A vehicle drift control method, device, equipment and medium

Abstract

The application relates to the technical field of intelligent driving, and discloses a vehicle drift control method, device, equipment and medium, wherein the method comprises the following steps: firstly, determining a road adhesion coefficient and a drift target parameter based on current lateral and longitudinal accelerations of a vehicle; comparing the current drift parameter with the drift target parameter to determine whether the vehicle enters a drift state; looking up an initial target slip rate and an initial torque by combining the road adhesion coefficient and an accelerator pedal opening degree; and correcting the initial target slip rate and the initial drift target yaw rate in real time by a dynamic adjustment model. Further, a yaw error between a target yaw rate and an actual yaw rate, and a slip error between a target slip rate and an actual slip rate are calculated, and finally, a target torque is comprehensively calculated based on the two errors, so that the driving torque is transitioned from an initial value to a target value. The technical scheme provided by the application can solve the contradiction between slip control and yaw control in the drift process.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a vehicle drift control method, device, equipment and medium. Background Technology

[0002] Even with increasingly sophisticated electronic control systems, drifting still represents the pure driving pleasure of human drivers actively challenging the laws of physics and pursuing perfect harmony between driver and vehicle.

[0003] Existing drift control schemes suffer from design flaws in the control dimension and introduce additional interference to the driver's steering wheel operation. Specifically, the two core control objectives, yaw rate and slip ratio, inherently conflict at the dynamic level. For instance, under conditions of high slip ratio and low yaw rate, slip ratio control requires reducing torque to restore traction, while yaw rate control needs to increase torque to maintain steering response. If a traditional PID parallel control strategy is adopted, this conflicting objective will directly lead to oscillations in the system's output torque command, affecting control stability and execution smoothness.

[0004] Therefore, resolving the contradiction between slip control and yaw control during drifting has become a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a vehicle drift control method, device, equipment, and medium, which solves the contradictory technical problem of slip control and yaw control during drifting.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include:

[0007] Firstly, embodiments of this application provide a vehicle drift control method. The method includes:

[0008] Based on the vehicle's current lateral acceleration and current longitudinal acceleration, the road adhesion coefficient and drift target parameters are determined; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration;

[0009] The vehicle's drift state is determined based on its current drift parameters and drift target parameters. If the determination result indicates that the vehicle is in a drift state, the current accelerator pedal opening and current slip ratio are obtained.

[0010] Based on the road surface adhesion coefficient and the current accelerator pedal opening, the initial target slip ratio and initial torque are determined, and the initial target slip ratio and the initial drift target yaw rate are adjusted by a pre-built dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate.

[0011] The yaw error between the target yaw rate and the current yaw rate in the current drift parameters is determined, and the drift error between the target slip ratio and the current slip ratio is determined. Based on the yaw error and the drift error, a target torque is determined to control the vehicle to convert from the initial torque to the target torque.

[0012] This embodiment provides a vehicle drift control method. Based on the vehicle's current lateral and longitudinal acceleration, it first determines the road adhesion coefficient and drift target parameters. Then, by comparing the current drift parameters with the drift target parameters, it determines whether the vehicle has entered a drift state. If a drift state is determined, the current accelerator pedal opening and current slip ratio are collected in real time. Combining the road adhesion coefficient and accelerator pedal opening, the initial target slip ratio and initial torque are obtained from a table. A dynamic adjustment model then corrects the initial target slip ratio and initial drift target yaw rate in real time, outputting the adjusted target slip ratio and target yaw rate. Further calculations are made of the yaw error between the target yaw rate and the actual yaw rate, and the slip error between the target slip ratio and the actual slip ratio. Finally, the target torque is calculated based on these two errors, thereby controlling the drive torque to transition from the initial value to the target value. During this process, the dynamic adjustment model ensures that the deviation between the actual slip ratio and the actual yaw rate remains within a reasonable range by adjusting the target slip ratio and the target yaw rate. The resulting yaw and slip errors are effectively constrained, thus avoiding target conflicts between slip control and yaw control. This embodiment directly calculates and simultaneously optimizes the target slip ratio and target yaw rate based on the vehicle state, without introducing other intermediate control variables, and features low response latency and high control accuracy. Furthermore, this embodiment does not actively intervene in the driver's steering input, but adjusts the drive torque in real time based on the dynamic changes in the steering wheel angle. This method improves operational intuitiveness and convenience while maintaining the driver's control over vehicle steering, thus balancing control efficiency and driving pleasure.

[0013] In one embodiment, determining the road adhesion coefficient and drift target parameters based on the vehicle's current lateral and longitudinal acceleration includes:

[0014] The road adhesion coefficient is determined based on the vehicle's current lateral and longitudinal accelerations.

[0015] Based on the road surface adhesion coefficient, determine the initial drift target yaw rate and initial drift target lateral acceleration that match the current vehicle speed.

[0016] This embodiment can estimate the current road surface adhesion coefficient by acquiring the vehicle's real-time lateral and longitudinal acceleration. Based on the current road surface adhesion coefficient and real-time vehicle speed, it queries the internally calibrated mapping table to determine the matching initial drift target yaw rate and target lateral acceleration, thereby providing key target setting values ​​for subsequent control and judgment of whether the vehicle has entered a drift state.

[0017] In one implementation, determining the vehicle's drift state based on the vehicle's current drift parameters and the drift target parameters, and if the determination result indicates that the vehicle is in a drift state, obtaining the current accelerator pedal opening and the current slip ratio, includes:

[0018] Determine the first error between the current yaw rate in the current drift parameters and the initial drift target yaw rate;

[0019] Determine a second error between the current lateral acceleration in the current drift parameters and the initial drift target lateral acceleration;

[0020] The first error and the second error are judged. If the judgment result is that the vehicle is in a drift state, the current accelerator pedal opening and the current slip ratio are obtained.

[0021] This embodiment performs real-time evaluation of vehicle dynamics by calculating a first error between the current yaw rate and the initial drift target yaw rate, and a second error between the current lateral acceleration and the initial drift target lateral acceleration. If both the first and second errors exceed preset thresholds, the vehicle is determined to have entered a drift state. Subsequently, the current accelerator pedal opening and current slip ratio are simultaneously collected as key inputs for subsequent control decisions. By evaluating and judging parameters such as vehicle yaw rate and lateral acceleration in real time, accurate identification of the vehicle's operating posture is achieved. It can also effectively distinguish between different states such as normal driving, extreme cornering, and uncontrolled drifting, thereby avoiding unintended interventions due to misjudgment (such as activating or suppressing torque at inappropriate times), fundamentally eliminating abnormal situations such as driving jerks, trajectory interference, or power interruption that may be caused by erroneous triggering. This not only ensures the predictability of vehicle behavior and driving safety, but also ensures a smooth and controllable driving process, ultimately providing users with a stable and consistent driving experience.

[0022] In one implementation, the dynamic adjustment model is constructed in the following manner:

[0023] The first coupling factor is determined based on the current yaw rate in the current drift parameters and the initial target slip ratio;

[0024] The second coupling factor is determined based on the initial drift target yaw rate and the current slip rate;

[0025] A dynamic adjustment model is constructed based on the ratio between the first coupling factor and the second coupling factor.

[0026] This embodiment constructs a dynamic adjustment model by calculating a first coupling factor and a second coupling factor, and then using the ratio between them. This model utilizes the balance between the lateral and longitudinal dynamic states represented by the ratio to dynamically coordinate the weight distribution between slip control and yaw control, thereby enabling them to work synergistically and effectively avoid dynamic instability caused by conflicting control objectives.

[0027] In one optional embodiment, the dynamic adjustment model is adjusted as follows:

[0028] If the coupling factor obtained through the dynamic adjustment model is greater than a preset threshold, the initial target slip ratio is adjusted to obtain the adjusted target slip ratio.

[0029] If the coupling factor obtained through the dynamic adjustment model is less than a preset threshold, the initial drift target yaw rate is adjusted to obtain the adjusted target yaw rate.

[0030] This embodiment calculates the coupling factor through a dynamic adjustment model. If the coupling factor is greater than a preset threshold, it indicates that the coupling effect of yaw motion on longitudinal slip in the current dynamic state has exceeded expectations. In this case, the initial target slip ratio will be dynamically corrected based on the coupling factor, and the adjusted target slip ratio will be output to suppress control conflicts caused by excessive coupling. Conversely, if the coupling factor is less than the preset threshold, it indicates that the coupling effect between the target yaw motion and the current slip state has not reached the expected strength. The initial drift target yaw rate will be adjusted accordingly to enhance the lateral dynamic response, and the coordinated target yaw rate will be output. This conditional judgment mechanism achieves coordinated optimization of slip and yaw control targets by sensing and responding to changes in the coupling state in real time, thereby ensuring that the vehicle maintains controllable and stable dynamic balance during drifting while avoiding dynamic conflicts.

[0031] In one alternative embodiment, determining the target torque based on the yaw error and the drift error includes:

[0032] Based on the yaw error, a yaw torque matching the current vehicle speed is determined;

[0033] Based on the drift error, a slip torque that matches the current speed of the vehicle is determined;

[0034] The target torque is determined based on the combination of the yaw torque and the slip torque.

[0035] This embodiment calculates the yaw torque by measuring the error between the target yaw rate and the actual value using a set of PI parameters. Simultaneously, based on the error between the target slip ratio and the actual slip ratio, the slip torque is calculated using another set of PI parameters. Finally, the two are added together with specific weights to form the target torque. This method achieves coordinated control of lateral rotation and longitudinal slip during drifting, effectively resolving the dynamic conflicts that may arise from a single control target, thereby ensuring the stability and controllability of the vehicle under extreme conditions.

[0036] In one alternative embodiment, determining the target torque based on the combination of the yaw torque and the slip torque includes:

[0037] Determine the yaw torque weighting coefficient that matches the current slip ratio;

[0038] Determine a slip torque weighting coefficient that matches the current steering wheel angle of the vehicle;

[0039] Configure the yaw torque weighting coefficient for the yaw torque, and configure the slip torque weighting coefficient for the slip torque;

[0040] The target torque is determined by summing the yaw torque after configuring the yaw torque weighting coefficient and the slip torque after configuring the slip torque weighting coefficient.

[0041] This embodiment uses a weighted fusion method to dynamically coordinate the torque contribution ratio of yaw control and slip control according to the actual vehicle state, thereby maintaining a suitable slip level while achieving the desired yaw motion, ensuring the vehicle's attitude stability and dynamic coordination during drifting.

[0042] Secondly, embodiments of this application provide a vehicle drift control device, the device comprising:

[0043] The parameter determination unit is used to determine the road adhesion coefficient and drift target parameters based on the vehicle's current lateral acceleration and current longitudinal acceleration; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration;

[0044] The state determination unit is used to determine the drift state of the vehicle based on the current drift parameters and the drift target parameters. If the determination result is that the vehicle is in a drift state, the unit obtains the current accelerator pedal opening and the current slip ratio.

[0045] The target adjustment unit is used to determine the initial target slip ratio and initial torque based on the road surface adhesion coefficient and the current accelerator pedal opening, and adjust the initial target slip ratio and the initial drift target yaw rate through a pre-built dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate.

[0046] The torque determination unit is used to determine the yaw error between the target yaw rate and the current yaw rate in the current drift parameters, and to determine the drift error between the target slip ratio and the current slip ratio. Based on the yaw error and the drift error, the unit determines the target torque to control the vehicle to convert from the initial torque to the target torque.

[0047] Thirdly, embodiments of this application provide a computer device, including:

[0048] The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the vehicle drift control method described above.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the vehicle drift control method described above. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 A flowchart of a vehicle drift control method provided in an embodiment of this application;

[0052] Figure 2 A flowchart of step S11 provided in an embodiment of this application;

[0053] Figure 3 A flowchart of step S31 provided in an embodiment of this application;

[0054] Figure 4 A flowchart of step S51 provided in an embodiment of this application;

[0055] Figure 5 A flowchart of step S71 provided in an embodiment of this application;

[0056] Figure 6 A flowchart of step S751 provided in an embodiment of this application;

[0057] Figure 7 A vehicle drift control device provided in this application embodiment;

[0058] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Vehicle drift control schemes are typically based on the following framework: First, the system calculates the desired target yaw rate based on the driver's real-time steering wheel angle and vehicle speed. Then, it acquires the vehicle's actual yaw rate using onboard sensors (such as yaw rate sensors) and compares this measured value with the target value in real time to obtain the yaw rate deviation. Based on this deviation signal, the controller dynamically adjusts the drive torque distribution between the front and rear axles and applies necessary steering compensation torque to achieve and maintain a stable and controllable vehicle drift posture.

[0061] However, this classic control architecture has gradually revealed its inherent limitations in in-depth applications, with the core problem being the coupling and interference in the control dimension. Specifically, when pursuing stable drift, the system needs to coordinate two key and coupled dynamic variables simultaneously: yaw rate and tire slip ratio. These two control objectives are fundamentally conflicting at the physical level. For example, when the vehicle is in a condition of high slip ratio and low yaw rate, the slip ratio control logic requires a reduction in drive torque to restore tire grip and prevent excessive slippage; while at the same time, to stimulate or maintain the vehicle's rotational motion around the vertical axis and achieve the target yaw state, the yaw rate control logic requires an increase in drive torque. When a traditional PID controller is used to control these two objectives independently and in parallel, the above-mentioned objective conflict will directly translate into contradictory torque commands, resulting in significant oscillations in the final torque output. This oscillation not only disrupts the smoothness and controllability of the vehicle's dynamic response but also interferes with the driver's steering intentions, affecting the overall stability of the system and the driving experience.

[0062] In summary, resolving the contradiction between slip control and yaw control during drifting has become a technical problem that needs to be solved.

[0063] To address the aforementioned technical problems, an embodiment of a vehicle drift control method is provided according to an embodiment of this application. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0064] This embodiment provides a vehicle drift control method. Figure 1 A flowchart of a vehicle drift control method provided in this application embodiment is shown below. Figure 1 As shown, the process includes the following steps:

[0065] Step S1: Based on the vehicle's current lateral acceleration and current longitudinal acceleration, determine the road adhesion coefficient and drift target parameters; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration.

[0066] Specifically, the vehicle uses an IMU (Inertial Measurement Unit) installed on its body, combined with signals from wheel speed sensors and steering wheel angle, to calculate in real time the lateral and longitudinal acceleration in the vehicle coordinate system through coordinate transformation, gravity compensation, and data fusion algorithms (such as Kalman filtering). Longitudinal acceleration represents the vehicle's acceleration capability during forward movement or braking; it is positive during acceleration and negative during braking. Lateral acceleration represents the lateral acceleration required for cornering, typically related to the vehicle's steering angle and speed. Based on this dynamic information, the current road surface adhesion coefficient is further estimated. The road surface adhesion coefficient is an important indicator of the friction between the tires and the road surface, affecting the vehicle's braking, acceleration, and steering capabilities during driving. Initial drift target values, including target yaw rate and target lateral acceleration, are determined based on an internally pre-calibrated mapping table. This mapping table is derived from empirical rules accumulated through extensive simulations and real-vehicle testing; for example, the range of yaw rates required to maintain a stable drift posture under specific vehicle speed and road surface conditions has been pre-calibrated. These initial target values ​​are quickly obtained by looking up tables, and based on these values, the power, braking and steering are coordinated and controlled to achieve and stabilize the vehicle's drift state.

[0067] Step S3: Determine the vehicle's drift state based on the vehicle's current drift parameters and drift target parameters. If the determination result indicates that the vehicle is in a drift state, obtain the current accelerator pedal opening and the current slip ratio.

[0068] Specifically, by comparing the vehicle's current drift parameters (such as current yaw rate, vehicle speed, and steering wheel angle) with the target drift parameters, the vehicle's operating posture and state are accurately identified and judged. This judgment mechanism is used to distinguish between normal driving and drifting conditions, thereby effectively avoiding false triggering and ensuring the safety and stability of basic driving. Once it is confirmed that the vehicle has entered a stable drifting state, two key control inputs will be collected in real time: the current accelerator pedal opening, which represents the driver's power request intention; and the current slip ratio, which serves as feedback reflecting the degree of rear wheel slip.

[0069] Step S5: Based on the road surface adhesion coefficient and the current accelerator pedal opening, determine the initial target slip ratio and initial torque, and adjust the initial target slip ratio and initial drift target yaw rate through a pre-built dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate.

[0070] Specifically, based on the real-time collected road surface adhesion coefficient and the current accelerator pedal opening, the initial target slip ratio and initial torque are first determined. Then, through a pre-built dynamic adjustment model, the initial target slip ratio and initial drift target yaw rate are optimized and adjusted in real time, ultimately outputting the adjusted target slip ratio and target yaw rate suitable for the current vehicle dynamics. Specifically, the initial target slip ratio and initial torque are determined first by real-time vehicle speed, accelerator pedal opening, and road surface adhesion coefficient to determine the corresponding control targets (target slip ratio and initial torque). In detail, based on the real-time sensed adhesion coefficient and accelerator pedal opening, the initial target slip ratio and initial drive torque corresponding to the current vehicle speed are retrieved through a pre-calibrated data mapping table, providing benchmark parameters for subsequent stability control. Based on the current adhesion coefficient and accelerator pedal opening, the initial target slip ratio and initial torque at this vehicle speed are obtained by looking up a table (calibrated and confirmed).

[0071] Step S7: Determine the yaw error between the target yaw rate and the current yaw rate in the current drift parameters, and determine the drift error between the target slip ratio and the current slip ratio. Based on the yaw error and the drift error, determine the target torque to control the vehicle to transform from the initial torque to the target torque.

[0072] Specifically, the target torque is dynamically determined by calculating two key control errors: First, the yaw error is calculated based on the preset target yaw rate and the real-time acquired current yaw rate. This error reflects the deviation between the vehicle's actual yaw motion and the desired posture. Second, the drift error is calculated based on the set target slip ratio and the real-time estimated current slip ratio. This error characterizes the difference between the drive wheel drift state and the ideal drift level. Finally, based on the weighted sum of the yaw error and the drift error, the required target torque is calculated in real time using a preset control algorithm (such as PID or model predictive control) to achieve precise closed-loop adjustment of the vehicle's drift posture.

[0073] This embodiment provides a vehicle drift control method. Based on the vehicle's current lateral and longitudinal acceleration, it first determines the road adhesion coefficient and drift target parameters. Then, by comparing the current drift parameters with the drift target parameters, it determines whether the vehicle has entered a drift state. If a drift state is determined, the current accelerator pedal opening and current slip ratio are collected in real time. Combining the road adhesion coefficient and accelerator pedal opening, the initial target slip ratio and initial torque are obtained from a table. A dynamic adjustment model then corrects the initial target slip ratio and initial drift target yaw rate in real time, outputting the adjusted target slip ratio and target yaw rate. Further calculations are made of the yaw error between the target yaw rate and the actual yaw rate, and the slip error between the target slip ratio and the actual slip ratio. Finally, the target torque is calculated based on these two errors, thereby controlling the drive torque to transition from the initial value to the target value. During this process, the dynamic adjustment model ensures that the deviation between the actual slip ratio and the actual yaw rate remains within a reasonable range by adjusting the target slip ratio and the target yaw rate. The resulting yaw and slip errors are effectively constrained, thus avoiding target conflicts between slip control and yaw control. This embodiment directly calculates and simultaneously optimizes the target slip ratio and target yaw rate based on the vehicle state, without introducing other intermediate control variables, and features low response latency and high control accuracy. Furthermore, this embodiment does not actively intervene in the driver's steering input, but adjusts the drive torque in real time based on the dynamic changes in the steering wheel angle. This method improves operational intuitiveness and convenience while maintaining the driver's control over vehicle steering, thus balancing control efficiency and driving pleasure.

[0074] Figure 2 The flowchart for step S1 provided in the embodiments of this application may include the following steps:

[0075] Step S11: Determine the road adhesion coefficient based on the vehicle's current lateral acceleration and current longitudinal acceleration.

[0076] Specifically, the road adhesion coefficient is calculated in real time based on the vehicle's lateral and longitudinal acceleration. :

[0077]

[0078] in, The road surface adhesion coefficient; It is longitudinal acceleration; It is lateral acceleration; This is the acceleration due to gravity.

[0079] For example, asphalt roads typically have a high coefficient of friction, making them suitable for fast driving and sharp turns. When driving on such surfaces, vehicles can apply acceleration and steering more aggressively while still maintaining control. Ice, on the other hand, has a low coefficient of friction, making it easy for vehicles to lose traction. In this situation, even slight acceleration or steering can cause the vehicle to skid. Therefore, by calculating the coefficient of friction in real time, vehicles can implement flexible control strategies under different road conditions, thus safely performing drift maneuvers.

[0080] Step S13: Based on the road surface adhesion coefficient, determine the initial drift target yaw rate and initial drift target lateral acceleration that match the vehicle's current speed.

[0081] Specifically, based on the current vehicle speed and road surface adhesion coefficient, the initial drift target yaw rate and initial drift target lateral acceleration at this speed are obtained by looking up a table. This table defines the dynamic indicators of the initial drift target yaw rate and initial drift target lateral acceleration required to achieve stable and controllable drift at different speeds and adhesion levels, providing key target setpoints for subsequent control and determining whether the vehicle has entered a drift state.

[0082] This embodiment can calculate the current road surface adhesion coefficient by acquiring the vehicle's real-time lateral and longitudinal acceleration. Based on the current road surface adhesion coefficient and real-time vehicle speed, it queries the internally calibrated mapping table to determine the matching initial drift target yaw rate and target lateral acceleration, thereby providing key target setting values ​​for subsequent control and judgment of whether the vehicle has entered a drift state.

[0083] Figure 3 The flowchart for step S3 provided in the embodiments of this application may include the following steps:

[0084] Step S31: Determine the first error between the current yaw rate and the initial drift target yaw rate in the current drift parameters.

[0085] Specifically, by calculating the difference between the currently measured yaw rate and the preset initial drift target yaw rate, a first error (i.e., yaw rate error) characterizing the magnitude of the deviation between the two is obtained. This error reflects the degree of deviation between the vehicle's actual yaw motion state and the desired drift posture in the angular velocity dimension.

[0086] Step S33: Determine the second error between the current lateral acceleration and the initial drift target lateral acceleration in the current drift parameters.

[0087] Specifically, by calculating the difference between the currently measured lateral acceleration and the preset initial drift target lateral acceleration, a second error (i.e., lateral acceleration error) characterizing the magnitude of the deviation between the two is obtained. This error reflects the degree of deviation between the vehicle's actual lateral dynamic response and the desired drift posture in the lateral force dimension.

[0088] Step S35: Judge the first error and the second error. If the judgment result is that the vehicle is in a drift state, obtain the current accelerator pedal opening and the current slip ratio.

[0089] Specifically, the first error (yaw rate error) and the second error (lateral acceleration error) are compared with preset calibration thresholds. The vehicle is determined to have entered a drift state consistent with control expectations only if both errors simultaneously exceed their respective thresholds, and the subsequent drift control algorithm is activated accordingly. At the same time, the current throttle pedal opening and the current slip ratio are acquired.

[0090] This embodiment performs real-time evaluation of vehicle dynamics by calculating a first error between the current yaw rate and the initial drift target yaw rate, and a second error between the current lateral acceleration and the initial drift target lateral acceleration. If both the first and second errors exceed preset thresholds, the vehicle is determined to have entered a drift state. Subsequently, the current accelerator pedal opening and current slip ratio are simultaneously collected as key inputs for subsequent control decisions. By evaluating and judging parameters such as vehicle yaw rate and lateral acceleration in real time, accurate identification of the vehicle's operating posture is achieved. It can also effectively distinguish between different states such as normal driving, extreme cornering, and uncontrolled drifting, thereby avoiding unintended interventions due to misjudgment (such as activating or suppressing torque at inappropriate times), fundamentally eliminating abnormal situations such as driving jerks, trajectory interference, or power interruption that may be caused by erroneous triggering. This not only ensures the predictability of vehicle behavior and driving safety, but also ensures a smooth and controllable driving process, ultimately providing users with a stable and consistent driving experience.

[0091] Figure 4 A flowchart illustrating the method for constructing a dynamic adjustment model provided in this application embodiment, the process may include the following steps:

[0092] Step S51: Determine the first coupling factor based on the current yaw rate and the initial target slip ratio in the current drift parameters.

[0093] Specifically, the first coupling factor is determined by multiplying the current yaw rate and the initial target slip ratio. The first coupling factor, in a physical sense, reflects the instantaneous interaction strength between the vehicle's yaw motion state and the expected slip level. As a key coupling variable in the dynamic adjustment model that coordinates longitudinal slip and lateral yaw control, it is used to quantify the degree of mutual influence between the two in real time.

[0094] Step S53: Determine the second coupling factor based on the initial drift target yaw rate and the current slip rate.

[0095] Specifically, a second coupling factor is determined using the initial drift target yaw rate and the current slip rate. This second coupling factor, in a physical sense, reflects the dynamic coupling relationship between the yaw intensity and the actual slip state. As another key coupling variable coordinating the initial drift target yaw rate and the current slip rate, it quantifies the degree of mutual influence between the initial drift target yaw rate and the current slip rate.

[0096] Step S55: Based on the ratio between the first coupling factor and the second coupling factor, a dynamic adjustment model is constructed.

[0097] Specifically, the coupling factor = (current yaw rate × initial target slip ratio) / (initial drifting target yaw rate × current slip ratio):

[0098]

[0099] in, The current yaw rate, The initial target slip ratio, The initial drift target's yaw rate, This represents the current slip ratio.

[0100] This embodiment constructs a dynamic adjustment model by calculating a first coupling factor and a second coupling factor, and then using the ratio between them. This model utilizes the balance between the lateral and longitudinal dynamic states represented by the ratio to dynamically coordinate the weight distribution between slip control and yaw control, thereby enabling them to work synergistically and effectively avoid dynamic instability caused by conflicting control objectives.

[0101] In one alternative embodiment, the dynamic adjustment model is adjusted as follows:

[0102] If the coupling factor obtained through the dynamic adjustment model is greater than the preset threshold, the initial target slip ratio is adjusted to obtain the adjusted target slip ratio.

[0103] If the coupling factor obtained through the dynamic adjustment model is less than the preset threshold, the initial drift target yaw rate is adjusted to obtain the adjusted target yaw rate.

[0104] This embodiment calculates the coupling factor through a dynamic adjustment model. If the coupling factor is greater than a preset threshold, it indicates that the coupling effect of yaw motion on longitudinal slip in the current dynamic state has exceeded expectations. In this case, the initial target slip ratio will be dynamically corrected based on the coupling factor, and the adjusted target slip ratio will be output to suppress control conflicts caused by excessive coupling. Conversely, if the coupling factor is less than the preset threshold, it indicates that the coupling effect between the target yaw motion and the current slip state has not reached the expected strength. The initial drift target yaw rate will be adjusted accordingly to enhance the lateral dynamic response, and the coordinated target yaw rate will be output. This conditional judgment mechanism achieves coordinated optimization of slip and yaw control targets by sensing and responding to changes in the coupling state in real time, thereby ensuring that the vehicle maintains controllable and stable dynamic balance during drifting while avoiding dynamic conflicts.

[0105] Figure 5 The flowchart for determining the target torque provided in this application embodiment may include the following steps:

[0106] Step S71: Based on the yaw error, determine the yaw torque that matches the vehicle's current speed.

[0107] Specifically, calculate the yaw error:

[0108]

[0109] in, For the target yaw rate, This represents the current yaw rate.

[0110] Yaw torque:

[0111] ,

[0112] in, The integral of the yaw error (accumulated in each control cycle); The P-term coefficient for yaw control (determined based on calibration); The I-term coefficient for yaw control (determined based on calibration).

[0113] Step S73: Based on the drift error, determine the slip torque that matches the vehicle's current speed.

[0114] Specifically, the drift error is calculated:

[0115]

[0116] in, For the target slip ratio, This represents the current slip ratio.

[0117] Slip torque:

[0118]

[0119] in, This is the integral value of the slip ratio error (accumulated over each control cycle). The P-term coefficient for slip control (determined based on calibration); The coefficient for slip control is I (determined based on calibration).

[0120] Step S75: Determine the target torque based on the combination of yaw torque and slip torque.

[0121] This embodiment calculates the yaw torque by measuring the error between the target yaw rate and the actual value using a set of PI parameters. Simultaneously, based on the error between the target slip ratio and the actual slip ratio, the slip torque is calculated using another set of PI parameters. Finally, the two are added together with specific weights to form the target torque. This method achieves coordinated control of lateral rotation and longitudinal slip during drifting, effectively resolving the dynamic conflicts that may arise from a single control target, thereby ensuring the stability and controllability of the vehicle under extreme conditions.

[0122] Figure 6 The flowchart of S75 provided in the embodiments of this application may include the following steps:

[0123] Step S751: Determine the yaw torque weighting coefficient that matches the current slip ratio.

[0124] Specifically, the yaw torque weighting coefficient K1 is obtained by looking up a table (calibrated and confirmed) based on the current slip ratio. This table lists the yaw torque weighting coefficients (K1) corresponding to various slip ratios.

[0125] Step S753: Determine the slip torque weighting coefficient that matches the current steering wheel angle of the vehicle.

[0126] Specifically, the slip torque weighting coefficient K2 is obtained by referring to a table (calibrated and confirmed) based on the driver's steering wheel angle. This table lists the slip torque weighting coefficients (K2) corresponding to various steering wheel angles.

[0127] Step S755: Configure yaw torque weighting coefficient for yaw torque and slip torque weighting coefficient for slip torque.

[0128] Step S757: Sum the yaw torque after configuring the yaw torque weighting coefficient and the slip torque after configuring the slip torque weighting coefficient to determine the target torque.

[0129] Specifically, determine the target torque:

[0130] Target torque = yaw torque × K1 + slip torque × K2

[0131] This embodiment uses a weighted fusion method to dynamically coordinate the torque contribution ratio of yaw control and slip control according to the actual vehicle state, thereby maintaining a suitable slip level while achieving the desired yaw motion, ensuring the vehicle's attitude stability and dynamic coordination during drifting.

[0132] Accordingly, please refer to Figure 7 This application provides a block diagram of a vehicle drift control device, which includes:

[0133] The parameter determination unit 101 is used to determine the road adhesion coefficient and drift target parameters based on the vehicle's current lateral acceleration and current longitudinal acceleration; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration.

[0134] The state judgment unit 103 is used to judge the drift state of the vehicle based on the current drift parameters and drift target parameters of the vehicle. If the judgment result is that the vehicle is in a drift state, the current accelerator pedal opening and the current slip ratio are obtained.

[0135] The target adjustment unit 105 is used to determine the initial target slip ratio and initial torque based on the road surface adhesion coefficient and the current accelerator pedal opening, and adjust the initial target slip ratio and initial drift target yaw rate through a pre-built dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate.

[0136] The torque determination unit 107 is used to determine the yaw error between the target yaw rate and the current yaw rate in the current drift parameters, and to determine the drift error between the target slip ratio and the current slip ratio. Based on the yaw error and the drift error, the target torque is determined to control the vehicle to convert from the initial torque to the target torque.

[0137] In some optional implementations, the parameter determination unit 101 is as follows:

[0138] The road adhesion coefficient is determined based on the vehicle's current lateral and longitudinal accelerations.

[0139] Based on the road surface adhesion coefficient, determine the initial drift target yaw rate and initial drift target lateral acceleration that match the vehicle's current speed.

[0140] In some optional implementations, the state determination unit 103 is as follows:

[0141] Determine the first error between the current yaw rate in the current drift parameters and the initial drift target yaw rate.

[0142] Determine the second error between the current lateral acceleration in the current drift parameters and the initial drift target lateral acceleration.

[0143] The first error and the second error are judged. If the judgment result is that the vehicle is in a drift state, the current accelerator pedal opening and the current slip ratio are obtained.

[0144] In some alternative implementations, the dynamic adjustment model is constructed as follows:

[0145] The first coupling factor is determined based on the current yaw rate and the initial target slip ratio in the current drift parameters.

[0146] The second coupling factor is determined based on the initial drift target yaw rate and the current slip rate.

[0147] A dynamic adjustment model is constructed based on the ratio between the first coupling factor and the second coupling factor.

[0148] In some alternative implementations, the dynamic adjustment model is adjusted as follows:

[0149] If the coupling factor obtained through the dynamic adjustment model is greater than the preset threshold, the initial target slip ratio is adjusted to obtain the adjusted target slip ratio.

[0150] If the coupling factor obtained through the dynamic adjustment model is less than the preset threshold, the initial drift target yaw rate is adjusted to obtain the adjusted target yaw rate.

[0151] In some alternative implementations, the torque determination unit 107 is as follows:

[0152] Based on the yaw error, determine the yaw torque that matches the vehicle's current speed.

[0153] Based on the drift error, determine the slip torque that matches the vehicle's current speed.

[0154] The target torque is determined based on the combination of yaw torque and slip torque.

[0155] In some alternative implementations, the torque determination unit 107 includes:

[0156] Determine the yaw torque weighting coefficient that matches the current slip ratio;

[0157] Determine the slip torque weighting coefficient that matches the vehicle's current steering wheel angle;

[0158] Assign a yaw torque weighting factor to the yaw torque and a slip torque weighting factor to the slip torque;

[0159] The target torque is determined by summing the yaw torque after configuring the yaw torque weighting coefficient and the slip torque after configuring the slip torque weighting coefficient.

[0160] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0161] In this embodiment, a vehicle drift control device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0162] Please see Figure 8 , Figure 8 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0163] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0164] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0165] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0167] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0168] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0169] The apparatus and units described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0170] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0171] Those skilled in the art will understand that the embodiments of this application can be provided as methods or apparatus. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, and devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process.Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0176] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0177] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0178] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A vehicle drift control method, characterized in that, The method includes: Based on the vehicle's current lateral acceleration and current longitudinal acceleration, the road adhesion coefficient and drift target parameters are determined; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration; The drift state of the vehicle is determined based on the current drift parameters and the drift target parameters. If the determination result is that the vehicle is in a drift state, the current accelerator pedal opening and the current slip ratio are obtained. Based on the road surface adhesion coefficient and the current accelerator pedal opening, an initial target slip ratio and initial torque are determined. The initial target slip ratio and initial drift target yaw rate are then adjusted using a pre-constructed dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate. The dynamic adjustment model is constructed as follows: a first coupling factor is determined based on the current yaw rate and the initial target slip ratio in the current drift parameters; a second coupling factor is determined based on the initial drift target yaw rate and the current slip ratio; and the dynamic adjustment model is constructed based on the ratio between the first coupling factor and the second coupling factor. The yaw error between the target yaw rate and the current yaw rate in the current drift parameters is determined, and the drift error between the target slip ratio and the current slip ratio is determined. Based on the yaw error and the drift error, a target torque is determined to control the vehicle to convert from the initial torque to the target torque.

2. The method according to claim 1, characterized in that, The determination of the road adhesion coefficient and drift target parameters based on the vehicle's current lateral and longitudinal acceleration includes: The road adhesion coefficient is determined based on the vehicle's current lateral and longitudinal accelerations. Based on the road surface adhesion coefficient, determine the initial drift target yaw rate and initial drift target lateral acceleration that match the current vehicle speed.

3. The method according to claim 1, characterized in that, The process of determining the vehicle's drift state based on its current drift parameters and the drift target parameters, and obtaining the current accelerator pedal opening and current slip ratio when the determination result indicates that the vehicle is in a drift state, includes: Determine the first error between the current yaw rate in the current drift parameters and the initial drift target yaw rate; Determine a second error between the current lateral acceleration in the current drift parameters and the initial drift target lateral acceleration; The first error and the second error are judged. If the judgment result is that the vehicle is in a drift state, the current accelerator pedal opening and the current slip ratio are obtained.

4. The method according to claim 1, characterized in that, The adjustment method of the dynamic adjustment model is as follows: If the coupling factor obtained through the dynamic adjustment model is greater than a preset threshold, the initial target slip ratio is adjusted to obtain the adjusted target slip ratio. If the coupling factor obtained through the dynamic adjustment model is less than a preset threshold, the initial drift target yaw rate is adjusted to obtain the adjusted target yaw rate.

5. The method according to claim 1, characterized in that, Determining the target torque based on the yaw error and the drift error includes: Based on the yaw error, a yaw torque matching the current vehicle speed is determined; Based on the drift error, a slip torque that matches the current speed of the vehicle is determined; The target torque is determined based on the combination of the yaw torque and the slip torque.

6. The method according to claim 5, characterized in that, Determining the target torque based on the combination of the yaw torque and the slip torque includes: Determine the yaw torque weighting coefficient that matches the current slip ratio; Determine a slip torque weighting coefficient that matches the current steering wheel angle of the vehicle; Configure the yaw torque weighting coefficient for the yaw torque, and configure the slip torque weighting coefficient for the slip torque; The target torque is determined by summing the yaw torque after configuring the yaw torque weighting coefficient and the slip torque after configuring the slip torque weighting coefficient.

7. A vehicle drift control device, characterized in that, The device includes: The parameter determination unit is used to determine the road adhesion coefficient and drift target parameters based on the vehicle's current lateral acceleration and current longitudinal acceleration; wherein, the drift target parameters include the initial drift target yaw rate and the initial drift target lateral acceleration; The state determination unit is used to determine the drift state of the vehicle based on the current drift parameters and the drift target parameters. If the determination result is that the vehicle is in a drift state, the unit obtains the current accelerator pedal opening and the current slip ratio. A target adjustment unit is used to determine an initial target slip ratio and an initial torque based on the road surface adhesion coefficient and the current accelerator pedal opening, and to adjust the initial target slip ratio and the initial drift target yaw rate through a pre-built dynamic adjustment model to obtain the adjusted target slip ratio and target yaw rate; wherein, the dynamic adjustment model is constructed in the following manner: a first coupling factor is determined based on the current yaw rate in the current drift parameters and the initial target slip ratio; a second coupling factor is determined based on the initial drift target yaw rate and the current slip ratio; and a dynamic adjustment model is constructed based on the ratio between the first coupling factor and the second coupling factor. The torque determination unit is used to determine the yaw error between the target yaw rate and the current yaw rate in the current drift parameters, and to determine the drift error between the target slip ratio and the current slip ratio. Based on the yaw error and the drift error, the unit determines the target torque to control the vehicle to convert from the initial torque to the target torque.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the vehicle drift control method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the vehicle drift control method according to any one of claims 1 to 6.

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