Vehicle control method and related equipment

Abstract

The invention discloses a vehicle control method and related equipment, and the method comprises the steps: obtaining the operation parameters of a vehicle in real time, the operation parameters comprising the driving parameters of the vehicle and the gradient parameters of a road where the vehicle is located; constructing a dynamic model of the vehicle, wherein the dynamic model is used for representing the driving state of the vehicle on the road based on the vehicle dynamics principle; prediction is carried out based on the operation parameters and a kinetic model, target parameters of the vehicle are obtained, and the target parameters comprise target state parameters of the vehicle and target slope parameters of a road where the vehicle is located; and adjusting control parameters of the vehicle based on the target parameters so as to control the vehicle to run on the road based on the control parameters. The problems that in the prior art, the accuracy of direct vehicle control based on sensor detection data is poor, so that the driving experience feeling is reduced, and particularly when a vehicle runs on a ramp, inaccurate vehicle control easily causes a landslide accident, so that the driving safety is reduced are solved.

Description

Background Art

[0002] With the development of vehicle technology, people have higher and higher requirements for vehicle control. In the prior art, since the performance of vehicle sensors is subject to different restrictions in different driving environments, there is a difference between the data detected by the vehicle sensors and the real data, resulting in poor accuracy of direct vehicle control based on sensor detection data, which reduces the driving experience. In particular, when the vehicle is driving on a slope, inaccurate vehicle control can easily cause landslide accidents, reducing driving safety. Summary of the invention

[0003] In order to overcome the problem in the prior art that direct vehicle control based on sensor detection data has poor accuracy, which reduces the driving experience, especially when the vehicle is driving on a slope, inaccurate vehicle control can easily cause landslide accidents, thereby reducing driving safety, the present application provides a vehicle control method and related equipment.

[0004] In a first aspect, in order to solve the above technical problems, the present application provides a vehicle control method, comprising:

[0005] Acquire the vehicle's operating parameters in real time, including the vehicle's driving parameters and the slope parameters of the road on which the vehicle is located;

[0006] Construct a dynamic model of the vehicle, which is used to characterize the driving state of the vehicle on the road based on the principles of vehicle dynamics;

[0007] Based on the operating parameters and the dynamic model, prediction is performed to obtain the target parameters of the vehicle, which include the target state parameters of the vehicle and the target slope parameters of the road where the vehicle is located;

[0008] Based on the target parameters, control parameters of the vehicle are adjusted so as to control the vehicle to travel on the road based on the control parameters.

[0009] In a second aspect, the present application also provides a vehicle control system, comprising:

[0010] An acquisition module is used to acquire the operating parameters of the vehicle in real time, the operating parameters including the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located;

[0011] A construction module, used to construct a dynamic model of the vehicle, the dynamic model is used to characterize the driving state of the vehicle on the road based on the vehicle dynamics principle;

[0012] A prediction module, used to make predictions based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, the target parameters including target state parameters of the vehicle and target slope parameters of the road where the vehicle is located;

[0013] The adjustment module is used to adjust the control parameters of the vehicle based on the target parameters so as to control the vehicle to travel on the road based on the control parameters.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes the steps of a vehicle control method.

[0015] In a fourth aspect, the present application also provides a vehicle, including a memory, a processor, and a program stored in the memory and running on the processor, and when the processor executes the program, steps of a vehicle control method are implemented.

[0016] The beneficial effects of the present application are as follows: first, by acquiring the operating parameters of the vehicle in real time, the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located can be understood based on the operating parameters. Then, a dynamic model of the vehicle is constructed, and the driving state of the vehicle on the road based on the dynamics principle can be understood based on the dynamic model. Finally, prediction is performed based on the operating parameters and the dynamic model to obtain the target parameters of the vehicle, so that the target state parameters of the vehicle and the target slope parameters of the road where the vehicle is located can be understood based on the target parameters, and the control parameters of the vehicle are adjusted based on the target parameters, so as to control the vehicle to travel on the road based on the control parameters. In this way, the parameter state of the vehicle is predicted by the operating parameters collected in real time, and the control parameters of the vehicle are adjusted in a targeted manner based on the predicted target parameters, so that the vehicle can be adaptively driven on the road based on the control parameters, and the vehicle can be accurately and stably controlled regardless of whether there is a slope on the road, which not only improves the control accuracy of the vehicle, but also improves the driving experience, reduces the probability of landslide accidents when the vehicle is driving on a slope, and thus can improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a vehicle control method shown as an exemplary embodiment of the present application;

[0018] Figure 2 This is a flow chart of a vehicle control method provided by the application in an exemplary embodiment of the present application;

[0019] Figure 3 The figure is a schematic diagram of the structure of a vehicle control system showing an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] The following examples are provided to further explain and supplement the present application and do not constitute any limitation to the present application.

[0021] A vehicle control method and related equipment according to an embodiment of the present application are described below in conjunction with the accompanying drawings.

[0022] A vehicle control method in an embodiment of the present application is applied to a terminal device. In the present application scheme, the terminal device is used as the execution subject to illustrate the present application scheme. The terminal device is used to execute the steps of a vehicle control method.

[0023] See also Figure 1 , Figure 1 A vehicle control method is shown as an exemplary embodiment of the present application, such as Figure 1 As shown, the present application provides a vehicle control method, comprising:

[0024] Step S11, obtaining the operating parameters of the vehicle in real time, the operating parameters including the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located;

[0025] Step S12, constructing a dynamic model of the vehicle, where the dynamic model is used to characterize the driving state of the vehicle on the road based on the vehicle dynamics principle;

[0026] Step S13, performing prediction based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, the target parameters including target state parameters of the vehicle and target slope parameters of the road where the vehicle is located;

[0027] Step S14, adjusting the control parameters of the vehicle based on the target parameters, so as to control the vehicle to travel on the road based on the control parameters.

[0028] A vehicle control method of this embodiment, first, by acquiring the operating parameters of the vehicle in real time, the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located can be understood based on the operating parameters. Then, a dynamic model of the vehicle is constructed, and the driving state of the vehicle on the road based on the dynamics principle can be understood based on the dynamic model. Finally, prediction is performed based on the operating parameters and the dynamic model to obtain the target parameters of the vehicle, so that the target state parameters of the vehicle and the target slope parameters of the road where the vehicle is located can be understood based on the target parameters, and the control parameters of the vehicle are adjusted based on the target parameters, so as to control the vehicle to travel on the road based on the control parameters. In this way, the parameter state of the vehicle is predicted by the operating parameters collected in real time, and the control parameters of the vehicle are adjusted in a targeted manner based on the predicted target parameters, so that the vehicle can be adaptively driven on the road based on the control parameters, and whether there is a slope on the road, the vehicle can be accurately and stably controlled, which not only improves the control accuracy of the vehicle, but also improves the driving experience, reduces the probability of landslide accidents when the vehicle is driving on a slope, and thus can improve driving safety.

[0029] In this embodiment, the operating parameters of the vehicle are acquired according to a preset collection cycle, and the corresponding specific steps are as follows:

[0030] Collecting original operating parameters of the vehicle according to a preset collection cycle;

[0031] The original operating parameters are filtered to obtain the vehicle operating parameters.

[0032] In this way, the original operating parameters of the vehicle are collected according to the preset collection period, and the original operating parameters are filtered to eliminate the noise and errors in the original operating parameters, so as to obtain the operating parameters that meet the requirements of purity and accuracy. This can not only improve the signal-to-noise ratio and reduce the workload of the original sensor, which helps to extend the service life of the sensor, but also reduce the interference of noise and errors on subsequent vehicle control, and further improve the accuracy and stability of vehicle control.

[0033] The original operating parameters include vehicle status data and road information such as the longitudinal force, speed, acceleration, etc. of the vehicle, which can be collected through sensors such as the vehicle's IMU (inertial measurement unit), wheel speed sensor, pulsed laser radar, etc. By obtaining this information in advance, the vehicle can better plan the driving path and adjust driving parameters such as shifting strategy and speed control to adapt to the upcoming slope conditions.

[0034] Pulse laser radar can predict the slope and length of the road in advance, which is crucial for the driving strategy and control of intelligent vehicles. When filtering the original acceleration in the original operating parameters, smoothing through a low-pass filter can improve the accuracy and availability of the data. The low-pass filter can use tools such as MATLAB / Simulink. When implementing the solution of this embodiment in Simulink (visual simulation tool), the "Low Pass Filter" module can be used to design a low-pass filter, and its parameters can be adjusted as needed to be able to smooth the original acceleration.

[0035] Optionally, prediction is performed based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, including:

[0036] A vehicle state transition model is established based on the dynamics model. The state transition model is used to characterize the law of vehicle state changes over time according to the vehicle dynamics principle.

[0037] Define the observation model of the vehicle, which is used to characterize the relationship between the slope parameters of the road and the driving parameters of the vehicle;

[0038] Based on Kalman filtering, the prediction parameters of the vehicle at the next time step are predicted according to the state transition model and operating parameters;

[0039] The prediction parameters are updated based on the observation model to obtain the target parameters.

[0040] In this embodiment, through the vehicle state transfer model established based on the dynamic model and the defined vehicle observation model, the prediction parameters of the vehicle at the next time step are obtained based on the Kalman filter prediction, and the prediction parameters are updated based on the observation model to obtain the target parameters. The target state parameters of the vehicle and the target slope parameters of the road where the vehicle is located can be understood from the target parameters, which facilitates the subsequent adjustment of the vehicle control parameters based on the target parameters, so that the vehicle can drive adaptively on the current road, improves the control accuracy of the vehicle, and thus can improve the driving experience.

[0041] In this embodiment, the specific steps of constructing the vehicle state transition model are as follows:

[0042] State variable definition: Define state variables, including vehicle mass (m), vehicle speed (v), and slope (θ).

[0043] The state model expression is: X(k+1)=F(k)X(k)+B(k)u(k)+w(k)

[0044] Where X(k) represents the state vector at time step k;

[0045] F(k) represents the state transfer matrix, which describes the transfer relationship between each state in the state vector;

[0046] B(k) represents the input matrix, which describes the influence of the control input on the state;

[0047] u(k) represents the control input vector at time step k (kth time step);

[0048] w(k) represents the process noise, which is usually assumed to be zero-mean Gaussian white noise.

[0049] The speed V and the vehicle's position state X are measured by the vehicle's IMU and tire sensors. The state vector is expressed as: Where x represents the position of the vehicle in a certain reference coordinate system; v represents the instantaneous speed of the vehicle; a represents the acceleration, which is a control input, then: u = a;

[0050] Then, the state model can be expressed as: Wherein, Ts represents the sampling time interval.

[0051] The state transfer matrix F and input matrix B are expressed as:

[0052] Combining the above formulas, the state transition model expression is:

[0053]

[0054] Among them, x(k+1) represents the position at time step k+1, v(k+1) represents the instantaneous velocity at time step k+1, x(k) represents the position at time step k, v(k) represents the instantaneous velocity at time step k, a(k) represents the acceleration at time step k, and w x (k) represents the process noise corresponding to x, w v (k) represents the process noise corresponding to v.

[0055] The state transfer matrix F(k) of this embodiment is a state transfer Jacobian matrix, which can linearize the original state transfer matrix, thereby simplifying the calculation process and improving the calculation efficiency. The calculation formula of the state transfer Jacobian matrix F(k) is as follows:

[0056]

[0057] Among them, f represents the original state transfer matrix, represents the target state of the current time step, and u=u(k) represents the control input vector of the current time step.

[0058] In this embodiment, while predicting the prediction parameters of the vehicle at the next time step, the covariance of the next time step is also predicted. The predicted covariance can be used to evaluate the overall performance of the system. If the predicted covariance is too large, it may indicate that there is a large uncertainty or noise in the system, and further adjustment or optimization is required to further adjust and control the vehicle in a targeted manner. The calculation formula for the covariance of the next time step is as follows:

[0059] P(k+1|k)=F(k)P(k|k)F T (k)+Q(k);

[0060] Among them, P(k+1|k) represents the covariance of the next time step, F(k) represents the state transfer matrix, P(k|k) represents the covariance of the current time step, and Q(k) represents the covariance matrix of the process noise at the current time step.

[0061] Optionally, based on the state transition model and the operating parameters, the predicted parameters of the vehicle at the next time step are predicted, including:

[0062] Determining initial parameters according to the operating parameters, the initial parameters including initial driving parameters of the vehicle;

[0063] Acquire real-time parameters of the vehicle, including real-time driving parameters of the vehicle;

[0064] Based on the state transition model, the state change of the vehicle is determined according to the initial parameters and real-time parameters;

[0065] According to the state change, the predicted parameters of the vehicle in the next time step are predicted.

[0066] In this embodiment, according to the initial parameters determined by the operating parameters and the real-time parameters of the vehicle, the state change of the vehicle is determined based on the state transition model, and based on the state change, the predicted parameters of the vehicle at the next time step are predicted, so as to prepare for the control of the vehicle at the next moment in advance based on the state change predicted in advance, thereby realizing predictive control of the vehicle.

[0067] Optionally, based on the state transition model, according to the initial parameters and the real-time parameters, determining the state change of the vehicle includes:

[0068] determining an initial state of the vehicle based on the initial parameters;

[0069] Determining a real-time status of the vehicle based on the real-time parameters;

[0070] Based on the initial state and the real-time state, the state change amount of the vehicle is determined by using the state transition model;

[0071] The calculation formula of state change is as follows:

[0072]

[0073] in, represents the state change in the next time step, F(k) represents the state transfer matrix in the state transfer model, It represents the change from the initial state to the real-time state of the current time step (real-time state - initial state), B(k) represents the input matrix, and u(k) represents the control input vector of the current time step.

[0074] In this embodiment, based on the initial state determined by the initial parameters and the real-time state determined by the real-time parameters, the state change of the vehicle is determined by using the state transition model, which facilitates the preparation for controlling the vehicle at the next moment in advance based on the state change predicted in advance, realizes predictive control of the vehicle, and further improves the accuracy and safety of vehicle driving.

[0075] Optionally, based on the state change, the predicted parameters of the vehicle at the next time step are predicted, including:

[0076] The sum of the state change amount and the real-time state is used as the predicted state parameter included in the predicted parameters of the vehicle at the next time step, wherein the predicted state parameter includes the predicted vehicle speed;

[0077] The predicted vehicle speed is calculated using the dynamic model to obtain the predicted parameters of the next time step corresponding to the vehicle on the road, including the slope prediction time parameters.

[0078] Optionally, the prediction parameters include prediction state parameters; the prediction parameters are updated based on the observation model to obtain target parameters, including:

[0079] Calculate the Kalman gain of the prediction parameters;

[0080] The calculation formula of Kalman gain is as follows:

[0081] K(k)=P(k|k-1)H T (k)[H(k)P(k|k-1)H T (k)+R(k)] -1 ;

[0082] Where K(k) represents the Kalman gain of the current time step, P(k|k-1) represents the covariance of the process noise of the previous time step to predict the covariance matrix of the current time step, H(k) represents the observation matrix of the current time step, T represents the transpose, and R(k) represents the covariance matrix of the measurement noise of the current time step;

[0083] The measurement residuals corresponding to the predicted parameters are calculated based on the observation model;

[0084] The measurement residual is calculated as follows:

[0085]

[0086] Among them, y(k) represents the measurement residual, H(k) represents the observation matrix corresponding to the observation model, Indicates the predicted state parameters included in the predicted parameters.

[0087] Update the state of the prediction parameters based on the Kalman gain and the measurement residual to obtain the target parameters;

[0088] The target parameter is calculated as follows:

[0089]

[0090] in, Indicates the target state parameters included in the target parameters, represents the predicted state parameters included in the prediction parameters, K(k) represents the Kalman gain, z(k) represents the measured value (operating parameter), and H(k) represents the observation matrix.

[0091] In this embodiment, the Kalman gain of the predicted parameters is calculated to fuse the predicted parameters using the Kalman gain, that is, the Kalman gain and the measurement residual are used to update the state estimation, so as to achieve adaptive updating of the vehicle state based on the predicted parameters, facilitate the subsequent adjustment of the vehicle's control parameters based on the predicted target parameters, and perform targeted control of the vehicle based on the control parameters, thereby improving the control accuracy of the vehicle and improving the driving experience.

[0092] Among them, the driving parameters in the measured values ​​(operating parameters) may include the measured vehicle speed v and the measured position X, and the slope parameters may include the measured slope θ. The covariance matrix R(k) of the measurement noise can be obtained directly from the technical specifications of the sensor. Sensor manufacturers usually provide parameters such as measurement accuracy, resolution, and linearity, which can be used to estimate the covariance of the measurement noise. For example, if the accuracy of a sensor is ±1%, the covariance of the measurement noise can be calculated based on this accuracy percentage.

[0093] In this embodiment, while updating the target state parameters included in the target parameters, the covariance corresponding to the target state parameters is also updated, and the updated covariance is used to reflect the uncertainty of the updated target state parameters. The calculation formula of the updated covariance is as follows:

[0094] P(k|k)=[IK(k)H(k)]P(k|k-1);

[0095] Among them, P(k|k) represents the covariance of the current time step, K(k) represents the Kalman gain of the current time step, H(k) represents the observation matrix of the current time step, and P(k|k-1) represents the covariance of the previous time step.

[0096] The measurement matrix H(k) of this embodiment is the measurement Jacobian matrix, which can linearize the original measurement matrix, thereby simplifying the calculation process and improving the calculation efficiency. The calculation formula of the measurement Jacobian matrix H(k) is as follows:

[0097]

[0098] Among them, h represents the original observation matrix, Represents the target state at the current time step.

[0099] In this embodiment, the target slope parameter included in the target parameters is calculated using a dynamic model, and the specific steps are as follows:

[0100] Obtaining a target vehicle speed included in a target state parameter in the target parameter;

[0101] The target vehicle speed is calculated using the dynamic model to obtain the target slope parameter of the road.

[0102] Since the vehicle's dynamic model can simulate the vehicle's behavior on a ramp based on factors such as the vehicle's mass, speed, acceleration, and ramp angle, it provides a theoretical basis for slope estimation. Therefore, through the vehicle's dynamic model, it is possible to understand the vehicle's driving state on the road based on the vehicle dynamics principle, and calculate the target speed based on the target state parameters corresponding to the driving state without manual calculation, so that the target slope parameters of the road can be quickly obtained, which improves the efficiency of obtaining the target parameters and can improve the timeliness of subsequent vehicle control.

[0103] The expression of the vehicle's dynamic model is Fx = mgf + 0.5ρC d Av^2+mgθ+m*dv / dt, where Fx represents the longitudinal force of the vehicle, m represents the mass of the vehicle, g represents the acceleration of gravity, f represents the rolling resistance coefficient, and C d represents the air resistance coefficient, ρ is the air density, A represents the front area of ​​the vehicle, v represents the target speed, θ is the ramp angle, and dv / dt represents the longitudinal acceleration. The longitudinal force of the vehicle is described by the gravity component, rolling resistance, air resistance and driving force. Gravity component: mgsin(θ), rolling resistance: f*m*gcos(θ), air resistance: a, driving force: F t .

[0104] Optionally, based on the target parameters, adjusting the control parameters of the vehicle includes:

[0105] Based on the real-time acquired vehicle operating parameters and target parameters, the road slope change is calculated;

[0106] Calculate the target control amount of the vehicle based on the slope change;

[0107] Through the adaptive PID control algorithm, the control parameters of the vehicle are adjusted according to the target control quantity. The control parameters include throttle opening, braking force and steering force.

[0108] In this embodiment, the slope change of the road is calculated based on the real-time acquired operating parameters and target parameters of the vehicle, and the target control amount of the vehicle is calculated based on the slope change, and then the control parameters of the vehicle are adjusted according to the target control amount through the adaptive PID control algorithm. In this way, when adjusting the control parameters of the vehicle, the slope change is taken into account, and the vehicle can be flexibly controlled according to the change of the slope at adjacent moments to improve the flexibility of the vehicle's response to the slope change, thereby improving the control accuracy of the vehicle traveling on the slope, reducing the probability of a landslide accident when the vehicle is traveling on the slope, and thus improving driving safety. At the same time, it can also reduce the acceleration change caused by the slope change and improve the ride comfort.

[0109] In an exemplary embodiment, the target control amount includes an accelerator opening control amount, a brake force control amount, and a steering force control amount.

[0110] Then, the throttle opening control amount of the vehicle is calculated based on the slope change, and the specific process is as follows:

[0111] Because F t =ma, a=gsin(θ);

[0112] Among them, F t represents the vehicle driving force, m represents the vehicle weight, g represents the acceleration, and θ represents the target slope parameter in the predicted target parameter;

[0113] Then, the throttle opening control amount u is related to the driving force and F t The relationship between them can be expressed as F t =K u *u;

[0114] Among them, K u Indicates the conversion coefficient from throttle opening to driving force.

[0115] Combining the above formula, we can get K u *u=m*g*sinθ, then For the slope change Δθ, the throttle opening change Δu can be approximated as

[0116] Then the sum of the throttle opening control amount and the throttle opening change Δu of the vehicle at the previous moment is the throttle opening control amount of the vehicle at the current moment.

[0117] In this way, the throttle of the vehicle is adjusted in a targeted manner through the calculated throttle opening control amount: if the target slope parameter predicted by the vehicle at the next moment shows an increasing trend compared to the real-time slope parameter (for example, from θ=3 to θ=5°), it means that the vehicle needs more power to maintain the speed, so the throttle opening control amount needs to be increased. If the target slope shows a decreasing trend, the throttle opening control amount can be reduced.

[0118] The braking force control amount of the vehicle is calculated based on the slope change, and the calculation formula is as follows:

[0119] Δθ=θ new —θ old ;

[0120] Δa=g*sinΔθ;

[0121] F brake =k*m*Δa

[0122] Among them, Δθ represents the slope change, θ newrepresents the target slope parameter in the predicted target parameter, θ old Indicates the real-time operating parameters including the slope parameter, k represents the braking coefficient, m represents the vehicle weight, g represents the acceleration, F brake Indicates the braking force control amount of the vehicle.

[0123] In this way, when the slope on which the vehicle is traveling is a downhill section, it is necessary to increase the braking force appropriately to control the speed of the vehicle and prevent it from being too fast. When the slope on which the vehicle is traveling is an uphill section, if the vehicle power is insufficient, it may be necessary to brake appropriately to restart the vehicle.

[0124] The steering force control amount of the vehicle is calculated based on the slope change, and the specific process is as follows:

[0125] because

[0126] Where, θV f Represents the vehicle's front wheel slip angle, θV r Represents the rear wheel slip angle of the vehicle, V y represents the lateral velocity of the vehicle, V x represents the longitudinal velocity of the vehicle, l f Represents the distance from the front axle to the center of mass of the vehicle, l r represents the distance from the rear axle to the center of mass of the vehicle, represents the yaw rate of the vehicle;

[0127] The vehicle's front wheel steering force F yf and rear wheel steering force F yr It can be expressed as:

[0128] F yf =2C αf (δ-ΔθV f ), F yr =2C αr (δ-ΔθV r );

[0129] Among them, C αf represents the front wheel cornering stiffness of the vehicle, δ represents the steering angle, ΔθV f Indicates the change in the vehicle's front wheel slip angle, C αr Represents the rear wheel cornering stiffness of the vehicle, ΔθV r Indicates the change in the rear wheel slip angle of the vehicle.

[0130] Since the slope change Δθ can affect the sideslip angle, thus affecting the lateral force, the front wheel steering force change ΔF yf and the rear wheel steering force change ΔF yr It can be approximated as:

[0131] ΔFyf ≈2C αf ΔθV f , ΔF yr ≈2C αr ΔθV r ;

[0132] Then, the front wheel steering force and the front wheel steering force change ΔF of the vehicle at the previous moment are yf The sum is the current front wheel steering force control amount of the vehicle, the rear wheel steering force of the vehicle at the last moment and the rear wheel steering force change ΔF yr The sum is the current rear wheel steering force control amount of the vehicle.

[0133] In this way, the stability and steering characteristics of the vehicle may be affected due to the change in slope, especially when driving on a curve. Therefore, more steering force may be required to keep the vehicle stable when going uphill. Then, the vehicle can be controlled by the calculated steering force control amount, which can improve the stability of the vehicle when driving on the slope.

[0134] Optionally, based on the target parameters, adjusting the control parameters of the vehicle includes:

[0135] After adjusting the control parameters of the vehicle, obtaining updated operating parameters corresponding to the vehicle;

[0136] Determining a response of the vehicle to the control parameters based on the target parameters and the updated operating parameters;

[0137] Calculating adjustment values ​​of PID parameters of the vehicle based on the response;

[0138] The PID parameters are adjusted based on the adjustment values ​​to adjust the response performance of the vehicle to the control parameters.

[0139] In this embodiment, after adjusting the control parameters of the vehicle, the updated operating parameters corresponding to the vehicle are obtained, and the response of the vehicle to the control parameters is determined based on the target parameters and the updated operating parameters. The adjustment value of the PID parameter of the vehicle is calculated based on the response, and the PID parameter is adjusted based on the adjustment value to adjust the response performance of the vehicle to the control parameter, so that the adjusted vehicle responds faster to the slope change, so that when the vehicle is driving on a slope, it can respond to the slope change in time to control the vehicle, improve the control accuracy of the vehicle driving on the slope, reduce the probability of a landslide accident when the vehicle is driving on the slope, and thus improve driving safety.

[0140] Optionally, the PID parameters include K p (proportional gain), K i (integral gain), K d(Differential gain). Based on the response, the adjustment values ​​of the vehicle's PID parameters are obtained, including:

[0141] When it is determined according to the response situation that the response time of the vehicle to the control parameter is greater than the preset time, a first adjustment value corresponding to the proportional gain is calculated based on the response time;

[0142] Calculating a second adjustment value corresponding to the differential gain based on a system closed-loop time constant;

[0143] When it is determined according to the response condition that the vehicle has a response steady-state error to the control parameter, a third adjustment value corresponding to the integral gain is calculated based on the first adjustment value;

[0144] Based on the first adjustment value, the second adjustment value, and the third adjustment value, an adjustment value of the PID parameter is determined.

[0145] In this embodiment, the adjustment values ​​of the current PID parameters of the vehicle are determined in a targeted manner through the response situation: a first adjustment value corresponding to the proportional gain, a second adjustment value corresponding to the differential gain, and a third adjustment value corresponding to the integral gain, so as to facilitate targeted adjustment of the PID parameters based on the adjustment values ​​to adjust the response performance of the vehicle to the control parameters, so that the response speed of the adjusted vehicle to the slope change is accelerated, so that when the vehicle is traveling on a slope, it can respond to the slope change in time to control the vehicle, thereby improving the control accuracy of the vehicle traveling on the slope.

[0146] For proportional gain K p : If the vehicle responds too slowly to the slope change, you can increase K p If the system overshoots, you can reduce K p value.

[0147] The vehicle's response time to a change in slope can be measured by observing the time it takes for the vehicle's state (such as speed or acceleration) to reach a new steady-state value after the vehicle starts to change. If this time exceeds the expected or acceptable range, the vehicle's response can be considered too slow.

[0148] For the PID controller with PID parameters, K p The value can be calculated by the following formula:

[0149]

[0150] Among them, T c represents the closed-loop time constant of the system, T i represents the integration time constant, T d represents the differential time constant, and ΔT represents the lag time of the system (the value obtained by subtracting the preset time from the response time).

[0151] For integral gain K i : If there is a steady-state error, that is, the vehicle cannot fully reach the expected speed or position, K can be increased i value to eliminate steady-state error. i Too large a value may cause system overshoot and oscillation.

[0152] After the system reaches a new steady-state, if there is a persistent non-zero error between the vehicle’s actual state (such as speed or position) and the desired state, this indicates a steady-state error. i The value of K depends on the specific application scenario and system characteristics. i It can be expressed as K p Multiply by the sampling time T divided by the integration time constant T i , that is, K i =K p *T / T i .

[0153] For the differential gain K d : Increase K d The value can improve the stability of the system and reduce overshoot and oscillation. If the system response is too slow, you can reduce K d value.

[0154] For example, when the slope changes from 3° to 5°, the expected vehicle speed should decrease by V1 per second (V1 = 1 km / h), but the actual vehicle speed only decreases by V2 per second (V2 = 0.5 km / h, then V1>V2), which may mean that the system responds slowly. d The calculation formula is as follows:

[0155]

[0156] Among them, T c Represents the system closed-loop time constant, T i Represents the integration time constant.

[0157] In an exemplary embodiment, the method of the present application is simulated and tested: before actual deployment, simulation tests are performed using tools such as MATLAB / Simulink to verify the effectiveness of the slope estimation algorithm of the target slope and the adaptive adjustment strategy of the PID parameters (adaptive PID control algorithm).

[0158] Simulation test process: In MATLAB / Simulink, set the initial speed of the vehicle to v0 = 10km / h. When simulating an uphill scene with a slope change from 0° to 5°, observe the change curve of the vehicle speed.

[0159] Real vehicle testing: The slope estimation algorithm is combined with the vehicle's control system and tested under actual road conditions to verify the accuracy and robustness of the algorithm.

[0160] Verification results: If the vehicle speed is stable at a value close to the expected value (the target speed V1 = 8 km / h at a 5° uphill slope calculated based on the power system and control strategy) in a short period of time (e.g. within 5 seconds), and the fluctuation range is small (±1 km / h), this indicates that the adaptive adjustment strategy of PID parameters (adaptive PID control algorithm) is effective in speed control. The reason is that the fast and stable speed indicates that the system can adjust the power output in time, K p , K i , K d The adaptive adjustment of parameters enables the vehicle to quickly adapt to the impact of slope changes on speed. p It may enable the vehicle to respond quickly to slope changes. The integral gain K i The steady-state error is effectively eliminated, so that the vehicle speed is finally stabilized near the expected value. The differential gain K d Can help reduce overshoot and oscillation during speed adjustment.

[0161] The vehicle control method of the present application can obtain the original operating parameters such as the driving state and road information of the vehicle in a more comprehensive manner by combining IMU (inertial measurement unit) and multiple sensors to obtain the data of the vehicle in real time. After the signal is collected, the original operating parameters are filtered, for example, the acceleration signal is smoothed by a low-pass filter to eliminate the interference of noise and errors in the original operating parameters, and the accuracy and availability of the obtained operating parameters are improved. Then the operating parameters are calculated using Kalman filtering (EKF) and a dynamic model, and the state estimation of the vehicle system is updated in real time to obtain the target parameters of the vehicle. Kalman filtering (EKF) is a powerful nonlinear filter. In slope estimation, EKF can be used to deal with the coupling problem between vehicle mass and slope, provide real-time slope estimation, and obtain the corresponding target slope parameters of the position predicted at the next moment when the vehicle is driving on the ramp. Based on the target slope parameters, the vehicle is controlled to travel on the road in a targeted manner, which can improve the accuracy and stability of the vehicle control, thereby improving the driving experience, reducing the probability of a landslide accident when the vehicle is driving on the ramp, and thus improving driving safety. At the same time, by using the method of the present application, the vehicle can better adapt to different driving conditions and environmental changes, such as uneven road surfaces and changes in vehicle speed, further improving the driving experience.

[0162] See also Figure 2 , Figure 2 In an exemplary embodiment of the present application, a flow chart of a vehicle control method provided by the application is shown as follows: Figure 2As shown, first, the vehicle's IMU (inertial measurement unit), wheel speed sensor, pulse laser radar and other sensors are used to collect the original operating parameters such as vehicle status data and road information. In Simulink, the "Low PassFilter" module can be used to design a low-pass filter, and its original operating parameters can be adjusted as needed to obtain the operating parameters that eliminate noise and errors. Secondly, using the dynamic equation, the formula is Fx = mgf + 0.5ρC d AV^2+mgθ+m*dv / dt, construct the corresponding dynamic model of the vehicle when driving on a slope. In Simulink, build a vehicle slope recognition model consisting of a dynamic model and a Kalman filter algorithm, and use the extended Kalman filter algorithm to simulate the road slope estimation algorithm. Again, use the adaptive PID control algorithm to adjust the vehicle's control input, such as throttle, brake or steering, to adapt to slope changes. Finally, use tools such as MATLAB / Simulink to perform simulation tests to verify the effectiveness of the slope estimation algorithm and the adaptive PID control algorithm, and test them under actual road conditions to verify the accuracy and robustness of the algorithm.

[0163] See also Figure 3 , Figure 3 A vehicle control system is shown as an exemplary embodiment of the present application, such as Figure 3 As shown, the present application provides a vehicle control system 300, including:

[0164] The acquisition module 301 is used to acquire the operating parameters of the vehicle in real time, and the operating parameters include the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located;

[0165] A construction module 302 is used to construct a dynamic model of the vehicle, where the dynamic model is used to characterize the driving state of the vehicle on the road based on the vehicle dynamics principle;

[0166] A prediction module 303 is used to perform prediction based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, the target parameters including target state parameters of the vehicle and target slope parameters of the road where the vehicle is located;

[0167] The adjustment module 304 is used to adjust the control parameters of the vehicle based on the target parameters, so as to control the vehicle to travel on the road based on the control parameters.

[0168] A vehicle control system of this embodiment, first, the operation parameters of the vehicle are acquired in real time by the acquisition module 301, and the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located can be understood based on the operation parameters. Then, the dynamic model of the vehicle is constructed by the construction module 302, and the driving state of the vehicle on the road based on the dynamics principle can be understood based on the dynamic model. Finally, the prediction module 303 is used to predict based on the operation parameters and the dynamic model to obtain the target parameters of the vehicle, so that the target state parameters of the vehicle and the target slope parameters of the road where the vehicle is located can be understood based on the target parameters, and the control parameters of the vehicle are adjusted based on the target parameters by the adjustment module 304, so as to control the vehicle to travel on the road based on the control parameters. In this way, the parameter state of the vehicle is predicted by the operation parameters collected in real time, and the control parameters of the vehicle are adjusted in a targeted manner based on the predicted target parameters, so that the vehicle can be adaptively driven on the road based on the control parameters, and whether there is a slope on the road, the vehicle can be accurately and stably controlled, which not only improves the control accuracy of the vehicle, but also improves the driving experience, reduces the probability of landslide accidents when the vehicle is driving on the slope, and thus can improve driving safety.

[0169] Optionally, the prediction module is specifically used to:

[0170] A vehicle state transition model is established based on the dynamics model. The state transition model is used to characterize the law of vehicle state changes over time according to the vehicle dynamics principle.

[0171] Define the observation model of the vehicle, which is used to characterize the relationship between the slope parameters of the road and the driving parameters of the vehicle;

[0172] Based on Kalman filtering, the prediction parameters of the vehicle at the next time step are predicted according to the state transition model and operating parameters;

[0173] The prediction parameters are updated based on the observation model to obtain the target parameters.

[0174] Optionally, the prediction module is specifically used to:

[0175] Determining initial parameters according to the operating parameters, the initial parameters including initial driving parameters of the vehicle;

[0176] Acquire real-time parameters of the vehicle, including real-time driving parameters of the vehicle;

[0177] Based on the state transition model, the state change of the vehicle is determined according to the initial parameters and real-time parameters;

[0178] According to the state change, the predicted parameters of the vehicle in the next time step are predicted.

[0179] Optionally, the prediction parameters include prediction state parameters; the prediction module is specifically used for:

[0180] Calculate the Kalman gain of the prediction parameters;

[0181] The measurement residuals corresponding to the predicted parameters are calculated based on the observation model;

[0182] The predicted parameters are updated based on the Kalman gain and the measurement residual to obtain the target parameters.

[0183] Optionally, the adjustment module is specifically used to:

[0184] Based on the real-time acquired vehicle operating parameters and target parameters, the road slope change is calculated;

[0185] Calculate the target control amount of the vehicle based on the slope change;

[0186] Through the adaptive PID control algorithm, the control parameters of the vehicle are adjusted according to the target control quantity. The control parameters include throttle opening, braking force and steering force.

[0187] Optionally, the adjustment module is specifically used to:

[0188] After adjusting the control parameters of the vehicle, obtaining updated operating parameters corresponding to the vehicle;

[0189] Determining a response of the vehicle to the control parameters based on the target parameters and the updated operating parameters;

[0190] Calculating adjustment values ​​of PID parameters of the vehicle based on the response;

[0191] The PID parameters are adjusted based on the adjustment values ​​to adjust the response performance of the vehicle to the control parameters.

[0192] Optionally, the adjustment module is specifically used to:

[0193] When it is determined according to the response situation that the response time of the vehicle to the control parameter is greater than the preset time, a first adjustment value corresponding to the proportional gain is calculated based on the response time;

[0194] Calculating a second adjustment value corresponding to the differential gain based on a system closed-loop time constant;

[0195] When it is determined according to the response condition that the vehicle has a response steady-state error to the control parameter, a third adjustment value corresponding to the integral gain is calculated based on the first adjustment value;

[0196] Based on the first adjustment value, the second adjustment value, and the third adjustment value, an adjustment value of the PID parameter is determined.

[0197] It should be noted that the vehicle control system provided in the above embodiment and the vehicle control method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In practical applications, the vehicle control system provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0198] A computing device according to an embodiment of the present application includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, some or all of the steps of the above-mentioned vehicle control method are implemented.

[0199] Among them, the computing device can be a computer, and correspondingly, its program is computer software. The above-mentioned parameters and steps in a computing device of the present application can refer to the parameters and steps in the embodiment of the vehicle control method above, and will not be repeated here.

[0200] In an embodiment of the present application, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed, the steps of the above-mentioned vehicle control method are executed.

[0201] The computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0202] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned computer-readable storage medium may be a non-transitory computer-readable storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, RandomAccess Memory), a disk or an optical disk, and other media that can store program codes, or a transient computer-readable storage medium.

[0203] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0204] Those skilled in the art know that the present application can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, generally referred to as "module" or "system" herein. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code. Computer-readable storage media can be, for example, but not limited to - electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above.

[0205] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0206] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A vehicle control method, characterized in that: include: Acquiring operating parameters of the vehicle in real time, the operating parameters including driving parameters of the vehicle and slope parameters of the road on which the vehicle is located; Constructing a dynamic model of the vehicle, wherein the dynamic model is used to characterize the driving state of the vehicle on the road based on the vehicle dynamics principle; Predicting based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, the target parameters including target state parameters of the vehicle and target slope parameters of the road on which the vehicle is located; Based on the target parameter, a control parameter of the vehicle is adjusted so as to control the vehicle to travel on the road based on the control parameter.

2. The method according to claim 1, characterized in that The predicting based on the operating parameters and the dynamic model to obtain the target parameters of the vehicle includes: Establishing a state transition model of the vehicle based on the dynamic model, wherein the state transition model is used to characterize the law of how the state of the vehicle changes over time according to the vehicle dynamics principle; defining an observation model of the vehicle, wherein the observation model is used to characterize the relationship between the slope parameters of the road and the driving parameters of the vehicle; Based on the Kalman filter, according to the state transition model and the operating parameters, predict the prediction parameters of the vehicle at the next time step; The prediction parameters are updated based on the observation model to obtain target parameters.

3. The method according to claim 2, characterized in that The predicting of the predicted parameters of the vehicle at the next time step based on the state transition model and the operating parameters includes: Determining initial parameters according to the operating parameters, wherein the initial parameters include initial driving parameters of the vehicle; Acquiring real-time parameters of the vehicle, wherein the real-time parameters include real-time driving parameters of the vehicle; Based on the state transition model, determining the state change amount of the vehicle according to the initial parameters and the real-time parameters; According to the state change, the prediction parameters of the vehicle at the next time step are predicted.

4. The method according to claim 2, characterized in that: The prediction parameters include prediction state parameters; the updating of the prediction parameters based on the observation model to obtain target parameters includes: Calculating the Kalman gain of the prediction parameter; Calculate the measurement residual corresponding to the prediction parameter based on the observation model; The predicted parameter is updated in state based on the Kalman gain and the measurement residual to obtain a target parameter.

5. The method according to any one of claims 1 to 4, characterized in that: The adjusting the control parameters of the vehicle based on the target parameters includes: Calculating a change in the slope of the road based on the operating parameters of the vehicle and the target parameters acquired in real time; Calculating a target control amount of the vehicle based on the slope change; The control parameters of the vehicle are adjusted according to the target control amount through an adaptive PID control algorithm, and the control parameters include throttle opening, braking force and steering force.

6. The method according to any one of claims 1 to 4, characterized in that: The adjusting the control parameters of the vehicle based on the target parameters includes: After adjusting the control parameters of the vehicle, obtaining updated operating parameters corresponding to the vehicle; determining a response of the vehicle to the control parameter based on the target parameter and the updated operating parameter; Calculating adjustment values ​​of PID parameters of the vehicle based on the response; The PID parameter is adjusted based on the adjustment value to adjust the response performance of the vehicle to the control parameter.

7. The method according to claim 6, characterized in that The PID parameters include proportional gain, integral gain and differential gain; the adjustment value of the PID parameter of the vehicle is obtained based on the response condition, including: When it is determined according to the response condition that the response time of the vehicle to the control parameter is greater than a preset time, a first adjustment value corresponding to the proportional gain is calculated based on the response time; Calculating a second adjustment value corresponding to the differential gain based on a system closed-loop time constant; When it is determined according to the response condition that the vehicle has a response steady-state error to the control parameter, a third adjustment value corresponding to the integral gain is calculated based on the first adjustment value; An adjustment value of the PID parameter is determined based on the first adjustment value, the second adjustment value, and the third adjustment value.

8. A vehicle control system, characterized in that: include: An acquisition module, used for acquiring in real time the operating parameters of the vehicle, the operating parameters including the driving parameters of the vehicle and the slope parameters of the road where the vehicle is located; A construction module, used to construct a dynamic model of the vehicle, wherein the dynamic model is used to characterize the driving state of the vehicle on the road based on the vehicle dynamics principle; A prediction module, configured to perform prediction based on the operating parameters and the dynamic model to obtain target parameters of the vehicle, wherein the target parameters include target state parameters of the vehicle and target slope parameters of the road on which the vehicle is located; An adjustment module is used to adjust a control parameter of the vehicle based on the target parameter so as to control the vehicle to travel on the road based on the control parameter.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the steps of a vehicle control method as described in any one of claims 1 to 7.

10. A vehicle, comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a vehicle control method as described in any one of claims 1 to 7 are implemented.

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