Control method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of vehicles, and discloses a control method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining associated data affecting the driving safety of a vehicle when the vehicle is driven to a target location at a future moment; according to the associated data, determining predictive control parameters of the vehicle in the transverse direction, the longitudinal direction and the vertical direction; when the vehicle runs to the target location, controlling the vehicle to run according to the predictive control parameters, and determining initial kinetic parameters when the vehicle runs according to the predictive control parameters; the initial kinetic parameters are corrected according to the actual kinetic parameters of the vehicle, target control parameters are determined, and the target control parameters indicate control parameters of the vehicle in a stable state; and controlling the vehicle to work according to the target control parameters. Through the technical scheme of the invention, the problem that the driving stability of the vehicle in different scenes is insufficient is solved, and the stability and safety of the vehicle are improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically to control methods, devices, computer equipment, and storage media. Background Technology

[0002] As vehicles rapidly evolve towards electrification, intelligence, and digitalization, the application of intelligent devices and new technologies in passenger vehicles is becoming increasingly widespread. However, existing vehicle stability control technologies still primarily rely on passive adjustments based on data from the vehicle's own sensors; or, in driver assistance systems, stability control in specific scenarios is achieved based on calibration of limited scenarios.

[0003] Therefore, in related technologies, the stability of vehicles in different scenarios is insufficient. Summary of the Invention

[0004] In view of this, embodiments of this application provide a control method, apparatus, computer device, and storage medium to solve the problem of insufficient stability of vehicles driving in different scenarios in the related art.

[0005] In a first aspect, embodiments of this application provide a control method, the method comprising:

[0006] Acquire relevant data that will affect vehicle driving safety when the vehicle reaches the target location at a future time, where the future time is the moment when the vehicle reaches the target location;

[0007] Based on the associated data, predictive control parameters for the vehicle in the lateral, longitudinal, and vertical directions are determined. The lateral direction refers to the vehicle's steering direction, the longitudinal direction refers to the vehicle's braking or driving direction, and the vertical direction refers to the direction of the vehicle's suspension vertical movement.

[0008] When the vehicle reaches the target location, the vehicle is controlled to run according to the predictive control parameters, and the initial dynamic parameters of the vehicle when running according to the predictive control parameters are determined.

[0009] The initial dynamic parameters are corrected based on the actual dynamic parameters of the vehicle to determine the target control parameters. The actual dynamic parameters are the parameters calculated using the dynamic model based on the vehicle's state data, while the target control parameters indicate the control parameters of the vehicle in a stable state.

[0010] Control the vehicle to operate with target control parameters.

[0011] The control method provided in this application acquires correlation data affecting vehicle driving safety when the vehicle reaches a target location at a future time. The future time refers to the moment the vehicle reaches the target location, and comprehensive scenario data, including weather, traffic, road conditions, and vehicle condition, is obtained for the vehicle's future journey to the target location. This provides a scenario for determining the vehicle's control parameters. Based on the correlation data, predictive control parameters for the vehicle in the lateral, longitudinal, and vertical directions are determined. Lateral refers to the vehicle's steering direction, longitudinal refers to the vehicle's braking or driving direction, and vertical refers to the direction of the vehicle's suspension's vertical movement. This provides vehicle control parameters for maintaining stable driving in the scenario of the target location at the future time. When the vehicle reaches the target location, the system controls the vehicle to operate according to the predictive control parameters and determines that the vehicle is operating according to the predictive control parameters. The initial dynamic parameters during operation allow the vehicle to use predicted control parameters to ensure stability, while also acquiring dynamic parameters reflecting the vehicle's current movement according to these control parameters. The initial dynamic parameters are then corrected based on the vehicle's actual dynamic parameters to determine the target control parameters. These target control parameters indicate the control parameters for the vehicle in a stable state. The initial dynamic parameters are further corrected based on the actual dynamic parameters calculated using the dynamic model from real-time monitored vehicle state data, ensuring the dynamic parameters remain within a reasonable range. This prevents instability or even danger caused by initial dynamic parameters mismatched with the current scenario. Controlling the vehicle to operate with the target control parameters allows these stability-enhancing parameters to be applied to vehicle control, improving both stability and safety.

[0012] In one possible implementation, predictive control parameters for the vehicle in the lateral, longitudinal, and vertical directions are determined based on correlated data, including:

[0013] Based on the associated data, the first control parameter of the vehicle in the lateral direction is found in the first lookup table of the associated data and the lateral control parameters. The first lookup table indicates that multiple associated data correspond one-to-one with multiple first control parameters. The first control parameters include steering wheel angle and vehicle speed.

[0014] Based on the associated data, the second control parameters of the vehicle in the longitudinal direction are found in the second lookup table of the associated data and the longitudinal control parameters. The second lookup table indicates that multiple associated data correspond one-to-one with multiple second control parameters, including driving force and braking force.

[0015] Based on the associated data, the third control parameters of the vehicle in the vertical direction are found in the third lookup table of the associated data and the vertical control parameters. The third lookup table indicates that multiple associated data correspond one-to-one with multiple third control parameters, including suspension stiffness and damping.

[0016] The first control parameter, the second control parameter, and the third control parameter are used as predictive control parameters. The priority of the predictive control parameters used from high to low is as follows: the first control parameter, the second control parameter, and the third control parameter.

[0017] In this implementation method, by using a lookup table, the first horizontal control parameter, the second vertical control parameter, and the third vertical control parameter corresponding to the associated data are determined. The predicted scenario of the vehicle at the target location reflected by the associated data is associated and corresponded with the control parameters that enable the vehicle to maintain stability in that scenario, thereby providing control parameters to ensure the stability of the vehicle when it passes through the target location in the future.

[0018] In one possible implementation, determining the initial dynamic parameters of the vehicle when it operates according to predictive control parameters includes:

[0019] The yaw rate of the vehicle is determined based on the steering wheel angle, vehicle speed, vehicle wheelbase, and vehicle understeer.

[0020] Determine the vehicle's lateral acceleration based on its speed;

[0021] Determine the vehicle's roll angle based on the vehicle's track width and center of gravity height;

[0022] The wheel slip ratio of a vehicle is determined based on its speed, wheel angular velocity, and effective wheel rolling radius.

[0023] Yaw rate, lateral acceleration, roll angle, and wheel slip ratio were used as initial dynamic parameters.

[0024] In this implementation method, when the vehicle is controlled according to the predictive control parameters, the collected data is used to construct yaw rate, lateral acceleration, roll angle, and wheel slip ratio using the above formulas, thus comprehensively reflecting the initial dynamic parameters. The dynamic parameters of the vehicle at future moments are predicted, providing dynamic parameters to ensure vehicle stability.

[0025] In one possible implementation, the initial dynamic parameters are corrected based on the actual dynamic parameters of the vehicle to determine the target control parameters, including:

[0026] Determine the deviation between the actual dynamic parameters and the initial dynamic parameters;

[0027] If the deviation is within the set deviation range, the predicted control parameter is used as the target control parameter; otherwise, the target control parameter is determined based on the actual dynamic parameters.

[0028] This implementation method determines the deviation between the actual and initial dynamic parameters. It obtains the deviation between the predicted initial dynamic parameters when the vehicle reaches the target location and the actual dynamic parameters calculated from real-time monitored vehicle status data. This provides a reference value for determining whether the predicted initial dynamic parameters match the road conditions, weather, traffic, and vehicle condition at the target location. Based on the analysis of the deviation, target control parameters for ensuring vehicle stability are obtained. Correcting the initial dynamic parameters with the actual dynamic parameters avoids vehicle instability or compromised safe driving when the initial dynamic parameters do not match the actual scenario at the target location. It also provides a control method to ensure vehicle stability and safety when the initial dynamic parameters do not match the actual scenario at the target location.

[0029] In one possible implementation, the target control parameters are determined based on the actual dynamic parameters, including:

[0030] The target control parameter corresponding to the actual dynamic parameter is found from the mapping table of dynamic parameters and control parameters. The mapping table of dynamic parameters and control parameters indicates that multiple dynamic parameters correspond one-to-one with multiple control parameters.

[0031] This implementation provides target control parameters for the vehicle's control system based on a mapping table between dynamic parameters and control parameters. Therefore, when it is necessary to control the vehicle using control parameters corresponding to the actual dynamic parameters, the corresponding target control parameters can be obtained through this mapping table. This facilitates the use of actual dynamic parameters to regulate the vehicle, provides control parameters to ensure vehicle stability in scenarios where actual dynamic parameters are used for vehicle control, and improves vehicle stability.

[0032] In one possible implementation, controlling the vehicle to operate with target control parameters includes:

[0033] If the roll angle error corresponding to the roll angle in the deviation is greater than the first threshold, or the yaw rate error corresponding to the yaw rate in the deviation is greater than the second threshold, the vehicle's electronic stability system will be triggered.

[0034] The chassis domain controller of the vehicle applies braking force to at least one wheel of the vehicle and / or limits the output of the vehicle's power domain until the roll angle error is less than or equal to a first threshold and the yaw rate error is less than or equal to a second threshold.

[0035] In this implementation method, when the deviation is large, ESP is triggered. As an active safety system, ESP mainly helps the vehicle maintain stability by monitoring the vehicle status and intervening, thus ensuring the vehicle's safety and stability.

[0036] Secondly, embodiments of this application provide a control device, the device comprising:

[0037] The acquisition module is used to acquire relevant data that will affect the vehicle's driving safety when the vehicle reaches the target location at a future time. The future time is the moment when the vehicle reaches the target location.

[0038] The prediction module is used to determine the predictive control parameters of the vehicle in the lateral, longitudinal and vertical directions based on the associated data. The lateral direction refers to the vehicle's steering direction, the longitudinal direction refers to the vehicle's braking or driving direction, and the vertical direction refers to the direction of the vehicle's suspension vertical movement.

[0039] The operation module is used to control the vehicle to run according to the predictive control parameters when the vehicle reaches the target location, and to determine the initial dynamic parameters of the vehicle when running according to the predictive control parameters.

[0040] The determination module is used to control the vehicle based on predictive control parameters and determine the initial dynamic parameters of the vehicle.

[0041] The correction module is used to correct the initial dynamic parameters based on the actual dynamic parameters of the vehicle and determine the target control parameters. The actual dynamic parameters are the parameters calculated by using the dynamic model based on the vehicle's state data, and the target control parameters indicate the control parameters of the vehicle in a stable state.

[0042] The working module is used to control the vehicle to operate according to the target control parameters.

[0043] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the control method described in the first aspect or any corresponding embodiment.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the control method described in the first aspect or any corresponding embodiment.

[0045] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to execute the control method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0046] 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.

[0047] Figure 1 This is a flowchart illustrating a control method according to an embodiment of this application;

[0048] Figure 2 This is a flowchart illustrating another control method according to an embodiment of this application;

[0049] Figure 3 This is a structural block diagram of a control device according to an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application. Detailed Implementation

[0051] 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.

[0052] As the automotive industry rapidly evolves towards electrification, intelligence, and digitalization, intelligent features and new technologies are increasingly being applied to passenger vehicles. However, existing vehicle stability control technologies still primarily rely on data from the vehicle's own sensors, maintaining stability through passive responses and failing to anticipate complex driving conditions. Specifically:

[0053] Insufficient traffic environment forecasting: Unable to predict traffic lights, traffic flow, congestion, traffic accidents, etc. in real time.

[0054] Inadequate weather forecasting: Failure to effectively predict weather changes, such as rain, snow, and fog.

[0055] Insufficient road condition prediction: It is difficult to predict in real time road types and characteristics (such as friction coefficient, slope, tilt angle), road maintenance, road obstacles, etc.

[0056] Insufficient vehicle state prediction and assessment: Failure to fully predict and assess vehicle dynamic states, such as yaw rate and sideslip angle.

[0057] Therefore, how to achieve proactive and intelligent control of vehicle stability by combining real-time prediction and reasoning capabilities with multi-dimensional information such as traffic environment, weather conditions, road conditions, and vehicle status has become an important direction for current technological development.

[0058] According to an embodiment of this application, a control method embodiment is provided. 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.

[0059] This application provides a control method that can be used in the aforementioned in-vehicle devices, such as autonomous driving domain controllers and vehicle infotainment systems. Figure 1 This is a flowchart of a control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0060] Step S101: Obtain the associated data that will affect the vehicle's driving safety when the vehicle travels to the target location at a future time.

[0061] Specifically, related data refers to data that affects vehicle driving safety when the vehicle reaches the target location, including but not limited to traffic data, weather data, road data, and vehicle status data. The target location refers to a location that the vehicle is predicted to pass through at a future time. The closer the future time is to the current time, the closer the target location is to the current location of the vehicle.

[0062] In one possible implementation, a planned route from the current location to the destination can be obtained through the vehicle's navigation system, with the destination being any point on the planned route. For example, 9:05, the time when the vehicle can reach the destination in 5 minutes after the current time of 9:00, can be taken as a future time. The vehicle's infotainment system can also obtain weather data for the future time, as well as traffic data such as traffic flow, traffic lights, traffic congestion, and traffic accidents at the destination. Furthermore, it can obtain road data for the destination, such as asphalt roads, gravel roads, and concrete roads, and the vehicle's potential status data.

[0063] It should be noted that, to ensure the real-time nature and reliability of the data, weather, traffic, and road data in the associated data can be downloaded to the vehicle from the cloud, or associated data that has been stored for a certain period of time can be stored on the vehicle's storage device. Furthermore, for vehicle status data, the vehicle can use current driving data and vehicle status monitoring data to predict its status at the target location, or the cloud can collect current vehicle status data to predict future vehicle status data.

[0064] Step S102: Based on the associated data, determine the predictive control parameters of the vehicle in the lateral, longitudinal, and vertical directions, where the lateral direction refers to the vehicle's steering direction, the longitudinal direction refers to the vehicle's braking or driving direction, and the vertical direction refers to the direction of the vehicle's suspension vertical movement.

[0065] Specifically, lateral refers to the direction of vehicle rotation, with the vehicle body as the origin of a three-dimensional Cartesian coordinate system; longitudinal refers to the direction of braking or driving; and vertical refers to the direction of vertical movement of the vehicle's suspension. Predictive control parameters refer to the control parameters in the lateral, longitudinal, and vertical directions that are predicted based on correlated data when the vehicle reaches the target location. Examples include vehicle speed, acceleration, and angular velocity.

[0066] In one possible implementation, the associated data for the future time is as follows: the future time is 10:00, the target location is the exit of road A, road A has a gravel surface, the weather at 10:00 is sunny, the traffic light at the exit is green, the vehicle's remaining battery power is 90%, and the suspension vibration amplitude is 30% of the maximum suspension amplitude. Based on the predicted associated data for the vehicle at the future time, predictive control parameters are determined to maintain the vehicle's ideal stable state under the current weather, road conditions, traffic conditions, and vehicle status. For example, the vehicle speed is 10 km / h, and the longitudinal acceleration is 1 m / s². 2 .

[0067] Step S103: When the vehicle reaches the target location, control the vehicle to run according to the predicted control parameters, and determine the initial dynamic parameters of the vehicle when running according to the predicted control parameters.

[0068] Specifically, initial dynamic parameters refer to the dynamic parameters monitored when the vehicle is running according to predictive control parameters, such as engine output torque and power. When the vehicle reaches the target location, the vehicle is controlled to run according to the predictive control parameters, and at the same time, the initial dynamic parameters are obtained using the predictive control parameters.

[0069] In one possible implementation, a mapping table is constructed for the control parameters and dynamic parameters, and the initial dynamic parameters corresponding to the predicted control parameters are found using this mapping table.

[0070] Step S104: Correct the initial dynamic parameters based on the actual dynamic parameters of the vehicle to determine the target control parameters, which indicate the control parameters of the vehicle in a stable state.

[0071] Specifically, actual dynamic parameters refer to the dynamic parameters calculated using a dynamic model based on real-time monitored vehicle state data. Target control parameters refer to the control parameters that ensure the vehicle operates in a stable state. To ensure that the initial dynamic parameters are adapted to the current actual vehicle condition, road condition, weather, traffic, and other conditions, the initial dynamic parameters are corrected using the real-time monitored actual dynamic parameters to obtain the target control parameters that ensure the vehicle remains in a stable state.

[0072] Step S105: Control the vehicle to operate with the target control parameters.

[0073] Specifically, after obtaining the target control parameters, controlling the vehicle to operate according to the target control parameters can maintain the vehicle's stable state and ensure that the vehicle is in a stable and safe state.

[0074] The control method provided in this embodiment acquires relevant data affecting vehicle driving safety when the vehicle reaches the target location at a future time. The future time refers to the moment the vehicle reaches the target location, providing comprehensive scenario data such as weather, traffic, road conditions, and vehicle status, thus providing a scenario for determining the vehicle's control parameters. Based on the relevant data, predictive control parameters for the vehicle in the lateral, longitudinal, and vertical directions are determined. Lateral refers to the vehicle's steering direction, longitudinal refers to the vehicle's braking or driving direction, and vertical refers to the direction of the vehicle's suspension's vertical movement, providing vehicle control parameters for maintaining stable driving in the scenario of the target location at the future time. When the vehicle reaches the target location, the system controls the vehicle to operate according to the predictive control parameters and determines the driving parameters for the vehicle operating according to the predictive control parameters. Initial dynamic parameters allow the vehicle to use predicted control parameters to ensure stability, while also acquiring dynamic parameters reflecting the vehicle's current movement according to these control parameters. The initial dynamic parameters are then corrected based on the vehicle's actual dynamic parameters to determine target control parameters. These target control parameters indicate the control parameters for the vehicle in a stable state. The initial dynamic parameters are further corrected based on the actual dynamic parameters calculated from real-time monitored vehicle state data using a dynamic model, ensuring the dynamic parameters remain within a reasonable range. This prevents initial dynamic parameters from being mismatched with the current scenario, which could lead to vehicle instability or even danger. Controlling the vehicle to operate with the target control parameters allows these stability-enhancing parameters to be applied to vehicle control, improving both stability and safety.

[0075] This application provides a control method that can be used in the aforementioned vehicle-mounted equipment. Figure 2 This is a flowchart of another control method according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:

[0076] Step S201: Obtain the associated data that will affect the vehicle's driving safety when the vehicle travels to the target location at a future time.

[0077] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0078] Step S202: Based on the associated data, determine the predictive control parameters of the vehicle in the lateral, longitudinal, and vertical directions, where the lateral direction refers to the vehicle's steering direction, the longitudinal direction refers to the vehicle's braking or driving direction, and the vertical direction refers to the direction of the vehicle's suspension vertical movement.

[0079] Specifically, step S202 includes:

[0080] Step S2021: Based on the associated data, search for the first control parameters of the vehicle in the lateral direction in the first lookup table of associated data and lateral control parameters. The first lookup table indicates that multiple associated data correspond one-to-one with multiple first control parameters. The first control parameters include steering wheel angle and vehicle speed.

[0081] Specifically, the first lookup table refers to a database or table that stores a one-to-one correspondence between multiple related data and multiple first control parameters. The first control parameters refer to the vehicle's lateral control parameters, including steering wheel angle and vehicle speed. The first lookup table of related data and lateral control parameters is used to find the corresponding first lateral control parameters for the vehicle.

[0082] In one example, the associated data shows rainy weather, heavy traffic, highway auxiliary road, and low tire friction, which corresponds to the vehicle's lateral control parameters of 15 km / h speed and 3-degree steering wheel angle.

[0083] Step S2022: Based on the associated data, look up the second control parameters of the vehicle in the longitudinal direction in the second lookup table of associated data and longitudinal control parameters. The second lookup table indicates that multiple associated data correspond one-to-one with multiple second control parameters, including driving force and braking force.

[0084] Specifically, the second lookup table refers to a database or table that stores a one-to-one correspondence between multiple related data and multiple second control parameters. The second control parameters refer to the vehicle's longitudinal control parameters, including driving force and braking force. The second lookup table of related data and longitudinal control parameters is used to find the corresponding second control parameters of the vehicle in the longitudinal direction.

[0085] In one example, the scenario described in the example associated with S2021 above corresponds to a ratio of 6:4 for the driving force and braking force of the vehicle.

[0086] Step S2023: Based on the associated data, look up the third control parameters of the vehicle in the vertical direction in the third lookup table of the associated data and the vertical control parameters. The third lookup table indicates that multiple associated data correspond one-to-one with multiple third control parameters. The third control parameters include suspension stiffness and damping.

[0087] Specifically, a third lookup table refers to a database or table that stores a one-to-one correspondence between multiple related data and multiple third control parameters. The third control parameters refer to the vehicle's vertical control parameters, including suspension stiffness and damping. The third lookup table containing the related data and vertical control parameters is used to find the corresponding third control parameters for the vehicle in the vertical direction.

[0088] In one example, the road condition in the associated data is gravel road surface, and the third lookup table shows that the suspension stiffness is 20% of the maximum stiffness and the damping is 80% of the maximum damping value.

[0089] Step S2024: The first control parameter, the second control parameter, and the third control parameter are used as predicted control parameters, wherein the priority of the predicted control parameters used from high to low is: the first control parameter, the second control parameter, and the third control parameter.

[0090] Specifically, after obtaining the first, second, and third control parameters corresponding to the lateral, longitudinal, and vertical directions in a three-dimensional Cartesian coordinate system, these parameters are combined into predictive control parameters, reflecting the overall adjustment values ​​of the vehicle's various parameters that the vehicle should achieve. The first control parameter has a higher priority than the second, and the second has a higher priority than the third. In other words, the system first ensures that rollover or sideslip does not occur laterally, then ensures that sudden acceleration or deceleration does not occur longitudinally, and finally ensures the comfort provided by the suspension. For example, in monitoring the vehicle's safety strategy, the system first monitors the vehicle's lateral stability. If a risk of sideslip is detected, the lateral control parameter (first control parameter) is adjusted first. If lateral stability is controlled, the system then adjusts the longitudinal control parameter (second control parameter) to optimize longitudinal stability. Finally, based on the remaining control capability, the system adjusts the vertical control parameter (third control parameter) to improve comfort.

[0091] Step S203: When the vehicle reaches the target location, control the vehicle to run according to the predicted control parameters, and determine the initial dynamic parameters of the vehicle when running according to the predicted control parameters.

[0092] Please see details Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0093] Step S204: Correct the initial dynamic parameters based on the actual dynamic parameters of the vehicle to determine the target control parameters. The actual dynamic parameters are the parameters calculated using the dynamic model based on the vehicle's state data. The target control parameters indicate the control parameters of the vehicle in a stable state.

[0094] Please see details Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0095] Step S205: Control the vehicle to operate with the target control parameters.

[0096] Please see details Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0097] It should be noted that, based on the associated data, after determining the first control parameter, the second control parameter, and the third control parameter, the vehicle is controlled first according to the first control parameter, then according to the second control parameter, and finally according to the third control parameter.

[0098] In one example, the weather data indicates rain or snow. The driving mode is adjusted to comfort mode. After determining the second control parameter using the second lookup table, the throttle response is reduced, the braking response is increased, and the ABS and TCS intervention thresholds are adjusted to prevent wheel slippage. After determining the third control parameter using the third lookup table, the suspension damping and stiffness are adjusted, with increased damping to improve stability.

[0099] In one example, the weather in the associated data is foggy. The fog lights are turned on, and the steering mode and steering torque are adjusted according to the first control parameters determined by the first lookup table to prevent the vehicle from losing lateral control. According to the second control parameters determined by the second lookup table, the vehicle speed is reduced to increase the distance between vehicles.

[0100] In one example, when traffic data in the associated data reflects an increase in traffic flow, the driving mode is adjusted to standard mode, the second control parameter is determined according to the second lookup table, the throttle response speed is reduced, the braking response speed is increased, the distance and speed are adjusted, and the vehicle brakes and decelerates in advance when traffic lights are detected.

[0101] In one example, the traffic data in the associated data includes, when a traffic accident is detected, changing the lane trajectory to avoid the accident based on the first control parameter in the first lookup table; and slowing down in advance based on the second control parameter in the second lookup table.

[0102] It should be noted that when the above-mentioned related data occur in combination, for example, if the related data reflects that the target location is in traffic congestion and foggy weather, the vehicle may switch to standard mode, turn on the fog lights, determine the second control parameters according to the second lookup table, reduce the throttle response, increase the braking response, and adjust the distance and speed according to the traffic congestion to prevent rear-end collisions.

[0103] For example, the associated data reflects that when the target location is in rainy or snowy weather and on a rough road, the vehicle is adjusted to comfort mode, the first control parameter is determined according to the first lookup table, the vehicle direction is adjusted to prevent sideslip, the second control parameter is determined according to the second lookup table, the speed is reduced and the distance is increased, the third control parameter is determined according to the third lookup table, the suspension stiffness is adjusted, and the driving force distribution is adjusted according to the road slope to prevent slippage.

[0104] In one example, the associated data reflects that the weather is sunny, the road is a dry asphalt surface, the traffic is highway, and the vehicle condition reflects that the vehicle is traveling at a speed of 120 km / h. When the vehicle's perception system identifies that the vehicle is about to approach a curve, based on the determined predictive control parameters, the following coordinated control is performed in the lateral, longitudinal, and vertical directions:

[0105] Based on the second longitudinal control parameters determined by the second lookup table, braking is engaged to appropriately reduce vehicle speed; for four-wheel drive models, the distribution of driving force and braking torque is optimized to prevent wheel slippage; and the driving torque is dynamically adjusted to optimize tire adhesion.

[0106] Based on the first lateral control parameters determined by the first lookup table, a torque difference is generated when the front wheels brake on the inside and outside when entering a curve (the braking torque on the inside is slightly greater than that on the outside), which produces an auxiliary steering effect; if there is rear wheel steering, the steering angles of the front and rear wheels are adjusted to stabilize the vehicle's steering response.

[0107] Based on the third vertical control parameters determined by the third lookup table, increase the stiffness and compression damping of the inner suspension, increase the restoring damping and stiffness of the outer suspension, and increase the support and stiffness of the inner body.

[0108] In one example, the associated data reflects the weather as rainy, the road as slippery, the vehicle as traveling at 100 km / h, and the traffic as the vehicle recognizing an emergency situation 100 meters ahead. The following coordinated control is implemented in the horizontal, vertical, and longitudinal directions:

[0109] Based on the second longitudinal control parameters determined by the second lookup table, the vehicle decelerates and brakes in advance, while simultaneously illuminating the hazard taillights to warn vehicles behind; it optimizes the front and rear wheel braking torque distribution to prevent tire lock-up; and it quickly adjusts the ABS to ensure that the drive wheel slip ratio is within a safe range, maintaining tire adhesion to the road surface.

[0110] Based on the first control parameters for the lateral direction determined by the first lookup table, the steering force is increased, the steering angle is dynamically adjusted, and the lateral stability of the vehicle is optimized.

[0111] Based on the third vertical control parameters determined by the third lookup table, the suspension stiffness and damping are adjusted to optimize the vertical motion of the vehicle body; reduce vehicle body dive and improve braking stability.

[0112] The control method provided in this application uses a lookup table to determine the first horizontal control parameter, the second vertical control parameter, and the third vertical control parameter corresponding to the associated data. The predicted scenario of the vehicle at the target location reflected by the associated data is associated and corresponded with the control parameters that enable the vehicle to maintain stability in that scenario, thereby providing control parameters to ensure vehicle stability when the vehicle passes through the target location in the future.

[0113] In one possible implementation, determining the initial dynamic parameters of the vehicle when it operates according to the predictive control parameters in step S203 above includes the following steps:

[0114] Step a1: Determine the vehicle's yaw rate based on the steering wheel angle, vehicle speed, vehicle wheelbase, and understeer.

[0115] Specifically, the steering wheel angle refers to the angle formed between the centerline of the front wheels and the centerline of the vehicle when the front wheels are turned to their extreme left or right positions, with a normal range of 30 to 40 degrees. Wheelbase refers to the distance between the front and rear axles, directly affecting the vehicle's handling stability and steering response. Understeer refers to the characteristic of insufficient steering exhibited when the yaw rate is less than the trajectory angular velocity, resulting in a gradual decrease in the vehicle's sideslip angle. Yaw rate is the angular velocity of a vehicle rotating around its vertical axis, reflecting its dynamic characteristics during lateral movement, and is measured in rad / s. The yaw rate γ can be calculated using the steering wheel angle, vehicle speed, wheelbase, and understeer. target The formula is as follows:

[0116]

[0117] In the formula, v is the longitudinal velocity, δ is the steering wheel angle, L is the wheelbase, and K is the understeer gradient (calibrated value).

[0118] Step a2: Determine the lateral acceleration of the vehicle based on its speed.

[0119] Specifically, lateral acceleration refers to the acceleration component perpendicular to the direction of travel during vehicle movement, used to describe the dynamic characteristics of a vehicle during cornering or lateral movement. The lateral acceleration can be obtained by dividing the vehicle's speed by the acceleration per unit time. Lateral acceleration is typically calibrated to be within the range of 0.4g to 0.5g; exceeding this range triggers a warning.

[0120] Step a3: Determine the vehicle's roll angle based on the vehicle's track width and center of gravity height.

[0121] Specifically, wheelbase refers to the distance between the centerlines of the left and right tires on the same axle, directly affecting the vehicle's lateral stability and turning radius. Center of gravity height refers to the vertical distance from the vehicle's center of gravity to the ground, affecting the vehicle's dynamic response under acceleration. Roll angle refers to the maximum angle formed between the vehicle body and the ground when the vehicle is turning at a certain speed. Based on the vehicle's wheelbase and center of gravity height, the vehicle's roll angle can be calculated using the following formula.

[0122]

[0123] In the formula, T is the wheel track and h is the height of the center of gravity.

[0124] When the roll angle generally exceeds 5° to 8°, the risk of rollover increases significantly.

[0125] Step a4: Determine the vehicle's wheel slip ratio based on the vehicle speed, wheel angular velocity, and effective wheel rolling radius.

[0126] Specifically, wheel angular velocity refers to the angular velocity of a wheel rotating around its axis. Effective rolling radius refers to the rolling radius of the tire tread at the point of contact with the ground when there is no braking force. Wheel slip ratio refers to the proportion of slippage in wheel motion; it is a core parameter for vehicle dynamics control, reflecting the adhesion state between the tire and the road surface, and directly affecting braking / driving efficiency and stability control. Based on vehicle speed, wheel angular velocity, and effective rolling radius, the wheel slip ratio s can be calculated using the following formula:

[0127]

[0128] In the formula, Vx is the longitudinal velocity of the vehicle (m / s), ω is the angular velocity of the wheel (rad / s), and rx is the angular velocity of the wheel. e The effective rolling radius of the wheel (m) is given. The slip ratio is determined by calibration according to different road surfaces (e.g., the calibration value of s is 20% for dry road surfaces and 10% for wet road surfaces).

[0129] Step a5: Use yaw rate, lateral acceleration, roll angle, and wheel slip ratio as initial dynamic parameters.

[0130] Specifically, the yaw rate, lateral acceleration, roll angle, and wheel slip ratio obtained from the above steps can be combined to form initial dynamic parameters that reflect the vehicle's dynamic performance.

[0131] In this implementation method, when the vehicle is controlled according to the predictive control parameters, the collected data is used to construct yaw rate, lateral acceleration, roll angle, and wheel slip ratio using the above formulas, thus comprehensively reflecting the initial dynamic parameters. The dynamic parameters of the vehicle at future moments are predicted, providing dynamic parameters to ensure vehicle stability.

[0132] In one possible implementation, step S204 above includes the following steps:

[0133] Step b1: Determine the deviation between the actual dynamic parameters and the initial dynamic parameters.

[0134] Step b2: If the deviation is within the set deviation range, the predicted control parameter is used as the target control parameter; otherwise, the target control parameter is determined based on the actual dynamic parameters.

[0135] Specifically, the difference between the actual dynamic parameters and the corresponding initial dynamic parameters is used as the deviation. The system then determines whether the deviation falls within a set range. If the deviation is within the set range, it indicates that the predictive control parameters corresponding to the initial dynamic parameters are suitable for the current scenario and can be used as target control parameters. Otherwise, the target control parameters need to be determined based on the actual dynamic parameters obtained from real-time vehicle monitoring.

[0136] In one example, in the longitudinal direction, the optimal braking force distribution ratio is calculated using Model Predictive Control (MPC), for example, a front-to-rear force distribution ratio between 6:4 and 7:3. In the lateral direction, the yaw rate deviation is defined as the difference Δγ between the actual value and the predicted value in the predictive control parameters. In the vertical direction, damping and stiffness are adjusted according to the roll angle Φ; for example, if the roll angle deviation is >3°, the outer damping is increased by 30%.

[0137] In one possible implementation, step b2 includes: searching for the target control parameter corresponding to the actual dynamic parameter from a mapping table of dynamic parameters and control parameters, wherein the mapping table of dynamic parameters and control parameters indicates that multiple dynamic parameters correspond one-to-one with multiple control parameters.

[0138] Specifically, the mapping table between dynamic parameters and control parameters refers to a database or table that stores a one-to-one correspondence between multiple dynamic parameters and multiple control parameters. The target control parameters corresponding to the actual dynamic parameters can be found from this mapping table.

[0139] In one example of this implementation, regarding whether the deviation corresponding to the wheel slip ratio is within a set deviation range, considering that the Anti-lock Braking System (ABS) and Traction Control System (TCS) will affect the reference slip ratio, the boundary threshold of the deviation range affected by the presence of ABS or TCS can be adjusted using the following formula:

[0140] λ new =λ default ×μ ratio

[0141] In the formula, λ default The baseline slip ratio threshold under standard dry road conditions (depending on vehicle calibration parameters (such as tire characteristics and suspension stiffness)), typical range: λ corresponding to ABS default The range is 15% to 25%; the corresponding λ for TCS default The range is 10%-20%. μ ratio The current road surface friction coefficient μ current Friction coefficient μ of standard road surface dry The ratio of [the ratio of the two values] is referred to below as the intervention threshold. The intervention threshold can be expressed by the following formula:

[0142]

[0143] Typically, μ dry The friction coefficient of dry asphalt pavement (usually taken as 0.8 to 1.0), μ current This represents the current road surface friction coefficient, estimated in real time. A typical value for the friction coefficient on a wet, slippery road surface is μ. current and μ ratio The results can be obtained through calibration tests as shown in the table below:

[0144] Road conditions <![CDATA[μ current ]]> <![CDATA[μ ratio ]]> wet asphalt 0.5～0.7 0.5～0.8 compacted snow 0.2～0.3 0.25～0.35

[0145] To adjust the ABS intervention threshold, the following methods can be used to ensure vehicle stability on different road surfaces:

[0146] On low-friction surfaces, when a decrease in the road surface friction coefficient is detected, the ABS system increases the intervention threshold, lowers the acceleration threshold for triggering ABS, and allows ABS to intervene earlier to prevent tire lock-up and ensure vehicle stability during braking.

[0147] On high-friction surfaces, when the coefficient of friction is high, the ABS system lowers the intervention threshold, allowing greater braking force to be applied to the tires, thereby improving braking efficiency while maintaining vehicle handling.

[0148] To adjust the TCS intervention threshold, vehicle stability can be ensured on different road surfaces through the following methods:

[0149] On low-friction surfaces and under low-friction conditions, the TCS system increases the intervention threshold and reduces the driving force on the drive wheels to prevent them from slipping due to lack of traction. Simultaneously, the system may adjust the distribution of driving torque to optimize tire traction.

[0150] On high-friction surfaces and under high-friction conditions, the TCS system lowers the intervention threshold, allowing more driving force to be output, improving the vehicle's acceleration performance while maintaining vehicle stability.

[0151] In other words, the system dynamically adjusts the intervention thresholds of ABS and TCS based on real-time road and weather conditions to ensure that the vehicle maintains optimal stability and driving performance under different driving conditions. This process is typically managed by the Electronic Control Unit (ECU), which calculates and adjusts relevant parameters in real time based on sensor data and preset control logic.

[0152] Step b2: If the deviation is within the set deviation range, the predicted control parameter is used as the target control parameter; otherwise, the target control parameter is determined based on the actual dynamic parameters.

[0153] Specifically, if the deviation is within the set deviation range, it indicates that the initial dynamic parameters and the actual dynamic parameters are very close, and the predicted control parameters corresponding to the initial dynamic parameters can be used as the target control parameters. Otherwise, it indicates that the initial dynamic parameters do not match the actual scenario when the vehicle travels to the target location. Therefore, the target control parameters are re-determined based on the actual dynamic parameters to ensure that the vehicle can pass through the target location stably and safely.

[0154] This implementation method determines the deviation between the actual and initial dynamic parameters, obtaining the deviation between the predicted initial dynamic parameters when the vehicle reaches the target location and the actual dynamic parameters monitored in real time when the vehicle actually reaches the target location. This provides a reference value for whether the predicted initial dynamic parameters match the road conditions, weather, traffic, and vehicle condition at the target location. Based on the analysis of the deviation, target control parameters for ensuring vehicle stability are obtained. By correcting the initial dynamic parameters with the actual dynamic parameters, vehicle instability or compromised safe driving is avoided when the initial dynamic parameters do not match the actual scenario at the target location. It also provides a control method to ensure vehicle stability and safety when the initial dynamic parameters do not match the actual scenario at the target location.

[0155] In one possible implementation, step S205 includes:

[0156] Step c1: If the roll angle error corresponding to the roll angle in the deviation is greater than the first threshold, or the yaw rate error corresponding to the yaw rate in the deviation is greater than the second threshold, then the vehicle's electronic stability system is triggered.

[0157] Step c2: The chassis domain controller of the vehicle applies braking force to at least one wheel of the vehicle and / or limits the output of the vehicle's power domain until the roll angle error is less than or equal to a first threshold and the yaw rate error is less than or equal to a second threshold.

[0158] Specifically, roll angle error refers to the difference between the two roll angles in the initial dynamic parameters and the actual dynamic parameters. The first threshold indicates a value reflecting a large roll angle error. Yaw rate error refers to the difference between the two yaw rates in the initial dynamic parameters and the actual dynamic parameters. The second threshold indicates a value reflecting a large yaw rate error.

[0159] If the roll angle error exceeds a first threshold, or the yaw rate exceeds a second threshold, it indicates a significant deviation, and the vehicle's Electronic Stability Program (ESP) is triggered. In response to the triggering of the vehicle's ESP, the chassis domain controller applies braking force to at least one wheel of the vehicle and / or limits the output of the vehicle's power domain to reduce vehicle speed or complete braking in a timely manner until the roll angle error is less than or equal to the first threshold and the yaw rate error is less than or equal to the second threshold, that is, the deviation is adjusted to a preset reasonable range.

[0160] In this implementation method, when the deviation is large, ESP is triggered. As an active safety system, ESP mainly helps the vehicle maintain stability by monitoring the vehicle status and intervening, thus ensuring the vehicle's safety and stability.

[0161] In one possible implementation of this application's embodiments, in low-friction road surfaces, the optimization of driving force and braking force distribution can be achieved through the following strategy:

[0162] The vehicle status is estimated by measuring wheel slip ratio, yaw rate, sideslip rate, and center of gravity sideslip angle to determine whether the vehicle is at risk of losing control. If there is a risk, active optimization control is implemented.

[0163] Drive force distribution adjustment: Dynamically adjusts drive force output through TCS and ESP to prevent drive wheel slippage and optimize vehicle stability;

[0164] Brake force distribution adjustment: Through EBD (Electronic Brake Distribution), the braking force ratio between the front and rear wheels is dynamically optimized to prevent wheel lock-up and improve braking efficiency;

[0165] Cooperative control: Drive force and braking force distribution adjustment systems (such as TCS, ESP, EBD, ABS) work together to ensure the vehicle maintains safety and stability under various driving conditions.

[0166] In one possible implementation of this application, the suspension stiffness and damping are adjusted in a curve to improve steering stability.

[0167] The vehicle's yaw rate and roll angle are monitored in real time.

[0168] Stability threshold judgment: If the yaw rate deviation or roll angle exceeds the safety threshold (the stability threshold is a calibrated value, confirmed by actual vehicle calibration), active adjustment is triggered;

[0169] Dynamic adjustment strategy for suspension parameters:

[0170] Damping adjustment: During the entry phase of a corner, increase the compression damping (calibrated value: adjust the range according to the roll angle) to reduce body roll; during the mid-corner phase, balance the compression / recovery damping (calibrated value) to maintain tire contact with the ground; during the exit phase of a corner, reduce the recovery damping (e.g., 20%) to prevent body rollback.

[0171] Stiffness adjustment (if there is an air spring): The air pressure of the airbag is controlled by a solenoid valve to increase the stiffness of the outer suspension (calibrated value: the adjustment range is confirmed by calibration according to the roll angle) to suppress roll.

[0172] Cooperative control: In conjunction with ESP or TCS, when understeer is detected, the front suspension stiffness is increased first to improve front wheel grip.

[0173] In the environmental perception of weather, roads, traffic, and vehicle conditions corresponding to the associated data, the perceived data plays the following role in the three-dimensional collaborative control strategy (horizontal, vertical, and longitudinal): By predicting future changes in roads, traffic, weather, and vehicle conditions through environmental perception, control parameters are determined based on these changes. Corresponding dynamic parameters are then determined based on these control parameters, and the final target control parameters are obtained through adjustments to the dynamic parameters. The following results were achieved:

[0174] Improved prediction accuracy: By sensing road, traffic and environmental data (such as friction coefficient, slope, road type, etc.), the system can predict the future dynamic state of the vehicle (such as sideslip, skidding, loss of control, etc.); for example, on a wet and slippery road surface, the system can predict the risk of reduced tire adhesion, thereby adjusting the damping parameters of the suspension in advance to enhance the stability of the vehicle.

[0175] Optimize real-time adjustment of control parameters: Environmental perception data can be identified in advance, providing the control system with adjustment time and opportunities, allowing the control system to better optimize the distribution logic of driving torque, braking torque, suspension parameters, etc., which can improve vehicle stability and driving performance;

[0176] Achieving active safety: Through real-time analysis of environmental perception data, the system can proactively identify potential risks (such as sideslip, lock-up, rollover, etc.) and intervene through a three-way coordinated control strategy of lateral, longitudinal, and vertical directions to improve the active safety of the vehicle.

[0177] Enhanced driving comfort: Real-time adjustments to environmental perception data optimize suspension parameters and driving modes, improving vehicle ride comfort and driving experience.

[0178] This application also provides a control device for implementing the above embodiments and preferred embodiments, which will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0179] This application provides a control device, such as... Figure 3 As shown, it includes:

[0180] The acquisition module 301 is used to acquire related data that will affect the vehicle's driving safety when the vehicle reaches the target location at a future time, where the future time is the moment when the vehicle reaches the target location.

[0181] The prediction module 302 is used to determine the predictive control parameters of the vehicle in the lateral, longitudinal and vertical directions based on the associated data, where the lateral direction refers to the steering direction of the vehicle, the longitudinal direction refers to the braking or driving direction of the vehicle, and the vertical direction refers to the vertical movement direction of the vehicle's suspension.

[0182] The operation module 303 is used to control the vehicle to run according to the predicted control parameters when the vehicle travels to the target location, and to determine the initial dynamic parameters of the vehicle when running according to the predicted control parameters.

[0183] The correction module 304 is used to correct the initial dynamic parameters based on the actual dynamic parameters of the vehicle and determine the target control parameters. The actual dynamic parameters are the parameters calculated by using the dynamic model based on the vehicle's state data, and the target control parameters indicate the control parameters of the vehicle in a stable state.

[0184] Working module 305 is used to control the vehicle to operate with target control parameters.

[0185] In one possible implementation, the prediction module 302 includes:

[0186] The first lookup unit is used to look up the first control parameter of the vehicle in the lateral direction in the first lookup table of the associated data and the lateral control parameters. The first lookup table indicates that multiple associated data correspond one-to-one with multiple first control parameters. The first control parameters include steering wheel angle and vehicle speed.

[0187] The second lookup unit is used to look up the second control parameters of the vehicle in the longitudinal direction in the second lookup table of the associated data and the longitudinal control parameters. The second lookup table indicates that multiple associated data correspond one-to-one with multiple second control parameters, including driving force and braking force.

[0188] The third lookup unit is used to look up the third control parameters of the vehicle in the vertical direction in the third lookup table of the associated data and the vertical control parameters. The third lookup table indicates that multiple associated data correspond one-to-one with multiple third control parameters, including suspension stiffness and damping.

[0189] The predictive control parameter determination unit is used to use the first control parameter, the second control parameter, and the third control parameter as predictive control parameters, wherein the priority of the predictive control parameters used from high to low is: the first control parameter, the second control parameter, and the third control parameter.

[0190] In one possible implementation, the operation module 303 includes:

[0191] The first determining unit is used to determine the yaw rate of the vehicle based on the steering wheel angle, vehicle speed, vehicle wheelbase and understeer.

[0192] The second determining unit is used to determine the lateral acceleration of the vehicle based on the vehicle speed;

[0193] The third determining unit is used to determine the vehicle's roll angle based on the vehicle's track width and center of gravity height.

[0194] The fourth determining unit is used to determine the wheel slip ratio of the vehicle based on the vehicle speed, the wheel angular velocity of the vehicle, and the effective rolling radius of the vehicle's wheels.

[0195] The initial dynamic parameter determination unit is used to determine the yaw rate, lateral acceleration, roll angle, and wheel slip ratio as initial dynamic parameters.

[0196] In one possible implementation, the correction module 304 includes:

[0197] The deviation determination unit is used to determine the deviation between the actual dynamic parameters and the initial dynamic parameters;

[0198] The target control parameter determination unit is used to determine the target control parameter based on the predicted control parameter if the deviation is within the set deviation range; otherwise, it determines the target control parameter based on the actual dynamic parameters.

[0199] In one possible implementation, the target control parameter determination unit includes:

[0200] The lookup sub-unit is used to find the target control parameter corresponding to the actual dynamic parameter from the mapping table of dynamic parameters and control parameters. The mapping table of dynamic parameters and control parameters indicates that multiple dynamic parameters correspond one-to-one with multiple control parameters.

[0201] In one possible implementation, the working module 305 includes:

[0202] The triggering unit is used to trigger the vehicle's electronic stability system if the roll angle error corresponding to the roll angle in the deviation is greater than a first threshold, or the yaw rate error corresponding to the yaw rate in the deviation is greater than a second threshold.

[0203] The stability control unit controls the vehicle's chassis domain controller to apply braking force to at least one wheel of the vehicle and / or limit the output of the vehicle's power domain until the roll angle error is less than or equal to a first threshold and the yaw rate error is less than or equal to a second threshold.

[0204] 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.

[0205] The control device in the embodiments of this application is presented in the form of a functional unit. Here, a unit refers to an application-specific integrated circuit (ASIC), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0206] This application also provides a computer device having the above-described features. Figure 3 The control device shown.

[0207] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of this application, such as... Figure 4As 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 4 Take a processor 10 as an example.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0213] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0214] 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.

[0215] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0216] 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 all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A control method, characterized in that, The method includes: Obtain relevant data that will affect vehicle driving safety when the vehicle reaches the target location at a future time; Based on the associated data, predictive control parameters of the vehicle in the lateral, longitudinal, and vertical directions are determined, wherein the lateral direction refers to the steering direction of the vehicle, the longitudinal direction refers to the braking or driving direction of the vehicle, and the vertical direction refers to the vertical movement direction of the vehicle's suspension. When the vehicle reaches the target location, the vehicle is controlled to run according to the predicted control parameters, and the initial dynamic parameters of the vehicle running according to the predicted control parameters are determined. The initial dynamic parameters are corrected based on the actual dynamic parameters of the vehicle to determine the target control parameters. The actual dynamic parameters are parameters calculated using a dynamic model based on the vehicle's state data. The target control parameters indicate the control parameters of the vehicle in a stable state. Control the vehicle to operate with the target control parameters.

2. The method according to claim 1, characterized in that, The step of determining the predictive control parameters of the vehicle in the lateral, longitudinal, and vertical directions based on the associated data includes: Based on the associated data, the first control parameter of the vehicle in the lateral direction is searched in the first lookup table of associated data and lateral control parameters. The first lookup table indicates that multiple associated data correspond one-to-one with multiple first control parameters. The first control parameters include steering wheel angle and vehicle speed. Based on the associated data, the second control parameter of the vehicle in the longitudinal direction is searched in the second lookup table of associated data and longitudinal control parameters. The second lookup table indicates that multiple associated data correspond one-to-one with multiple second control parameters. The second control parameter includes driving force and braking force. Based on the associated data, the third control parameter of the vehicle in the vertical direction is searched in the third lookup table of the associated data and the vertical control parameter. The third lookup table indicates that multiple associated data correspond one-to-one with multiple third control parameters. The third control parameter includes suspension stiffness and damping. The first control parameter, the second control parameter, and the third control parameter are used as the predictive control parameters, wherein the priority of the predictive control parameters used from high to low is: the first control parameter, the second control parameter, and the third control parameter.

3. The method according to claim 2, characterized in that, Determining the initial dynamic parameters of the vehicle when it operates according to the predictive control parameters includes: The yaw rate of the vehicle is determined based on the steering wheel angle, the vehicle speed, the vehicle wheelbase, and the vehicle understeer. Based on the vehicle speed, determine the lateral acceleration of the vehicle; The roll angle of the vehicle is determined based on the vehicle's track width and center of gravity height. The wheel slip ratio of the vehicle is determined based on the vehicle speed, the wheel angular velocity of the vehicle, and the effective rolling radius of the vehicle's wheels. The yaw rate, the lateral acceleration, the roll angle, and the wheel slip ratio are used as the initial dynamic parameters.

4. The method according to claim 3, characterized in that, The step of correcting the initial dynamic parameters based on the actual dynamic parameters of the vehicle to determine the target control parameters includes: Determine the deviation between the actual dynamic parameters and the initial dynamic parameters; If the deviation is within the set deviation range, the predicted control parameter is used as the target control parameter; otherwise, the target control parameter is determined based on the actual dynamic parameters.

5. The method according to claim 4, characterized in that, Determining the target control parameters based on the actual dynamic parameters includes: The target control parameter corresponding to the actual dynamic parameter is found from the mapping table of dynamic parameters and control parameters. The mapping table of dynamic parameters and control parameters indicates that multiple dynamic parameters correspond one-to-one with multiple control parameters.

6. The method according to claim 4, characterized in that, Controlling the vehicle to operate with the target control parameters includes: If the roll angle error corresponding to the roll angle in the deviation is greater than a first threshold, or the yaw rate error corresponding to the yaw rate in the deviation is greater than a second threshold, then the vehicle's electronic stability system is triggered. The chassis domain controller of the vehicle applies braking force to at least one wheel of the vehicle and / or limits the output of the vehicle's power domain until the roll angle error is less than or equal to the first threshold and the yaw rate error is less than or equal to the second threshold.

7. A control device, characterized in that, The device includes: The acquisition module is used to acquire related data that will affect the vehicle's driving safety when the vehicle reaches the target location at a future time, wherein the future time is the time when the vehicle reaches the target location; The prediction module is used to determine the predictive control parameters of the vehicle in the lateral, longitudinal and vertical directions based on the associated data, wherein the lateral direction refers to the steering direction of the vehicle, the longitudinal direction refers to the braking or driving direction of the vehicle, and the vertical direction refers to the vertical movement direction of the vehicle's suspension. The operation module is used to control the vehicle to run according to the predicted control parameters when the vehicle travels to the target location, and to determine the initial dynamic parameters of the vehicle when running according to the predicted control parameters; The correction module is used to correct the initial dynamic parameters based on the actual dynamic parameters of the vehicle and determine the target control parameters. The actual dynamic parameters are parameters calculated using a dynamic model based on the vehicle's state data. The target control parameters indicate the control parameters of the vehicle in a stable state. The working module is used to control the vehicle to operate with the target control parameters.

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 control method of 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 control method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the control method according to any one of claims 1 to 6.

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