Vehicle drift trajectory control methods, devices, equipment and storage media
By constructing an initial transition trajectory and calculating control parameters using state error and tracking error models, the problem of low accuracy in vehicle drift trajectory control was solved, and high-precision trajectory control of the vehicle under extreme conditions was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGFENG MOTOR CO LTD DONGFENG NISSAN PASSENGER VEHICLE CO
- Filing Date
- 2023-03-21
- Publication Date
- 2026-05-26
Smart Images
Figure CN116215537B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle control technology, and in particular to a method, apparatus, device, and storage medium for controlling vehicle drift trajectory. Background Technology
[0002] Drifting, tail-sliding, and other extreme control techniques can break through the constraints of steady-state control, giving vehicles greater freedom and improving their active safety in conditions such as sideslip, roll, and low road adhesion.
[0003] Current autonomous driving control technology primarily focuses on steady-state vehicle control, where all wheels have good traction, and the vehicle operates on a stable equilibrium surface approximating an ideal Ackermann model. When disturbed, the vehicle tends to automatically return to equilibrium, making control relatively easy. However, in extreme conditions such as sideslip, drifting, or fishtailing, the rear wheels typically slip. To maintain balance, the front wheels need to counter-steer, placing the vehicle on an unstable equilibrium surface. When disturbed, the vehicle tends to deviate further from its equilibrium state, making control more challenging.
[0004] Current control methods used in autonomous driving tracking are mainly aimed at vehicle steady-state control. The vehicle state is similar to the Ackerman model, which makes control relatively easy. In the process of vehicle tracking and drifting, it is generally assumed that the vehicle state is near the drift steady-state equilibrium point. However, the problem is that when the vehicle deviates far from the tracking matching point, it is difficult for the vehicle to converge to the desired steady state, and the accuracy of vehicle drift trajectory control cannot be guaranteed.
[0005] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0006] The main objective of this invention is to provide a vehicle drift trajectory control method, device, equipment, and storage medium, aiming to solve the technical problem that the accuracy of vehicle drift trajectory control cannot be guaranteed in the prior art.
[0007] To achieve the above objectives, the present invention provides a vehicle drift trajectory control method, which includes the following steps:
[0008] Obtain the state deviation between the vehicle and the target trajectory point on the desired trajectory;
[0009] When the state deviation exceeds a preset range, an initial transition trajectory is constructed, and several sampling points are obtained from the initial transition trajectory.
[0010] The initial transition trajectory is adjusted based on several sampling points. The difference in state parameters between the vehicle and the target trajectory point is substituted into the vehicle state error model to calculate control parameters. The state of the vehicle is then pre-adjusted according to the adjusted transition trajectory based on the control parameters.
[0011] Obtain the current state parameters of the pre-adjusted vehicle, and determine the target state parameters based on the desired trajectory;
[0012] Determine the parameter difference between the current state parameter and the target state parameter;
[0013] Calculate reference control parameters based on the parameter differences and the vehicle tracking error model;
[0014] The vehicle is controlled to drift along the desired trajectory based on the reference control parameters.
[0015] Optionally, adjusting the initial transition trajectory based on several sampling points includes:
[0016] Determine the foresight time endpoint corresponding to the initial transition trajectory;
[0017] Obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the endpoint of the forward-looking time;
[0018] Determine the trajectory points on the desired trajectory that correspond to each of the plurality of sampling points;
[0019] The error between each sampling point and the corresponding trajectory point is obtained, and the error includes at least the speed error, yaw speed error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error.
[0020] The initial transition trajectory is adjusted based on the endpoint lateral error, the endpoint velocity angle error, and the error between each sampling point and its corresponding trajectory point.
[0021] Optionally, after pre-adjusting the vehicle's state according to the adjusted transition trajectory, the method further includes:
[0022] Then, several sampling points are obtained from the adjusted transition trajectory;
[0023] The adjusted transition trajectory is readjusted based on several sampling points, and the state of the vehicle is pre-adjusted according to the readjusted transition trajectory.
[0024] Optionally, constructing the initial transition trajectory includes:
[0025] Obtain the preset forward look time;
[0026] The endpoint of the forward look-ahead time is determined based on the preset forward look-ahead time.
[0027] An initial transition trajectory is constructed based on the vehicle and the forward-looking time endpoint.
[0028] Optionally, obtaining the state deviation between the vehicle and the target trajectory point on the desired trajectory includes:
[0029] Get the vehicle's current location;
[0030] Based on the current location, determine the target trajectory point on the desired trajectory that is closest to the vehicle;
[0031] Calculate the state parameter difference between the vehicle and the trajectory point, and use the state parameter difference as the state deviation.
[0032] Optionally, before obtaining the state deviation between the vehicle and the target trajectory point on the desired trajectory, the method further includes:
[0033] Construct a vehicle state error model corresponding to the vehicle;
[0034] The vehicle is adjusted to a steady-state drift state based on the vehicle state error model.
[0035] When the vehicle is in the steady-state drift state, the step of obtaining the state deviation between the vehicle and the target trajectory point on the desired trajectory is performed.
[0036] Optionally, controlling the vehicle to drift according to the desired trajectory based on the reference control parameters includes:
[0037] Query the desired tracking state that the vehicle can achieve after a preset time.
[0038] Determine the tracking reference control parameters corresponding to the preset time based on the desired tracking state;
[0039] Obtain the state ratio coefficient between the current state of the vehicle and the desired tracking state;
[0040] Calculate the target control parameters based on the state proportional coefficient, the reference control parameters, and the tracking reference control parameters;
[0041] The vehicle is controlled to drift along the desired trajectory according to the target control parameters.
[0042] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drift trajectory control device, the vehicle drift trajectory control device comprising:
[0043] The acquisition module is used to acquire the state deviation between the vehicle and the target trajectory point on the desired trajectory.
[0044] The acquisition module is also used to construct an initial transition trajectory and acquire several sampling points from the initial transition trajectory when the state deviation exceeds a preset range;
[0045] The control module is used to adjust the initial transition trajectory based on several sampling points, substitute the state parameter difference between the vehicle and the target trajectory point into the vehicle state error model to calculate control parameters, and pre-adjust the state of the vehicle according to the adjusted transition trajectory based on the control parameters.
[0046] The calculation module is used to obtain the current state parameters of the pre-adjusted vehicle and determine the target state parameters based on the desired trajectory;
[0047] The calculation module is also used to determine the parameter difference between the current state parameter and the target state parameter;
[0048] The calculation module is also used to calculate reference control parameters based on the parameter difference and the vehicle tracking error model;
[0049] The control module is also used to control the vehicle to drift along the desired trajectory according to the reference control parameters.
[0050] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drift trajectory control device, which includes: a memory, a processor, and a vehicle drift trajectory control program stored in the memory and running on the processor, wherein the vehicle drift trajectory control program is configured to implement the vehicle drift trajectory control method as described above.
[0051] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a vehicle drift trajectory control program, which, when executed by a processor, implements the vehicle drift trajectory control method as described above.
[0052] This invention constructs an initial transition trajectory when the state deviation between the vehicle and the target trajectory point on the desired trajectory exceeds a preset range. The initial transition trajectory is adjusted based on several sampling points. Control parameters, obtained from the state parameter difference between the vehicle and the target trajectory point, are used to pre-adjust the vehicle's state according to the adjusted transition trajectory. Reference control parameters are calculated based on the parameter difference between the pre-adjusted vehicle's current state parameters and the target state parameters determined by the desired trajectory, combined with the vehicle's tracking error model. The vehicle is then controlled to drift along the desired trajectory according to these reference control parameters. This invention improves the accuracy of vehicle drift trajectory control by first transitioning the vehicle to a balanced state when its state deviates significantly from the trajectory matching point, and then controlling the vehicle according to the desired trajectory. Attached Figure Description
[0053] Figure 1 This is a schematic diagram of the structure of the vehicle drift trajectory control device in the hardware operating environment involved in the embodiments of the present invention;
[0054] Figure 2 This is a flowchart illustrating the first embodiment of the vehicle drift trajectory control method of the present invention;
[0055] Figure 3 This is a schematic diagram of a vehicle model in one embodiment of the vehicle drift trajectory control method of the present invention;
[0056] Figure 4 This is a flowchart of an open-loop drift initiation model in one embodiment of the vehicle drift trajectory control method of the present invention;
[0057] Figure 5 This is a two-dimensional surface diagram of state parameters in one embodiment of the vehicle drift trajectory control method of the present invention;
[0058] Figure 6 This is a two-dimensional surface diagram of a control parameter in one embodiment of the vehicle drift trajectory control method of the present invention;
[0059] Figure 7 This is a two-dimensional surface diagram of another control parameter in one embodiment of the vehicle drift trajectory control method of the present invention;
[0060] Figure 8 This is a schematic diagram of tracking error in one embodiment of the vehicle drift trajectory control method of the present invention;
[0061] Figure 9 This is a two-dimensional surface diagram of the state parameters of the vehicle under the error between the vehicle and the desired trajectory in one embodiment of the vehicle drift trajectory control method of the present invention;
[0062] Figure 10 This is a two-dimensional surface schematic diagram of control parameters under the error between the vehicle and the desired trajectory in one embodiment of the vehicle drift trajectory control method of the present invention;
[0063] Figure 11 This is a schematic diagram of a two-dimensional surface representing another control parameter under the error between the vehicle and the desired trajectory in one embodiment of the vehicle drift trajectory control method of the present invention;
[0064] Figure 12 This is a flowchart illustrating the second embodiment of the vehicle drift trajectory control method of the present invention;
[0065] Figure 13 This is a schematic diagram of the transition trajectory and sampling points in one embodiment of the vehicle drift trajectory control method of the present invention;
[0066] Figure 14This is a structural block diagram of the first embodiment of the vehicle drift trajectory control device of the present invention.
[0067] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0068] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.
[0069] Reference Figure 1 , Figure 1 This is a schematic diagram of the vehicle drift trajectory control device structure in the hardware operating environment involved in the embodiments of the present invention.
[0070] like Figure 1 As shown, the vehicle drift trajectory control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0071] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the vehicle drift trajectory control device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0072] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle drift trajectory control program.
[0073] exist Figure 1In the vehicle drift trajectory control device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the vehicle drift trajectory control device of the present invention can be set in the vehicle drift trajectory control device, and the vehicle drift trajectory control device calls the vehicle drift trajectory control program stored in the memory 1005 through the processor 1001 and executes the vehicle drift trajectory control method provided in the embodiment of the present invention.
[0074] This invention provides a vehicle drift trajectory control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a vehicle drift trajectory control method according to the present invention.
[0075] In this embodiment, the vehicle drift trajectory control method includes the following steps:
[0076] Step S10: Obtain the state deviation between the vehicle and the target trajectory point on the desired trajectory.
[0077] In this embodiment, the executing entity is the vehicle drift trajectory control device, which has functions such as data acquisition, data communication, and program execution. Of course, other devices with similar functions can also be used; this embodiment does not limit this. This embodiment uses a vehicle drift trajectory control device as an example for explanation.
[0078] It should be noted that the current control methods used for autonomous driving tracking are mainly aimed at vehicle steady-state control. The vehicle state is similar to the Ackerman model, which makes the control relatively easy. In addition, during the vehicle tracking drift, it is generally assumed that the vehicle state is near the drift steady-state equilibrium point. However, the problem is that when the vehicle deviates far from the tracking matching point, it is difficult for the vehicle to converge to the desired steady state, and the accuracy of the vehicle drift trajectory control cannot be guaranteed.
[0079] In this embodiment, to solve the aforementioned technical problem, the state deviation between the vehicle and the target trajectory point on the desired trajectory is obtained. When the state deviation exceeds a preset range, an initial transition trajectory is constructed, and several sampling points are obtained from the initial transition trajectory. The initial transition trajectory is adjusted based on the sampling points, and the vehicle's state is pre-adjusted according to the adjusted transition trajectory. The current state parameters of the pre-adjusted vehicle are obtained, and target state parameters are determined based on the desired trajectory. The parameter difference between the current state parameters and the target state parameters is determined. Reference control parameters are calculated based on the parameter difference and the vehicle tracking error model. The vehicle is controlled to drift according to the desired trajectory based on the reference control parameters. By using the above method, when the vehicle's state deviates significantly from the trajectory matching point, the vehicle can first be transitioned to a balanced state, and then the vehicle can be controlled according to the desired trajectory, thus improving the accuracy of vehicle drift trajectory control.
[0080] In this specific implementation, the state deviation between the vehicle and the target trajectory point on the desired trajectory can be obtained. This state deviation can be the difference in state parameters between the vehicle and the target trajectory point. This difference can be calculated by subtracting the current state parameter difference of the vehicle from the reference state parameter value corresponding to the trajectory point. The target trajectory point can be the closest trajectory point to the vehicle on the desired trajectory, based on the vehicle's current position.
[0081] Furthermore, before performing the above operations, this embodiment needs to first control the vehicle to a steady-state drift state. Controlling the vehicle to a steady-state drift state can be achieved using a vehicle state error model. Since this embodiment does not rely on an empirical dynamics model, vehicle physical parameter calibration is not required. When drift control of the vehicle is needed, the current state parameters of the vehicle can be directly obtained. The current state parameters of the vehicle obtained in this embodiment include, but are not limited to, the center of mass velocity, sideslip angle, yaw rate, and axial velocity. When the vehicle enters a drift equilibrium state, it is a three-degree-of-freedom model. Specifying different drift directions simplifies it to a two-degree-of-freedom model, that is, specifying two linearly independent dynamic parameters determines the vehicle state during all other drifts, such as... Figure 3 As shown. Figure 3 In this context, r represents the vehicle's yaw rate, β represents the vehicle's center of gravity velocity and sideslip angle, V represents the vehicle's axial velocity, Fxr and Fxy represent the vehicle's tire driving force, and Φ represents the vehicle's steering wheel angle.
[0082] In this embodiment, the center-of-gravity velocity sideslip angle and yaw rate are selected as control variables, with the axial velocity being the key parameter for observing the vehicle's state. Therefore, the current state of the vehicle can be determined using these parameters. It is important to emphasize that when the center-of-gravity velocity sideslip angle, yaw rate, and axial velocity are in a stable state, especially when the axial velocity is near the steady-state drift point, the vehicle can be considered to be in a steady-state drift state. It should be noted that the above current state parameters are illustrative; in practice, other parameters can be selected as the vehicle's state parameters based on different control requirements. This embodiment does not impose any restrictions on this.
[0083] by Figure 3 Based on a simplified model, the specific construction process of the vehicle state error model in this embodiment is as follows: For a vehicle with unknown parameters, this embodiment uses an open-loop drift initiation model to control it from a stationary state to a steady-state drift, and records the drift parameters. The flowchart of the open-loop drift initiation model is as follows: Figure 4 As shown, by adjusting the open-loop control parameters of the open-loop drift initiation model, the vehicle can be made to enter different steady-state drift states. Since each steady-state drift state corresponds to a set of drift parameters, multiple sets of drift parameters for the vehicle can be recorded. The recorded drift parameters include at least the axial velocity, steering wheel angle, and rear wheel driving force under different drift states, as well as the center of gravity velocity, sideslip angle, and yaw rate. Then, using the multiple sets of parameters recorded in the open-loop drift initiation model under different steady-state drift states—that is, several reference state parameters and several reference control parameters—a two-dimensional surface is constructed. The reference state parameters include the axial velocity, center of gravity velocity, sideslip angle, and yaw rate. Based on these parameters, a two-dimensional surface of state parameters can be constructed. The reference control parameters include the steering wheel angle and rear wheel driving force. Based on these parameters, a two-dimensional surface of control parameters can be constructed. The two parameter surfaces obtained in the above process are shown below. Figures 5-7 As shown. (Refer to...) Figure 5 As shown, Figure 5 For state parameter surfaces, Figure 5 In the figure, U_x_low represents the axial velocity, yaw_rate_low represents the yaw rate, and bata_low represents the center of mass velocity sideslip angle. The coordinate points in the figure represent the axial velocity, center of mass velocity sideslip angle, and yaw rate corresponding to different steady-state drift states. Figure 6 and Figure 7 All are control parameter surfaces. Figure 6 In this context, delta_low represents the steering wheel angle. Figure 7In this context, Fx_R_low represents the rear-wheel drive force. Finally, based on the constructed parametric two-dimensional surface, an arbitrary set of historical steady-state parameters and historical steady-state control parameters are determined. These parameters are then substituted into the following equation for solution: d(△X)=A*△X+B*△U, where △X is the parameter difference of the state parameters and △U is the parameter difference of the control parameters. Substituting these parameters into the equation yields A and B. It should be noted that A and B differ under different steady-state drift states; each steady-state drift state corresponds to a separate set of A and B. After obtaining the A and B model parameters, the vehicle state error model is complete.
[0084] Step S20: When the state deviation exceeds the preset range, construct an initial transition trajectory and obtain several sampling points from the initial transition trajectory.
[0085] It should be noted that when the detected state deviation between the vehicle and the target trajectory point on the desired trajectory exceeds a preset range, directly controlling the vehicle's drift trajectory at this time is difficult, making it hard to keep the vehicle on the drift equilibrium surface and greatly reducing control accuracy. In this situation, the approach adopted in this embodiment is to find a transition trajectory that satisfies the drift equilibrium state, allowing the vehicle to transition along the transition trajectory before controlling the vehicle according to the desired trajectory. The parameter value range corresponding to the preset range can be set according to control requirements, and the transition trajectory in this embodiment can be set as a circular arc trajectory; other types of trajectories can also be selected, and this embodiment does not impose any restrictions on them.
[0086] In the specific implementation, when constructing the target transition trajectory, the end point of the forward look time can be determined based on the set forward look time. After determining the initial transition trajectory, this embodiment further selects several sampling points from the initial transition trajectory. Based on these sampling points and the aforementioned end point of the forward look time, the transition trajectory can be corrected. The number of sampling points can be set according to actual needs, and this embodiment does not impose any restrictions on this.
[0087] Step S30: Adjust the initial transition trajectory based on several sampling points, substitute the state parameter difference between the vehicle and the target trajectory point into the vehicle state error model to calculate the control parameters, and pre-adjust the state of the vehicle according to the adjusted transition trajectory based on the control parameters.
[0088] In practical implementation, after determining several sampling points, this embodiment adjusts the initial transition trajectory based on these sampling points. This adjustment can be based on the error between the sampling points and the trajectory points on the desired trajectory, allowing the trajectory points on the transition trajectory to better match the trajectory points of the desired trajectory. The control parameters required for the vehicle to transition according to the adjusted trajectory can be calculated based on the difference in state parameters between the vehicle and the target trajectory points. Specifically, after obtaining the difference in state parameters between the vehicle and the target trajectory points, this difference is substituted into the vehicle state error model to calculate the control parameters. Controlling the vehicle according to these control parameters ensures that the vehicle remains in a drift equilibrium state after the transition.
[0089] Step S40: Obtain the current state parameters of the pre-adjusted vehicle, and determine the target state parameters based on the desired trajectory.
[0090] In practical implementation, if the state deviation between the pre-adjusted vehicle and the target trajectory point on the desired trajectory is within a preset range, the vehicle can be considered to have reached a drift equilibrium state and is near a steady-state point. In this case, subsequent drift trajectory control can be performed on the vehicle in this embodiment. When performing drift trajectory control on the vehicle, it is necessary to first obtain the current state parameters of the pre-adjusted vehicle and the target state parameters determined based on the desired trajectory. The target state parameters can be the state parameter values of the target trajectory point on the desired trajectory.
[0091] Step S50: Determine the parameter difference between the current state parameter and the target state parameter.
[0092] In specific implementation, after obtaining the above state parameters, this embodiment further needs to calculate the parameter difference between the current state parameters and the target state parameters. For example, if the yaw rate and the center of mass velocity and the sideslip angle do not change in the current state parameters and the preset state parameters, the only difference between the current state of the vehicle and the target trajectory point is the axial velocity of the vehicle. Assuming that the current axial velocity of the vehicle is 11 m / s and the axial velocity of the target trajectory point is 10 m / s, the parameter difference at this time can be calculated to be 1 m / s.
[0093] Step S60: Calculate the reference control parameters based on the parameter difference and the vehicle tracking error model.
[0094] It should be noted that the vehicle state error model mentioned above is used to control the vehicle in a steady-state drift state, while the vehicle tracking error model in this embodiment is used to control the drift trajectory of the vehicle in a steady-state drift state.
[0095] In this specific implementation, after calculating the parameter difference, the parameter difference can be substituted into the vehicle tracking error model to obtain the reference control parameters. Specifically, the parameter difference between the control parameters can be calculated based on the parameter difference of the state parameters, and the reference control parameters can be calculated based on the parameter difference between the control parameters.
[0096] Furthermore, in this embodiment, before constructing the vehicle tracking error model, it is necessary to first construct the tracking reference line corresponding to the vehicle, such as... Figure 8 L1 is shown.
[0097] In practical implementation, the lateral error e between the vehicle and the trajectory, as well as the vehicle's own velocity direction angle, can be determined based on this tracking reference line. With trajectory error From the lateral error e, we can also obtain the derivative of the lateral error, e_dot. Wherein, V = Ux / cos(β), V_dot = Ux_dot / cos(β).
[0098] It should be noted that, compared to the vehicle state error model, the vehicle tracking error model extends the state variables of the differential equation for tracking error. The constant 1 can compensate for the error caused by the tracking state. Based on these quantities, a vehicle tracking error model can be constructed, d(△X1)=A*△X1+B*△U, where the model parameters A and B are shown below.
[0099]
[0100]
[0101] Based on the extended differential equations described above, by substituting any set of errors between the vehicles and the desired trajectory, and combining this error with the state parameter differences and control parameter differences between the vehicles regressing from the current trajectory to the desired trajectory, model parameters A and B can be calculated to complete the construction of the vehicle tracking error model. It is important to emphasize that different errors correspond to different sets of model parameters AA and BB. In this embodiment, AA and BB are the same as A and B calculated in the vehicle state error model above. Each set of centroid velocity sideslip angle and yaw rate corresponds to a set of A and B. In the tracking part, AA and BB can be directly looked up based on the centroid velocity sideslip angle and yaw rate, but other parts of A and B need to be calculated in real time.
[0102] Furthermore, in this embodiment, the construction of the two-dimensional surface parameters involved in the steady-state drifting process of the controlled vehicle is the same as that described above. Both are constructed using variables such as longitudinal axis velocity, yaw angle, center of gravity velocity sideslip angle, steering wheel angle, and historical rear-wheel drive force. For example... Figures 9-11 As shown, the definitions of the parameters involved are the same as those mentioned above. Figures 5-7 Similarly, when controlling the vehicle to return to the desired trajectory, it can be done from... Figures 9-11 Find the corresponding status parameters and control parameters in the database.
[0103] Step S70: Control the vehicle to drift along the desired trajectory according to the reference control parameters.
[0104] In practice, the desired trajectory can be viewed as a curve composed of multiple trajectory points. The control parameters differ depending on the relative position of the vehicle to each trajectory point, and these parameters can be determined based on the error between the vehicle and the desired trajectory. After determining the reference control parameters, this embodiment controls the vehicle according to the steering wheel angle and rear-wheel drive force included in the reference control parameters, thus enabling the vehicle to drift along the desired trajectory.
[0105] Furthermore, to improve the control accuracy of the vehicle's trajectory during drifting, this embodiment also needs to query the desired tracking state that the vehicle can reach after a preset time. For example, the desired tracking state that the vehicle can reach at time T from the current time. The preset time can be obtained according to actual needs, and this embodiment does not impose any restrictions on it. After determining the desired tracking state that the vehicle can reach at the preset time, the tracking reference control parameters corresponding to the desired tracking state can be queried based on the desired tracking state. This is determined by the preset desired trajectory and can be set in advance. Then, the state ratio coefficient between the vehicle's current state and the desired tracking state is obtained. This state ratio coefficient can also be set in advance according to actual needs. Finally, the obtained state ratio coefficient is substituted into the following formula to calculate the target control parameter: U_des=α*Uo_des+(1-α)*Ut_des, where α is the state ratio coefficient, Uo_des is the reference control parameter, Ut_des is the tracking reference control parameter, and U_des is the target control parameter. After determining the target control parameters, in this embodiment, the vehicle is controlled according to the steering wheel angle and rear wheel drive force included in the target control parameters, so that the vehicle can drift along the desired trajectory.
[0106] This embodiment constructs an initial transition trajectory when the state deviation between the vehicle and the target trajectory point on the desired trajectory exceeds a preset range. The initial transition trajectory is adjusted based on several sampling points. Control parameters obtained from the state parameter difference between the vehicle and the target trajectory point are used to pre-adjust the vehicle's state according to the adjusted transition trajectory. Reference control parameters are calculated based on the parameter difference between the pre-adjusted vehicle's current state parameters and the target state parameters determined by the desired trajectory, combined with the vehicle's tracking error model. The vehicle is then controlled to drift along the desired trajectory according to these reference control parameters. This approach improves the accuracy of vehicle drift trajectory control by first transitioning the vehicle to a balanced state when its state deviates significantly from the trajectory matching point, and then controlling the vehicle according to the desired trajectory.
[0107] refer to Figure 12 , Figure 12 This is a flowchart illustrating a second embodiment of a vehicle drift trajectory control method according to the present invention.
[0108] Based on the first embodiment described above, the vehicle drift trajectory control method of this embodiment specifically includes the following in step S30:
[0109] Step S301: Determine the foresight time endpoint corresponding to the initial transition trajectory.
[0110] It is easy to understand that the construction of the initial transition trajectory requires the use of the target trajectory point and the forward time endpoint. Therefore, after the initial transition trajectory is constructed, the corresponding forward time endpoint can be found based on the initial transition trajectory in this embodiment.
[0111] Step S302: Obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the endpoint of the forward-looking time.
[0112] It should be noted that, in order to ensure that the planned trajectory points at the endpoint better match the reference trajectory points, and to increase the lateral matching error and velocity angle error at the endpoint, this embodiment will further obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the endpoint of the forward-looking time. The endpoint of the forward-looking time is as follows: Figure 13 Point A is shown in the diagram, and point B is the target trajectory point.
[0113] Step S303: Determine the trajectory points on the desired trajectory that correspond to each of the plurality of sampling points.
[0114] In practical implementation, after determining the initial transition trajectory, this embodiment can select several sampling points on the initial trajectory at preset intervals. Figure 13P1-P6 shown are the selected sampling points. The selection interval of the sampling points can be selected according to actual needs, and the number of sampling points can also be set by the user. This embodiment does not impose any restrictions on this. After determining a number of sampling points, a corresponding trajectory point can be found on the desired trajectory for each sampling point.
[0115] Step S304: Obtain the error between each sampling point and the corresponding trajectory point.
[0116] In specific implementation, this embodiment will further obtain the error between each sampling point and the corresponding trajectory point. The error includes, but is not limited to, speed error, yaw rate error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error.
[0117] Step S305: Adjust the initial transition trajectory based on the endpoint lateral error, the endpoint velocity angle error, and the error between each sampling point and the corresponding trajectory point.
[0118] In specific implementation, after obtaining the end-point lateral error, end-point velocity angle error, and the error between each sampling point and the corresponding trajectory point, this embodiment can use the vehicle's transition radius and the vehicle's desired sideslip angle as state variables to calculate using the following equation, for example: cost_total=w1*∫(U_x_cur(t)-f_v(radius,beta)) 2 dt+w2*∫(yawrate_cur(t)-f_r(radius,beta)) 2 dt+
[0119] w3*∫(fxr_cur(t)-f_fxr(radius,beta)) 2 dt+w4*∫(delta_cur(t)-f_delta(radius,beta)) 2 dt+w5*∫e(t) 2 dt+w6*∫(beta_cur(t)-beta)dt+w7*e(t_ahead) 2 +w8*v_heading_error(t_ahe ad) 2Where f_v(radius,beta) represents the steady-state quadratic surface equation of velocity, f_r(radius,beta) represents the quadratic surface equation of yaw rate, f_delta(radius,beta) represents the steady-state equation of front wheel steering angle, f_fxr(radius,beta) represents the equation of rear wheel driving force, U_x_cur(t) represents the velocity error, yawrate_cur(t) represents the yaw rate error, fxr_cur(t) represents the rear wheel driving force error, delta_cur(t) represents the front wheel steering angle error, e(t) represents the lateral error, beta_cur(t) represents the center of gravity sideslip angle error, radius represents the vehicle's transition radius, and beta represents the vehicle's desired sideslip angle. The above optimization equations can be used for iteration, meaning that the initial transition trajectory changes in real time. After each adjustment, several sampling points are obtained again from the adjusted transition trajectory, and the new error is then substituted into the above equations for calculation, which is used for the next iteration. Finally, the optimal transition trajectory can be obtained. Based on the optimal transition trajectory, the optimal vehicle transition radius and sideslip angle can be obtained.
[0120] This embodiment determines the forward-looking time endpoint corresponding to the initial transition trajectory; obtains the endpoint lateral error and endpoint velocity angle error between the vehicle and the forward-looking time endpoint; determines the trajectory points on the desired trajectory corresponding to each of the plurality of sampling points; obtains the errors between each sampling point and the corresponding trajectory point, the errors including at least velocity error, yaw rate error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error; adjusts the initial transition trajectory based on the endpoint lateral error, the endpoint velocity angle error, and the errors between each sampling point and the corresponding trajectory point, and optimizes the vehicle's transition trajectory through iteration, ensuring that the vehicle smoothly transitions to a drift balance state.
[0121] Furthermore, this embodiment of the invention also proposes a storage medium storing a vehicle drift trajectory control program, which, when executed by a processor, implements the steps of the vehicle drift trajectory control method described above.
[0122] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.
[0123] Reference Figure 14 , Figure 14 This is a structural block diagram of the first embodiment of the vehicle drift trajectory control device of the present invention.
[0124] like Figure 14As shown, the vehicle drift trajectory control device proposed in this embodiment of the invention includes:
[0125] The acquisition module 10 is used to acquire the state deviation between the vehicle and the target trajectory point on the desired trajectory.
[0126] The acquisition module 10 is further configured to construct an initial transition trajectory and acquire several sampling points from the initial transition trajectory when the state deviation exceeds a preset range.
[0127] The control module 20 is used to adjust the initial transition trajectory based on several sampling points, substitute the state parameter difference between the vehicle and the target trajectory point into the vehicle state error model to calculate control parameters, and pre-adjust the state of the vehicle according to the adjusted transition trajectory based on the control parameters.
[0128] The calculation module 30 is used to obtain the current state parameters of the pre-adjusted vehicle and determine the target state parameters based on the desired trajectory.
[0129] The calculation module 30 is also used to determine the parameter difference between the current state parameter and the target state parameter.
[0130] The calculation module 30 is also used to calculate reference control parameters based on the parameter difference and the vehicle tracking error model.
[0131] The control module 20 is also used to control the vehicle to drift along the desired trajectory according to the reference control parameters.
[0132] This embodiment constructs an initial transition trajectory when the state deviation between the vehicle and the target trajectory point on the desired trajectory exceeds a preset range. The initial transition trajectory is adjusted based on several sampling points. Control parameters obtained from the state parameter difference between the vehicle and the target trajectory point are used to pre-adjust the vehicle's state according to the adjusted transition trajectory. Reference control parameters are calculated based on the parameter difference between the pre-adjusted vehicle's current state parameters and the target state parameters determined by the desired trajectory, combined with the vehicle's tracking error model. The vehicle is then controlled to drift along the desired trajectory according to these reference control parameters. This approach improves the accuracy of vehicle drift trajectory control by first transitioning the vehicle to a balanced state when its state deviates significantly from the trajectory matching point, and then controlling the vehicle according to the desired trajectory.
[0133] In one embodiment, the control module 20 is further configured to: determine the forward-looking time endpoint corresponding to the initial transition trajectory; obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the forward-looking time endpoint; determine the trajectory points on the desired trajectory corresponding to each of the plurality of sampling points; obtain the errors between each sampling point and the corresponding trajectory point, the errors including at least velocity error, yaw rate error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error; and adjust the initial transition trajectory based on the endpoint lateral error, the endpoint velocity angle error, and the errors between each sampling point and the corresponding trajectory point.
[0134] In one embodiment, the control module 20 is further configured to acquire several sampling points from the adjusted transition trajectory; adjust the adjusted transition trajectory again based on the several sampling points; and pre-adjust the state of the vehicle according to the readjusted transition trajectory.
[0135] In one embodiment, the acquisition module 10 is further configured to acquire a preset forward look time; determine a forward look time endpoint based on the preset forward look time; and construct an initial transition trajectory based on the vehicle and the forward look time endpoint.
[0136] In one embodiment, the acquisition module 10 is further configured to acquire the current position of the vehicle; determine the target trajectory point closest to the vehicle on the desired trajectory based on the current position; calculate the state parameter difference between the vehicle and the trajectory point, and use the state parameter difference as a state deviation.
[0137] In one embodiment, the vehicle drift trajectory control device further includes: a construction module;
[0138] The construction module is used to construct the vehicle state error model corresponding to the vehicle;
[0139] The control module 20 is also used to adjust the vehicle to a steady-state drift state according to the vehicle state error model;
[0140] The acquisition module 10 is further configured to perform the step of determining the target state parameters based on the expected trajectory corresponding to the vehicle when the vehicle is in the steady-state drift state.
[0141] In one embodiment, the control module 20 is further configured to query the desired tracking state that the vehicle can reach after a preset time; determine the tracking reference control parameters corresponding to the preset time based on the desired tracking state; obtain the state ratio coefficient between the current state of the vehicle and the desired tracking state; calculate the target control parameters based on the state ratio coefficient, the reference control parameters, and the tracking reference control parameters; and control the vehicle to drift according to the desired trajectory according to the target control parameters.
[0142] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.
[0143] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.
[0144] In addition, for technical details not described in detail in this embodiment, please refer to the vehicle drift trajectory control method provided in any embodiment of the present invention, which will not be repeated here.
[0145] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0146] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0148] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A method for controlling vehicle drift trajectory, characterized in that, The vehicle drift trajectory control method includes: Obtain the state deviation between the vehicle and the target trajectory point on the desired trajectory; When the state deviation exceeds a preset range, an initial transition trajectory is constructed, and several sampling points are obtained from the initial transition trajectory. The initial transition trajectory is a transition trajectory that makes the vehicle meet the balance state. The initial transition trajectory is adjusted based on several sampling points. The difference in state parameters between the vehicle and the target trajectory point is substituted into the vehicle state error model to calculate control parameters. The state of the vehicle is then pre-adjusted according to the adjusted transition trajectory based on the control parameters. Obtain the current state parameters of the pre-adjusted vehicle, and determine the target state parameters based on the desired trajectory; Determine the parameter difference between the current state parameter and the target state parameter; Calculate reference control parameters based on the parameter differences and the vehicle tracking error model; The vehicle is controlled to drift along the desired trajectory based on the reference control parameters. The adjustment of the initial transition trajectory based on several sampling points includes: Determine the foresight time endpoint corresponding to the initial transition trajectory; Obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the endpoint of the forward-looking time; Determine the trajectory points on the desired trajectory that correspond to each of the plurality of sampling points; The error between each sampling point and the corresponding trajectory point is obtained, and the error includes at least the speed error, yaw speed error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error. The initial transition trajectory is adjusted based on the endpoint lateral error, the endpoint velocity angle error, and the error between each sampling point and its corresponding trajectory point.
2. The vehicle drift trajectory control method as described in claim 1, characterized in that, After pre-adjusting the vehicle's state according to the adjusted transition trajectory, the process further includes: Then, several sampling points are obtained from the adjusted transition trajectory; The adjusted transition trajectory is readjusted based on several sampling points, and the state of the vehicle is pre-adjusted according to the readjusted transition trajectory.
3. The vehicle drift trajectory control method as described in claim 1, characterized in that, The construction of the initial transition trajectory includes: Obtain the preset forward look time; The endpoint of the forward look-ahead time is determined based on the preset forward look-ahead time. An initial transition trajectory is constructed based on the vehicle and the forward-looking time endpoint.
4. The vehicle drift trajectory control method as described in claim 1, characterized in that, The acquisition of the state deviation between the vehicle and the target trajectory point on the desired trajectory includes: Get the vehicle's current location; Based on the current location, determine the target trajectory point on the desired trajectory that is closest to the vehicle; Calculate the state parameter difference between the vehicle and the trajectory point, and use the state parameter difference as the state deviation.
5. The vehicle drift trajectory control method as described in claim 1, characterized in that, Before obtaining the state deviation between the vehicle and the target trajectory point on the desired trajectory, the method further includes: Construct a vehicle state error model corresponding to the vehicle; The vehicle is adjusted to a steady-state drift state based on the vehicle state error model. When the vehicle is in the steady-state drift state, the step of obtaining the state deviation between the vehicle and the target trajectory point on the desired trajectory is performed.
6. The vehicle drift trajectory control method according to any one of claims 1 to 5, characterized in that, The step of controlling the vehicle to drift along the desired trajectory according to the reference control parameters includes: Query the desired tracking state that the vehicle can achieve after a preset time. Determine the tracking reference control parameters corresponding to the preset time based on the desired tracking state; Obtain the state ratio coefficient between the current state of the vehicle and the desired tracking state; Calculate the target control parameters based on the state proportional coefficient, the reference control parameters, and the tracking reference control parameters; The vehicle is controlled to drift along the desired trajectory according to the target control parameters.
7. A vehicle drift trajectory control device, characterized in that, The vehicle drift trajectory control device includes: The acquisition module is used to acquire the state deviation between the vehicle and the target trajectory point on the desired trajectory. The acquisition module is also used to construct an initial transition trajectory when the state deviation exceeds a preset range, and to acquire several sampling points from the initial transition trajectory, wherein the initial transition trajectory is a transition trajectory that enables the vehicle to meet the balance state. The control module is used to adjust the initial transition trajectory based on several sampling points, substitute the state parameter difference between the vehicle and the target trajectory point into the vehicle state error model to calculate control parameters, and pre-adjust the state of the vehicle according to the adjusted transition trajectory based on the control parameters. The calculation module is used to obtain the current state parameters of the pre-adjusted vehicle and determine the target state parameters based on the desired trajectory; The calculation module is also used to determine the parameter difference between the current state parameter and the target state parameter; The calculation module is also used to calculate reference control parameters based on the parameter difference and the vehicle tracking error model; The control module is also used to control the vehicle to drift according to the desired trajectory based on the reference control parameters; The control module is further configured to: determine the forward-looking time endpoint corresponding to the initial transition trajectory; obtain the endpoint lateral error and endpoint velocity angle error between the vehicle and the forward-looking time endpoint; determine the trajectory points on the desired trajectory corresponding to each of the plurality of sampling points; obtain the errors between each sampling point and the corresponding trajectory point, the errors including at least velocity error, yaw rate error, rear wheel drive force error, front wheel steering angle error, lateral error, and center of gravity sideslip angle error; and adjust the initial transition trajectory based on the endpoint lateral error, the endpoint velocity angle error, and the errors between each sampling point and the corresponding trajectory point.
8. A vehicle drift trajectory control device, characterized in that, The vehicle drift trajectory control device includes: a memory, a processor, and a vehicle drift trajectory control program stored in the memory and running on the processor, the vehicle drift trajectory control program being configured to implement the vehicle drift trajectory control method as described in any one of claims 1 to 6.
9. A storage medium, characterized in that, The storage medium stores a vehicle drift trajectory control program, which, when executed by a processor, implements the vehicle drift trajectory control method as described in any one of claims 1 to 6.