference of the target vehicle based on the real-time operating parameter values ​​and the expected operating parameter values. The longitudinal operation parameter value determination module is used to predict the operation parameter values ​​required for the target vehicle to drive longitudinally based on the real-time operation difference, so as to obtain the longitudinal operation parameter values ​​corresponding to the target vehicle. The target operating parameter value determination module is used to adjust the longitudinal operating parameter value according to the real-time control mode to obtain the target operating parameter value corresponding to the target vehicle.

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